381 results
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2. Enhancing Retail Operations: Integrating Artificial Intelligence into the Theory of Constraints Thinking Process to Solve Shelf Issue.
- Author
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Aljaž, Tomaž
- Subjects
CONSTRAINT satisfaction ,CHATGPT ,ARTIFICIAL intelligence ,INVENTORY costs ,CUSTOMER satisfaction ,IDENTIFICATION - Abstract
Copyright of Electrotechnical Review / Elektrotehniski Vestnik is the property of Electrotechnical Society of Slovenia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2024
3. INTEGRATING VIRTUAL REALITY WITH INTERNET OF THINGS: ARCHITECTURES, APPLICATIONS AND CHALLENGES.
- Author
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ESWARAN, USHAA, ESWARAN, VISHAL, and ESWARAN, VIVEK
- Subjects
VIRTUAL reality ,INTERNET of things ,ARCHITECTURE ,SIMULATION methods & models ,CONSTRAINT satisfaction - Abstract
The integration of Virtual Reality (VR) environments with Internet of Things (IoT) infrastructure can enable more intuitive and immersive interactions. However, realizing the potential of this convergence requires overcoming technical constraints and implementation challenges. This paper reviews the motivations, architectures, applications, and open issues associated with combining VR and IoT. This study also aims to provide a comprehensive analysis of architectures, use cases and technical challenges involved in integrating VR and IoT to guide further research and real-world deployment. Use cases in manufacturing, energy, retail, entertainment and other sectors highlight the benefits like remote monitoring, training and collaboration unlocked by interfacing VR's realistic 3D visualizations with real-time IoT sensor data. This paper also discusses VR simulation of IoT systems for testing, limitations in interoperability, and security considerations. The outlook for VR-IoT solutions is promising, with 5G and edge computing advancements expected to address current bottlenecks to adoption. Human-centric design approaches focused on high-value use cases can enable impactful deployments across domains. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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- View/download PDF
4. Perceptual Modes of Presentation as Object Files.
- Author
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Siegel, Gabriel
- Subjects
CONSTRAINT satisfaction - Abstract
Some have defended a Fregean view of perceptual content. On this view, the constituents of perceptual contents are Fregean modes of presentation (MOPs). In this paper, I propose that perceptual MOPs are best understood in terms of object files. Object files are episodic representations that store perceptual information about objects. This information is updated when sensory conditions change. On the proposed view, when a subject perceptually represents some object a under two distinct MOPs, then the subject initiates two object files that both refer to a. My defense of this view appeals to its satisfaction of four constraints that I argue theories of perceptual MOPs should satisfy. Furthermore, I show that some existent accounts of perceptual MOPs fail to satisfy them. The defended constraints also indicate what is unique about perceptual, as opposed to linguistic or cognitive, MOPs. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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5. High‐level decision‐making for autonomous overtaking: An MPC‐based switching control approach.
- Author
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Wang, Xue‐Fang, Chen, Wen‐Hua, Jiang, Jingjing, and Yan, Yunda
- Subjects
OVERTAKING ,DECISION making ,CONSTRAINED optimization ,CONSTRAINT satisfaction ,AUTONOMOUS vehicles ,PREDICTION models - Abstract
The key motivation of this paper lies in the development of a high‐level decision‐making framework for autonomous overtaking maneuvers on two‐lane country roads with dynamic oncoming traffic. To generate an optimal and safe decision sequence for such scenario, an innovative high‐level decision‐making framework that combines model predictive control (MPC) and switching control methodologies is introduced. Specifically, the autonomous vehicle is abstracted and modelled as a switched system. This abstraction allows vehicle to operate in different modes corresponding to different high‐level decisions. It establishes a crucial connection between high‐level decision‐making and low‐level behaviour of the autonomous vehicle. Furthermore, barrier functions and predictive models that account for the relationship between the autonomous vehicle and oncoming traffic are incorporated. This technique enables us to guarantee the satisfaction of constraints, while also assessing performance within a prediction horizon. By repeatedly solving the online constrained optimization problems, we not only generate an optimal decision sequence for overtaking safely and efficiently but also enhance the adaptability and robustness. This adaptability allows the system to respond effectively to potential changes and unexpected events. Finally, the performance of the proposed MPC framework is demonstrated via simulations of four driving scenarios, which shows that it can handle multiple behaviours. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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6. Spacecraft Close Proximity to Noncooperative Target Based on Pseudospectral Convex Method.
- Author
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Wang, Qian, Li, Shunli, Zhang, Yanquan, and Cheng, Min
- Subjects
SPACE vehicles ,ROTATIONAL motion ,TRANSLATIONAL motion ,NONLINEAR programming ,CONSTRAINT satisfaction ,PROPORTIONAL navigation ,ARTIFICIAL satellite attitude control systems - Abstract
This paper proposes a trajectory-optimization problem for spacecraft close proximity to a noncooperative target, aiming at the generation of a six-degree-of-freedom (DOF) trajectory with the fuel-optimal objective value and considering multiple constraints on the control magnitude, line-of-sight, and glide-slope. The line-of-sight and glide-slope constraints are coupled between translational and rotational motions. The dual quaternion is an effective method for establishing the translationally and rotationally coupled model, because it can represent the translation and rotation in an integrated manner. Therefore, in this study, the trajectory-optimization problem of spacecraft close proximity coupled with position and attitude is established using dual quaternions. Next, the close-proximity trajectory-optimization problem is converted into a nonlinear programming problem, which can be solved efficiently using well-developed algorithms such as convex optimization. However, the zero-order hold used in the discrete method of convex optimization is an equidistant dispersion, which cannot guarantee the satisfaction of constraints between discrete points. Therefore the pseudospectral convex method is proposed using nonequidistant collocation points to mitigate the problem of constraint violation between discrete points and improve the accuracy and computational efficiency of the algorithm. The proposed algorithm can be applied to tasks such as rendezvous and docking with noncooperative targets and close proximity. Finally, the effectiveness of the proposed method was validated via numerical simulation, and the results were compared with those of the existing approach, GPOPS. The results indicate that the proposed algorithm is superior to GPOPS in computational efficiency and objective values. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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7. A Hybrid Genetic Algorithm for Ground Station Scheduling Problems.
- Author
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Xu, Longzeng, Yu, Changhong, Wu, Bin, and Gao, Ming
- Subjects
EARTH stations ,TABU search algorithm ,CONSTRAINT satisfaction ,DATA transmission systems ,SCHEDULING ,GENETIC algorithms - Abstract
In recent years, the substantial growth in satellite data transmission tasks and volume, coupled with the limited availability of ground station hardware resources, has exacerbated conflicts among missions and rendered traditional scheduling algorithms inadequate. To address this challenge, this paper introduces an improved tabu genetic hybrid algorithm (ITGA) integrated with heuristic rules for the first time. Firstly, a constraint satisfaction model for satellite data transmission tasks is established, considering multiple factors such as task execution windows, satellite–ground visibility, and ground station capabilities. Leveraging heuristic rules, an initial population of high-fitness chromosomes is selected for iterative refinement. Secondly, the proposed hybrid algorithm iteratively evolves this population towards optimal solutions. Finally, the scheduling plan with the highest fitness value is selected as the best strategy. Comparative simulation experimental results demonstrate that, across four distinct scenarios, our algorithm achieves improvements in the average task success rate ranging from 1.5% to 19.8% compared to alternative methods. Moreover, it reduces the average algorithm execution time by 0.5 s to 28.46 s and enhances algorithm stability by 0.8% to 27.7%. This research contributes a novel approach to the efficient scheduling of satellite data transmission tasks. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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8. A novel tri-stage with reward-switching mechanism for constrained multiobjective optimization problems.
- Author
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Qu, Jiqing, Li, Xuefeng, and Xiao, Hui
- Subjects
CONSTRAINED optimization ,EVOLUTIONARY algorithms ,CONSTRAINT satisfaction ,RELAXATION techniques ,SOURCE code - Abstract
The effective exploitation of infeasible solutions plays a crucial role in addressing constrained multiobjective optimization problems (CMOPs). However, existing constrained multiobjective optimization evolutionary algorithms (CMOEAs) encounter challenges in effectively balancing objective optimization and constraint satisfaction, particularly when tackling problems with complex infeasible regions. Subsequent to the prior exploration, this paper proposes a novel tri-stage with reward-switching mechanism framework (TSRSM), including the push, pull, and repush stages. Each stage consists of two coevolutionary populations, namely Pop 1 and Pop 2 . Throughout the three stages, Pop 1 is tasked with converging to the constrained Pareto front (CPF). However, Pop 2 is assigned with distinct tasks: (i) converging to the unconstrained Pareto front (UPF) in the push stage; (ii) utilizing constraint relaxation technique to discover the CPF in the pull stage; and (iii) revisiting the search for the UPF through knowledge transfer in the repush stage. Additionally, a novel reward-switching mechanism (RSM) is employed to transition between different stages, considering the extent of changes in the convergence and diversity of populations. Finally, the experimental results on three benchmark test sets and 30 real-world CMOPs demonstrate that TSRSM achieves competitive performance when compared with nine state-of-the-art CMOEAs. The source code is available at https://github.com/Qu-jq/TSRSM. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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9. Observer-based hybrid control for global attitude tracking on SO(3) with input quantisation.
- Author
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Hashemi, Seyed Hamed, Pariz, Naser, and Hosseini Sani, Seyed Kamal
- Subjects
LINEAR matrix inequalities ,POTENTIAL functions ,BODY image ,ANGULAR velocity ,CONSTRAINT satisfaction - Abstract
This paper studies the global attitude stabilization of a rigid body, a task that is subjected to topological obstacles. As a result of these obstructions, continuous feedbacks cannot globally stabilise the rigid body attitude. Therefore, this paper presents an observer-based hybrid controller to overcome these restrictions. Consequently, a new kind of synergistic potential function is designed which induces a gradient vector field to globally stabilise a given set. Moreover, the gradient of the proposed potential functions is utilised to derive a hybrid observer. Furthermore, this paper considers two types of constraints: angular velocity constraint and torque constraint. Afterward, these constraints are formulated in terms of the Linear Matrix Inequalities (LMI) optimisation problem to perform constraints satisfaction. Besides, this paper introduces a novel hybrid quantiser to deal with the problem of the low-price wireless network. Finally, a comparative study in simulations is provided to assess the performance of the proposed scheme. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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10. Cost-Effective and Low Power IoT-Based Paper Supply Monitoring System: An Application Modeling Approach.
- Author
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Senadeera, S. D. Arunya P., Kyi, Su, Sirisung, Thanapol, Pongsupan, Watsamon, Taparugssanagorn, Attaphongse, Dailey, Matthew N., and Wai, Tun Aung
- Subjects
BATTERY storage plants ,POWER resources ,CONSTRAINT satisfaction ,INTERNET of things ,FAMILY-owned business enterprises ,WIRELESS Internet - Abstract
IoT designers face the dual complexity of obtaining good application-level performance and user satisfaction under constraints on computing and power resources. We introduce a new IoT device for paper roll supply management in bathrooms and kitchens, both for homes and businesses, that is extremely cost effective and battery power-efficient. The device can be installed on practically any paper roll dispenser and makes use of existing Wi-Fi infrastructure. Despite Wi-Fi's reputation as "unsupportive for power saving," we introduce and experimentally validate a methodology for using Wi-Fi networks with low power utilization, resulting in a system that provides very good management of paper supplies while only requiring battery charging once every 3–4 months. The new device has the potential to provide more households and businesses with real-time, data-driven automated supply chains. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
11. A HEURISTIC-ACTION-INVOLVED SAFE LANE-CHANGE OF AUTONOMOUS VEHICLES WITH MULTIPLE CONSTRAINTS ON ROAD CENTERLINE AND SPEED UNDER HIGHWAY ENVIRONMENT.
- Author
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Jun CHEN, Fazhan TAO, Zhumu FU, Haochen SUN, and Nan WANG
- Subjects
HEURISTIC ,AUTONOMOUS vehicles ,DEEP reinforcement learning ,SIMULATION methods & models ,CONSTRAINT satisfaction - Abstract
Lane-change (LC) is one of the most important topics in autonomous vehicles (AVs) on highways. To enhance the implementation of effective LC in AVs, this paper proposes a framework based on deep reinforcement learning, which takes into account heuristic actions and multiple constraints related to the centerline of the road and speed, to improve the overall performance of LC in AVs. Firstly, the influence of unreasonable vehicle actions on the algorithm training process is studied. To improve the rationality of the to-be-trained actions, a novel reasonable action screening mechanism is proposed. Secondly, to keep the vehicle on the centerline of the lane and avoid the collision with other vehicles, a method is designed to calculate the center position of the vehicle. Thirdly, a segmented speed reward mechanism is proposed to constrain vehicle speed. Subsequently, a dynamic reward function is established to train the control algorithm. Lastly, the proposed strategy is evaluated in two simulation scenarios of highways. The simulation results show that the proposed method can increase the number of reasonable actions by more than 30% and improve the success rate of obstacle avoidance with the increase of over 52% in both static and dynamic scenarios compared with the benchmark algorithms. [ABSTRACT FROM AUTHOR]
- Published
- 2023
12. Instant distributed MPC with reference governor.
- Author
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Figura, Martin, Su, Lanlan, Inoue, Masaki, and Gupta, Vijay
- Subjects
CLOSED loop systems ,GOVERNORS ,ADAPTIVE control systems ,PREDICTION models ,CONSTRAINT satisfaction ,SYSTEM dynamics - Abstract
Model predictive control is a popular choice for systems that must satisfy prescribed constraints on states and control inputs. Although much progress has been made in distributed model predictive control, existing algorithms tend to be computationally expensive. This limits their use in systems with fast dynamics. In this paper, we propose a new distributed model predictive control algorithm that we term as instant distributed model predictive control (iDMPC). The proposed algorithm employs a realisation of the primal-dual algorithm in the controller dynamics for fast computation. We show that the closed-loop system trajectories with the proposed iDMPC algorithm converge asymptotically to a desired reference. To ensure the satisfaction of the state constraints during the transient, we also include an explicit reference governor in the feedback loop. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
13. Distributed switched model-based predictive control for distributed large-scale systems with switched topology.
- Author
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Alinia Ahandani, Morteza, Kharrati, Hamed, Hashemzadeh, Farzad, and Baradarannia, Mahdi
- Subjects
CLOSED loop systems ,TOPOLOGY ,INVARIANT sets ,CONSTRAINT satisfaction ,TIME-varying networks ,ELECTRIC network topology ,TIME-frequency analysis ,ADAPTIVE control systems ,ADAPTIVE fuzzy control - Abstract
Distributed switched large-scale systems are composed by dynamically coupled subsystems, in which interactions among subsystems vary over time according a switching signal. This paper presents a distributed robust switched model-based predictive control (DSwMPC) to control such systems. The proposed method guarantees stabilising the origin of the whole closed-loop system and ensures the constraints satisfaction in the presence of an unknown switching signal. In the distributed model-based predictive control (DMPC) used in this work, by considering the interactions among subsystems as an additive disturbance, the effect of the switch is reflected on the dynamic equation, local, and consistency constraint sets of the nominal subsystems. To compensate the effect of switching signal which creates a time-varying network topology, a robust tube-based switched model-based predictive control (RSwMPC) with switch–robust control invariant set as the target set robust to unknown mode switching is used as local controller. The scheme performance is assessed using three typical examples. The simulation results show that the input and state constraints are satisfied by the proposed DSwMPC at all times. They also validate that the closed-loop system converges to the origin. Also, a comparison of the DSwMPC with a centralised SwMPC (CSwMPC) and a decentralised SwMPC (DeSwMPC) are performed. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
14. Output feedback stochastic MPC for tracking control of quadrotors with disturbances.
- Author
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Xue, Ruochen, Dai, Li, Wang, Peizhan, Sun, Zhongqi, and Xia, Yuanqing
- Subjects
CONSTRAINT satisfaction ,ROBUST control ,STOCHASTIC models ,MATHEMATICAL models ,PROBLEM solving - Abstract
In this paper, the trajectory tracking problem of controlling a constrained quadrotor with unmeasurable system states in an environment with stochastic wind‐gust disturbance is considered. The mathematical model of the quadrotor is divided into the translational system and the rotational system, while only the measurement output of the quadrotor can be accessed. A new output‐based control method is developed for solving this problem. In the translational control system, an output feedback stochastic model predictive control (MPC) algorithm is proposed to generate the optimal control sequence with less conservativeness, by taking into account the information on the distribution of the disturbances and the uncertainty resulting from the attitude tracking error. The closed‐loop probabilistic constraints satisfaction, the recursive feasibility and the stability of the algorithm are further proved. In the rotational system, the active disturbance rejection control (ADRC) method to estimate and compensate for external disturbances is leveraged and robust control for attitude tracking is accomplished. The convergence of the disturbance estimator and the stability proof are provided. Finally, the robustness and effectiveness of the proposed control strategy are verified by an illustrative example. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
15. AUTONOMOUS APPLICATIONS IN TRANSPORTATION.
- Author
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SAINES, MADDIE
- Subjects
GLOBAL Positioning System ,DRIVERLESS cars ,ORBIT determination ,MULTISENSOR data fusion ,ANTENNAS (Electronics) ,CONSTRAINT satisfaction - Abstract
The article provides information on four papers presented at the ION GNSS+ conference, focusing on autonomous applications in transportation. The papers address topics such as integrity monitoring of autonomous navigation, estimation and reference systems in automation, evaluating GNSS performance for autonomous vehicles, and solving the localization problem in autonomous driving.
- Published
- 2023
16. WHAT MAKES AI DIFFERENT? EXPLORING AFFORDANCES AND CONSTRAINTS - THE CASE OF AUDITING.
- Author
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Jiaqi Yang, Marrone, Mauricio, and Amrollahi, Alireza
- Subjects
AUDITING ,INFORMATION technology ,HUMAN-artificial intelligence interaction ,CONSTRAINT satisfaction ,GROUNDED theory - Abstract
This study aims to gain a comprehensive understanding of the differences between classic IT and AI artefacts. To achieve this objective, the study employs a grounded theory literature review approach and analyses 81 papers related to the application of classic IT and AI artefacts in the auditing industry. Drawing on the Technology Affordances and Constraints Theory, we examine the actions that can be potentially enabled or restricted by using classic IT and AI artefacts. This analysis allows us to conceptualise and compare the affordances and constraints associated with these two types of artefacts. The study addresses the need for more research on AI from both social and technical perspectives. Our findings may facilitate practitioners in improving their business processes and promoting effective collaboration between humans and AI. [ABSTRACT FROM AUTHOR]
- Published
- 2023
17. Performance-based active controller design for nonlinear structures using modified black hole optimization.
- Author
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Yaghoobi, Saber, Fadali, M Sami, and Pekcan, Gokhan
- Subjects
BLACK holes ,OPTIMIZATION algorithms ,PERFORMANCE-based design ,SEISMIC response ,CONSTRAINT satisfaction - Abstract
This paper presents a novel approach that facilitates the design of active controllers to mitigate seismically induced damage in structural systems. The proposed method is based on stochastic Modified Black Hole optimization algorithm. Two traditional controllers, namely Proportional-Integral-Derivative (PID) and Linear–Quadratic Gaussian (LQG) controllers were designed, and their performance was demonstrated on a benchmark 20-story steel-framed building. Evaluation criteria were defined to satisfy constraints on various response quantities, including drift, base shear, ductility, residual story drift, and control force. The constraint limits were defined in view of performance-based design requirements for the benchmark structure. The performance of the controllers was contrasted with that of traditional LQG, and significant reductions of all response quantities were achieved for design-level earthquakes. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
18. Design and analysis of event‐triggered predictive sliding mode control for discrete‐time constrained system.
- Author
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Meng, Huan, Zhang, Jinhui, and Li, Sihang
- Subjects
- *
SLIDING mode control , *DISCRETE-time systems , *CONSTRAINT satisfaction - Abstract
This paper presents a new approach for designing an event‐triggered predictive sliding mode control (SMC) for discrete‐time systems subject to constraints. The proposed controller is based on an existing reach‐law‐based SMC, and it formulates an optimization control problem (OCP) to generate predictive control actions that satisfy both state and input constraints. An event‐triggered strategy is introduced to reduce the frequency of OCP solving by taking into account the sliding mode controller and unmeasurable states between sampling instants. The paper also analyzes the recursive feasibility of the proposed controller, which ensures the feasibility of each OCP solution. Numerical simulations and comparison studies are performed to validate the theoretical results. The proposed approach offers an effective way to design event‐triggered predictive sliding mode controllers that reduce the computational burden of OCP solving while guaranteeing the satisfaction of system constraints. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
19. Two Improved Constraint-Solving Algorithms Based on lmaxRPC3 rm.
- Author
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Pan, Xirui, Cheng, Zhuyuan, and Zhang, Yonggang
- Subjects
CONSTRAINT satisfaction ,ARTIFICIAL intelligence ,ALGORITHMS ,DATA structures ,HEURISTIC - Abstract
The Constraint Satisfaction Problem (CSP) is a significant research area in artificial intelligence, and includes a large number of symmetric or asymmetric structures. A backtracking search combined with constraint propagation is considered to be the best CSP-solving algorithm, and the consistency algorithm is the main algorithm used in the process of constraint propagation, which is the key factor in constraint-solving efficiency. Max-restricted path consistency (maxRPC) is a well-known and efficient consistency algorithm, whereas the lmaxRPC3 r m algorithm is a classic lightweight algorithm for maxRPC. In this paper, we leverage the properties of symmetry to devise an improved pruning strategy aimed at efficiently diminishing the problem's search space, thus enhancing the overall solving efficiency. Firstly, we propose the maxRPC3 s i m algorithm, which abandons the two complex data structures used by lmaxRPC3 r m . We can render the algorithm to be more concise and competitive compared to the original algorithm while ensuring that it maintains the same average performance. Secondly, inspired by the RCP3 algorithm, we propose the maxRPC3 s i m R algorithm, which uses the idea of residual support to cut down the redundant operation of the lmaxRPC3 r m algorithm. Finally, combining the domain/weighted degree (dom/wdeg) heuristic with the activity-based search (ABS) heuristic, a new variable ordering heuristic, ADW, is proposed. Our heuristic prioritizes the selection of variables with symmetry for pruning, further enhancing the algorithm's pruning capabilities. Experiments were conducted on both random and structural problems separately. The results indicate that our two algorithms generally outperform other algorithms in terms of performance on both problem classes. Moreover, the new heuristic algorithm demonstrates enhanced robustness across different problem types when compared to various existing algorithms. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
20. Attitude stabilization of spacecraft simulator based on modified constrained feedback linearization model predictive control.
- Author
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Khodaverdian, Maria and Malekzadeh, Maryam
- Subjects
ARTIFICIAL satellite attitude control systems ,SPACE vehicles ,PREDICTION models ,ANGULAR velocity ,ANGULAR momentum (Mechanics) ,CONSTRAINT satisfaction - Abstract
This paper aims to discuss the approach of constrained modified feedback linearization model predictive control for the spacecraft simulator. By utilizing the high accuracy and constrained properties of model predictive control (MPC), an optimum MPC is designed for the spacecraft feedback linearized system. The composite controller has the ability to control both the attitude and angular velocity of the reaction wheels (i.e. steering the angular momentum to zero at the end of the maneuver). The simulation and experimental results demonstrate that the proposed hybrid controller has an insignificant calculative cost and facilitates the spacecraft to perform regulation maneuver with sufficient precision in the presence of external torques and actuator saturations. This paper aims to discuss the approach of constrained modified feedback linearization model predictive control (CMFLMPC) for the spacecraft simulator. The simulation and experimental results demonstrate that the proposed hybrid controller has an insignificant calculative cost and facilitates the spacecraft to perform the regulation maneuver with sufficient precision in the presence of external torques and actuator saturations. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
21. Method for solving constrained 0-1 quadratic programming problems based on pointer network and reinforcement learning.
- Author
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Gu, Shenshen and Zhuang, Yuxi
- Subjects
REINFORCEMENT learning ,MACHINE learning ,QUADRATIC programming ,OPTIMIZATION algorithms ,DEEP learning ,CONSTRAINT satisfaction ,INTEGER programming - Abstract
The constrained 0-1 quadratic programming problem (CBQP) is an important problem of integer programming, and many combinatorial optimization problems can be converted to CBQP problem. Because BQP is NP-hard problem, the solving time and accuracy of traditional optimization algorithm are very dependent on the size of the problem, and the local optimal solution obtained by the heuristic algorithm is unstable. Deep learning algorithm has great advantages in solving such problems. In this paper, for the CBQP problem with linear constraints, we creatively apply two algorithms and models to solve it: the graph pointer network model (GPN) trained by hierarchical reinforcement learning (HRL), and the multi-head attention-based pointer network model trained by Advantage Actor-Critic (A2C), which greatly improves the solving speed, accuracy and constraint satisfaction rate of CBQP problems of different scales. At the same time, the bidirectional mask mechanism is innovatively introduced into the network so that the constraint satisfaction rate of the solution is very high. For the two algorithms, this paper solved the 0-1 knapsack (BKP) problem and the quadratic knapsack (QKP) problem, which are equivalent to the CBQP problem, and compared the results of the CBQP problem with different data distribution and scales. The experiment shows that no matter the objective function of the CBQP problem is linear or nonlinear, different data set distribution, or the scale, the pointer network trained by reinforcement learning in this paper has better results than traditional optimization algorithms in solving time, accuracy, stability and constraint satisfaction rate, and with the increase in the size of the problem, this advantage becomes more obvious. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
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22. Federated-Learning-Based Energy-Efficient Load Balancing for UAV-Enabled MEC System in Vehicular Networks.
- Author
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Shin, Ayoung and Lim, Yujin
- Subjects
REINFORCEMENT learning ,CONSTRAINT satisfaction ,MOBILE computing ,EDGE computing ,COMPUTER engineering ,DRONE aircraft - Abstract
At present, with the intelligence that has been achieved in computer and communication technologies, vehicles can provide many convenient functions to users. However, it is difficult for a vehicle to deal with computationally intensive and latency-sensitive tasks occurring in the vehicle environment by itself. To this end, mobile edge computing (MEC) services have emerged. However, MEC servers (MECSs), which are fixed on the ground, cannot flexibly respond to temporal dynamics where tasks are temporarily increasing, such as commuting time. Therefore, research has examined the provision of edge services using additional unmanned aerial vehicles (UAV) with mobility. Since these UAVs have limited energy and computing power, it is more important to optimize energy efficiency through load balancing than it is for ground MEC servers (MECSs). Moreover, if only certain servers run out of energy, the service coverage of a MEC server (MECS) may be limited. Therefore, all UAV MEC servers (UAV MECSs) need to use energy evenly. Further, in a high-mobility vehicle environment, it is necessary to have effective task migration because the UAV MECS that provides services to the vehicle changes rapidly. Therefore, in this paper, a federated deep Q-network (DQN)-based task migration strategy that considers the load deviation and energy deviation among UAV MECSs is proposed. DQN is used to create a local model for migration optimization for each of the UAV MECSs, and federated learning creates a more effective global model based on the fact that it has common spatial features between adjacent regions. To evaluate the performance of the proposed strategy, the performance is analyzed in terms of delay constraint satisfaction, load deviation, and energy deviation. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
23. A new knowledge-guided multi-objective optimisation for the multi-AGV dispatching problem in dynamic production environments.
- Author
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Liu, Lei, Qu, Ting, Thürer, Matthias, Ma, Lin, Zhang, Zhongfei, and Yuan, Mingze
- Subjects
PARTICLE swarm optimization ,DISTRIBUTION (Probability theory) ,EVOLUTIONARY algorithms ,AUTOMATED guided vehicle systems ,SATISFACTION ,CONSTRAINT satisfaction - Abstract
The efficiency of material supply for workstations using Automatic Guided Vehicles (AGVs) is largely determined by the performance of the AGV dispatching scheme. This paper proposes a new solution approach for the AGV dispatching problem (AGVDP) for material replenishment in a general manufacturing workshop where workstations are in a matrix layout, and where uncertainty in replenishment time of workstations and stochastic unloading efficiencies of AGVs are dynamic contextual factors. We first extend the literature proposing a mixed integer optimisation model with a delivery satisfaction soft constraint of material orders and two objectives: transportation costs and delivery time deviation. We then develop a new knowledge-guided estimation of distribution algorithm with delivery satisfaction evaluation for solving the model. Our algorithm fuses three knowledge-guided strategies to enhance optimisation capabilities at its respective execution stages. Comprehensive numerical experiments with instances built from a real-world scenario validate the proposed model and algorithm. Results demonstrate that the new algorithm outperforms three popular multi-objective evolutionary algorithms, a discrete version of a recent multi-objective particle swarm optimisation, and a multi-objective estimation of distribution algorithm. Findings of this work provide major implications for workshop management and algorithm design. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
24. A cerebellar operant conditioning-inspired constraint satisfaction approach for product design concept generation.
- Author
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Li, Mingdong, Lou, Shanhe, Gao, Yicong, Zheng, Hao, Hu, Bingtao, and Tan, Jianrong
- Subjects
OPERANT conditioning ,CONSTRAINT satisfaction ,PRODUCT design ,CONCEPTUAL design ,NEW product development ,PROPOSITION (Logic) - Abstract
Conceptual design is a pivotal stage of new product development. The function-behaviour-structure framework is adopted in this stage to help designers search design space and generate conceptual solutions iteratively. Computer-aided methods developed within this framework will yield significant insight into facilitating the cognitive activities of designers. In order to solve the mapping process from behaviours to structures which is a typical constraint satisfaction problem, a cerebellar operant conditioning-inspired constraint satisfaction approach is proposed in this paper. The design constraints-driven operant conditioning and its regulation mechanism by the cerebellum are analysed for the first time. Proposition logic is applied to transfer the constraint satisfaction problem into a propositional satisfiability problem while an undirected graph is utilised to model design space. Inspired by the modularised cerebellar structure, a modularised constraint satisfaction neural network is constructed to determine the satisfiability of design problems. Conceptual solutions can be generated by clustering the embedding of nodes in this network. The proposed approach imitates the design constraint-driven operant conditioning to narrow down design space without assigning specific values to design components. It reduces design iterations and avoids combinatorial explosions during conceptual design. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
25. Switched Auto-Regressive Neural Control (S-ANC) for Energy Management of Hybrid Microgrids.
- Author
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Cavus, Muhammed, Ugurluoglu, Yusuf Furkan, Ayan, Huseyin, Allahham, Adib, Adhikari, Kabita, and Giaouris, Damian
- Subjects
ENERGY management ,RECURRENT neural networks ,MICROGRIDS ,CONSTRAINT satisfaction ,ENERGY consumption - Abstract
Switched model predictive control (S-MPC) and recurrent neural networks with long short-term memory (RNN-LSTM) are powerful control methods that have been extensively studied for the energy management of microgrids (MGs). These methods ease constraint satisfaction, computational demands, adaptability, and comprehensibility, but typically one method is chosen over the other. The S-MPC method dynamically selects optimal models and control strategies based on the system's operating mode and performance objectives. On the other hand, integration of auto-regressive (AR) control with these powerful control methods improves the prediction accuracy and the adaptability of the system conditions. This paper compares the two control approaches and proposes a novel algorithm called switched auto-regressive neural control (S-ANC) that combines their respective strengths. Using a control formulation equivalent to S-MPC and the same controller model for learning, the results indicate that pure RNN-LSTM cannot provide constraint satisfaction. The novel S-ANC algorithm can satisfy constraints and deliver comparable performance to MPC, while enabling continuous learning. The results indicate that S-MPC optimization increases power flows within the MG, resulting in efficient utilization of energy resources. By merging the AR and LSTM, the model's computational time decreased by nearly 47.2%. In addition, this study evaluated our predictive model's accuracy: (i) the R-squared error was 0.951, indicating a strong predictive ability, and (ii) mean absolute error (MAE) and mean square error (MSE) values of 0.571 indicate accurate predictions, with minimal deviations from the actual values. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
26. Reference tracking via output feedback for constrained uncertain linear systems.
- Author
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Silveira Júnior, J. I. S. and Dórea, C. E. T.
- Subjects
UNCERTAIN systems ,CONSTRAINT satisfaction ,LINEAR systems ,INVARIANT sets ,ROBUST control ,ADAPTIVE control systems - Abstract
In this paper we propose a robust output feedback control scheme for constant reference tracking in uncertain linear systems subject to state and control constraints. First, we establish conditions under which a polyhedral set is Output Feedback Controlled Invariant with respect to a linear system subject to polytopic uncertainties, i.e. a set guaranteeing robust constraint satisfaction via output feedback. Then, we build a dynamic output feedback controller for the uncertain model based on set-membership observers, allowing the reduction of the set of possible states consistent with the measurements. Finally, to reduce the tracking error, we propose a model update procedure that adjusts the nominal model used for tracking to the output measurements. Numerical experiments illustrate the ability of the proposed controller to achieve reference tracking with reduced offset for the uncertain systems under consideration. To the best of our knowledge, it is one of the first works that addresses constant reference tracking in uncertain linear systems under constraints with output feedback. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
27. Exploiting Functional Constraints in Automatic Dominance Breaking for Constraint Optimization.
- Author
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Lee, Jimmy H. M. and Zhong, Allen Z.
- Subjects
MATHEMATICAL optimization ,CONSTRAINT algorithms ,CONSTRAINT satisfaction ,PROBLEM solving ,ARTIFICIAL intelligence - Abstract
Dominance breaking is a powerful technique in improving the solving efficiency of Constraint Optimization Problems (COPs) by removing provably suboptimal solutions with additional constraints. While dominance breaking is effective in a range of practical problems, it is usually problem specific and requires human insights into problem structures to come up with correct dominance breaking constraints. Recently, a framework is proposed to generate nogood constraints automatically for dominance breaking, which formulates nogood generation as solving auxiliary Constraint Satisfaction Problems (CSPs). However, the framework uses a pattern matching approach to synthesize the auxiliary generation CSPs from the specific forms of objectives and constraints in target COPs, and is only applicable to a limited class of COPs. This paper proposes a novel rewriting system to derive constraints for the auxiliary generation CSPs automatically from COPs with nested function calls, significantly generalizing the original framework. In particular, the rewriting system exploits functional constraints flattened from nested functions in a high-level modeling language. To generate more effective dominance breaking nogoods and derive more relaxed constraints in generation CSPs, we further characterize how to extend the system with rewriting rules exploiting function properties, such as monotonicity, commutativity, and associativity, for specific functional constraints. Experimentation shows significant runtime speedup using the dominance breaking nogoods generated by our proposed method. Studying patterns of generated nogoods also demonstrates that our proposal can reveal dominance relations in the literature and discover new dominance relations on problems with ineffective or no known dominance breaking constraints. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
28. Near‐optimal control of a class of output‐constrained systems using recurrent neural network: A control‐barrier function approach.
- Author
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Rad‐Moghadam, Surena and Farrokhi, Mohammad
- Subjects
CLOSED loop systems ,RECURRENT neural networks ,CONSTRAINT satisfaction ,STABILITY theory ,LYAPUNOV stability ,NONLINEAR systems ,CONSTRAINED optimization - Abstract
This paper proposes a near‐optimal controller design for the constrained nonlinear affine systems based on a Recurrent Neural Network (RNN) and Extended State Observers (ESOs). For this purpose, after defining a finite‐horizon integral‐type performance index, the prediction over the horizon is performed using the Taylor expansion that converts the primary problem into a finite‐dimensional optimization. In comparison with other controllers of the similar structure, the proposed method is capable of dealing with output constraints by employing the Control Barrier Function (CBF). The class of the output and input constraints are of the box‐type. Moreover, whereas several safe control approaches are proposed regardless of the performance of the closed‐loop system, this paper aims at achieving a near‐optimal performance as far as the constraints permit. As a result, a constrained optimization problem is achieved, where the online solution is obtained using a rapidly convergent RNN. Stability and the ease of implementation are some of the advantages of this network making the algorithm more reliable. Moreover, integrated stability analysis of the closed‐loop system that includes the dynamic RNN reveals that the closed‐loop system is stable in the sense of the Lyapunov stability theory. The effectiveness of the proposed control method in terms of the tracking performance and constraint satisfaction is illustrated through a simulating example of two‐inverted pendulums system. The results indicated advantages of the proposed method as compared with the recently published methods in well‐known literature. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
29. Predefined-time stabilization of stochastic nonlinear systems with application to UAVs.
- Author
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Qiu, Lifang, Zhao, Junsheng, Sun, Zong-Yao, and Xie, Xiangpeng
- Subjects
- *
NONLINEAR systems , *BACKSTEPPING control method , *LYAPUNOV functions , *CONSTRAINT satisfaction , *ANGLES - Abstract
The paper presents a new Lyapunov-type predefined-time stabilization control algorithm for stochastic high-order nonlinear systems with asymmetric output constraints. In contrast to stochastic finite-time and fixed-time stabilization, the average value of the settling-time function for stochastic predefined-time stabilization control is independent of both the initial value and the control factors. To mitigate the significant uncertainties arising from the asymmetric output constraint, a tan-type barrier Lyapunov function is formulated. Furthermore, by harnessing the previously mentioned barrier Lyapunov function and integrating the power integrator technique, a controller design strategy is formulated based on the backstepping method. The rigorous analysis in this study proves that the designed controller ensures both the attainment of predefined-time convergence of the system states to the origin in probability and the satisfaction of the output constraint. Finally, an example of a roll angle subsystem for quadrotor UAVs and a numerical illustration are presented to corroborate the theoretical analysis. • A novel Lyapunov-type stochastic predefined-time stable control algorithm is presented in Theorem 1. • Controller design based on the backstepping method is formulated through the power integrator technique. • The tan-type barrier Lyapunov function extends the order range and absorbs the inherent properties of the nonlinear terms. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
30. Chance-constrained programs with convex underlying functions: a bilevel convex optimization perspective.
- Author
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Laguel, Yassine, Malick, Jérôme, and van Ackooij, Wim
- Subjects
BILEVEL programming ,CONVEX functions ,PYTHON programming language ,RANDOM variables ,CONSTRAINT satisfaction ,STOCHASTIC programming - Abstract
Chance constraints are a valuable tool for the design of safe decisions in uncertain environments; they are used to model satisfaction of a constraint with a target probability. However, because of possible non-convexity and non-smoothness, optimizing over a chance constrained set is challenging. In this paper, we consider chance constrained programs where the objective function and the constraints are convex with respect to the decision parameter. We establish an exact reformulation of such a problem as a bilevel problem with a convex lower-level. Then we leverage this bilevel formulation to propose a tractable penalty approach, in the setting of finitely supported random variables. The penalized objective is a difference-of-convex function that we minimize with a suitable bundle algorithm. We release an easy-to-use open-source python toolbox implementing the approach, with a special emphasis on fast computational subroutines. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
31. Uncertainty-resilient constrained rendezvous trajectory optimization via stochastic feedback control and unscented transformation.
- Author
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Yuan, Hao, Li, Dongxu, He, Guanwei, and Wang, Jie
- Subjects
- *
TRAJECTORY optimization , *STOCHASTIC control theory , *CONSTRAINT satisfaction , *MONTE Carlo method , *OPTIMIZATION algorithms , *PSYCHOLOGICAL feedback - Abstract
This paper proposes an innovative approach for constrained rendezvous trajectory optimization (CRTO) using stochastic optimal feedback control and unscented transform (UT) uncertainty quantification. The method overcomes limitations of traditional deterministic CRTO solutions that suffer from uncontrolled terminal state error growth and severely deteriorated path constraint satisfaction when initial state uncertainty, dynamics uncertainty, and navigation uncertainty are considered. The approach involves constructing a stochastic optimal feedback control (SOFC) problem with chance constraints and introducing linear feedback control to regulate both the mean and variance of terminal state errors. UT is employed to approximately quantify the state errors and their propagation, transforming the SOFC problem into an unconstrained deterministic optimization (UDO) problem. Differential dynamic programming (DDP) is then used to solve the UDO problem. The obtained stochastic optimal solution provides a robust rendezvous trajectory and a corresponding explicit closed-loop guidance law, improving terminal accuracy and path constraint satisfaction in the presence of various considered uncertainties. The influence of different term weights in the objective function on terminal accuracy and path constraint satisfaction is also studied. The effectiveness of the proposed approach is verified through Monte Carlo simulation, demonstrating the robustness of the closed-loop control strategy. The results highlight the potential of the method in enhancing terminal accuracy and path constraint satisfaction in uncertain rendezvous scenarios. • The uncertainty-resilient rendezvous trajectory optimization algorithm provides not only a nominal trajectory but an explicit optimal state-feedback control policy. • Rendezvous trajectory uncertainty caused by initial state errors, dynamics errors, and navigation errors is propagated using an unscented transformation method. • The proposed method considers common input constraints and path constraints in space rendezvous missions. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
32. Architectural design space exploration of complex engineered systems with management constraints and preferences.
- Author
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Huang, Yu, Wang, Guoxin, Wang, Ru, Wei, Zhuqin, Liu, Zhendong, and Yan, Yan
- Subjects
- *
SPACE (Architecture) , *ARCHITECTURAL design , *CONSTRAINT satisfaction , *EVOLUTIONARY algorithms , *LAUNCH vehicles (Astronautics) , *WEIGHING instruments , *ARCHITECTURAL designs - Abstract
During the architectural design phase, the decisions made have a crucial impact on developing complex systems. These decisions often limit the performance bounds and steer subsequent research directions. However, the multitude of architectural components and intricate interrelationships cause component combination explosion. Additionally, the diverse requirements of different stakeholders add complexity to determining the weight of indicators, posing challenges to complex system architecture generation and trade-off. This paper proposes a method for exploring the architectural design space in complex systems realisation, which aims to resolve issues related to the formal representation of architectures, selection within the architectural design space, and trade-offs. Firstly, the architectural design space is represented by formulating and mathematically describing a morphological matrix. Then, the construction of the Constraint Satisfaction Problem model aims to achieve a reduction in the scale of the design space. Moreover, through the evolutionary algorithm, the Pareto front is identified. Weight sensitivity analysis and the improved Technique for Order Preference by Similarity to the Ideal Solution method are combined to assist designers in finding a satisfactory architecture from the design space with many combinations. Finally, the proposed method is verified by the design of the launch vehicle's primary and secondary separation system. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
33. A generalized three-dimensional cooperative guidance law for various communication topologies with field-of-view constraint.
- Author
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Kang, Honglong, Wang, Pengyu, and Song, Shenmin
- Subjects
TELECOMMUNICATIONS laws & regulations ,COMMUNICATION in law ,CONSTRAINT satisfaction ,COMMUNICATION laws ,PROPORTIONAL navigation - Abstract
In this paper, a generalized three-dimensional (3D) cooperative guidance law with seeker's field-of-view (FOV) constraints is designed for multiple missiles to achieve salvo attack against a stationary target. The proposed generalized guidance law is composed of two parts: a proportional navigation guidance (PNG) component for target capture and a biased feedback component for simultaneous arrival. Two novel auxiliary functions are integrated into the biased feedback component to guarantee the satisfaction of FOV constraint. Furthermore, a time-varying lower bound that contains the impact time error is utilized to avoid the guidance command singularity. The proposed guidance law can be easily applied to various missile communication topologies, including centralized connected, distributed connected, and no-connected topologies. The convergence of impact time error, FOV constraint satisfaction, and command non-singularity of the proposed guidance law are theoretically analyzed and proved. Extensive numerical simulations with comparative studies are conducted to demonstrate the effectiveness and advantages of the proposed guidance law. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
34. T-norms driven loss functions for machine learning.
- Author
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Giannini, Francesco, Diligenti, Michelangelo, Maggini, Marco, Gori, Marco, and Marra, Giuseppe
- Subjects
MACHINE learning ,TRIANGULAR norms ,ARTIFICIAL intelligence ,FIRST-order logic ,CONSTRAINT satisfaction ,SUPERVISED learning - Abstract
Injecting prior knowledge into the learning process of a neural architecture is one of the main challenges currently faced by the artificial intelligence community, which also motivated the emergence of neural-symbolic models. One of the main advantages of these approaches is their capacity to learn competitive solutions with a significant reduction of the amount of supervised data. In this regard, a commonly adopted solution consists of representing the prior knowledge via first-order logic formulas, then relaxing the formulas into a set of differentiable constraints by using a t-norm fuzzy logic. This paper shows that this relaxation, together with the choice of the penalty terms enforcing the constraint satisfaction, can be unambiguously determined by the selection of a t-norm generator, providing numerical simplification properties and a tighter integration between the logic knowledge and the learning objective. When restricted to supervised learning, the presented theoretical framework provides a straight derivation of the popular cross-entropy loss, which has been shown to provide faster convergence and to reduce the vanishing gradient problem in very deep structures. However, the proposed learning formulation extends the advantages of the cross-entropy loss to the general knowledge that can be represented by neural-symbolic methods. In addition, the presented methodology allows the development of novel classes of loss functions, which are shown in the experimental results to lead to faster convergence rates than the approaches previously proposed in the literature. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
35. 约束可满足性中求解 RB 模型实例的算法综述.
- Author
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杨 易, 王晓峰, 莫淳惠, 庞立超, 杨 澜, and 赵星宇
- Subjects
- *
CONSTRAINT satisfaction , *PHASE transitions , *NP-complete problems , *CONSTRAINT algorithms , *ARTIFICIAL intelligence , *METAHEURISTIC algorithms - Abstract
Constraint satisfaction problem is one of the most basic NP-complete problems in artificial intelligence. Over the years, with the in-depth study of constraint satisfaction problem, scholars at home and abroad have proposed a variety of case models. RB model is an example of growth domain constraint satisfaction problem with precise phase transition, which is very challenging to solve. In order to find a new efficient algorithm and promote the research of RB model algorithm for constraint satisfiability problem, firstly, this paper analyzed the model development and solving techniques of constraint satisfaction problem. Secondly, it sorted out various examples of RB model solving algorithms, and divided the solving algorithm literature into backtracking heuristic, information propagation and meta-heuristic related improved algorithms. This paper compared and summarized the algorithm principles, improved strategies, convergence and accuracy. Finally, it gave the research trend and development direction of solving RB model example algorithm. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
36. A survey of intelligent optimization algorithms for solving satisfiability problems.
- Author
-
Yang, Lan, Wang, Xiaofeng, Ding, Hongsheng, Yang, Yi, Zhao, Xingyu, and Pang, Lichao
- Subjects
OPTIMIZATION algorithms ,SWARM intelligence ,EVOLUTIONARY algorithms ,PROBLEM solving ,HEURISTIC algorithms ,CONSTRAINT satisfaction - Abstract
Constraint satisfaction problems have a wide range of applications in areas such as basic computer theory research and artificial intelligence, and many major studies in industry are not solved directly, but converted into instances of satisfiability problems for solution. Therefore, the solution of the satisfiability problem is a central problem in many important areas in the future. A large number of solution algorithms for this problem are mainly based on completeness algorithms and heuristic algorithms. Intelligent optimization algorithms with heuristic policies run significantly more efficiently on large-scale instances compared to completeness algorithms. This paper compares the principles, implementation steps, and applications of several major intelligent optimization algorithms in satisfiability problems, analyzes the characteristics of these algorithms, and focuses on the performance in solving satisfiability problems under different constraints. In terms of algorithms, evolutionary algorithms and swarm intelligence algorithms are introduced; in terms of applications, the solution to the satisfiability problem is studied. At the same time, the performance of the listed intelligent optimization algorithms in applications is analyzed in detail in terms of the direction of improvement of the algorithms, advantages and disadvantages and comparison algorithms, respectively, and the future application of intelligent optimization algorithms in satisfiability problems is prospected. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
37. Multi-agent system-based fuzzy constraints offer negotiation of workflow scheduling in Fog-Cloud environment.
- Author
-
Marwa, Mokni, Hajlaoui, Jalel Eddine, Sonia, Yassa, Omri, Mohamed Nazih, and Rachid, Chelouah
- Subjects
PRODUCTION scheduling ,NEGOTIATION ,WORKFLOW management systems ,WORKFLOW ,TIME management ,CONSTRAINT satisfaction ,FUZZY sets ,ECOLOGY - Abstract
This paper presents the multi-agent system fuzzy-constraints offer negotiation of Workflow Scheduling in Fog-Cloud environment, called (Fuzzy-Cone) approach, to solve the workflow scheduling problem with conflicting constraints in Fog-Cloud IT infrastructures. A client agent and a supplier agent are created to represent the client and supplier sides respectively, with a win–win strategy based on negotiation. The novelty of this approach is the design of a multi-agent system with agents supervised by a strategy based on a fuzzy inference system modeling all possible cases, thus facilitating decision-making. The workflow scheduling problem is treated as a set of fuzzy constraint satisfaction problems (FCSP). Each agent has an FCSP modeling a set of fuzzy constraints based on the negotiation with other agents by proposing offers or counter-offers. The proposed negotiation approach is implemented to respect all the imposed restrictions and represent the imprecise preferences of the approach entities by pre-defining the fuzzy constraints and optimizing the workflow scheduling solution in terms of time and cost. of compiling. The proposed approach has been tested with different experiments and compared with state-of-the-art algorithms. The experimental results show that the negotiation between the solutions, of mutually satisfactory scheduling, considerably improved the values of time and cost of compilation, while respecting the set of the imposed constraints. The proposed approach achieves a workflow scheduling scheme that reduces compilation time by 37% and increases cost by 6% compared to state-of-the-art algorithms. The different solutions we have proposed respect the constraints of time and budget by executing workflows of different sizes in reasonable time and cost. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
38. Collision and obstacle avoidance for agents based on velocity programming in unknown environments under kinematical constraints.
- Author
-
Xiong, Zhigang, Luo, Yasong, Liu, Zhong, Zhang, Jianqiang, and Liu, Zhikun
- Subjects
VELOCITY ,CONSTRAINTS (Physics) ,CONVEX programming ,CONSTRAINT satisfaction ,LINEAR programming ,DYNAMIC programming - Abstract
This paper conducts research on obstacle and collision avoidance under kinematical constraints in unknown environments, while communication and simultaneous localization and mapping (SLAM) are unavailable to agents. Then a strategy based on mixed‐integer programming is proposed, in which velocity constraints are established with a modified Barrier function for obstacles that can be completely detected. As for obstacles that cannot be completely detected, a feasible set is built for the velocity programming based on the convex theory, and the contradicted constraints are addressed with the logic metric method. Besides, the actuator saturation is avoided by converting kinematical constraints into the restrictions on the magnitude, the restrictions on the direction, and the negative correlations between the components of the velocity respectively. Given that invalid nominal velocity leads to collisions, a virtual goal is estimated for nominal velocity improvement. In addition, the local extremum brought by empty programming space due to multiple constraints is repaired by fixing the velocity constraints. The feasibility of the proposed strategy is analyzed, and numerical simulations are provided to verify the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
39. Optimal Hybrid Precoding Based QoE for Partially Structured Massive MIMO System.
- Author
-
Samklang, Farung, Uthansakul, Peerapong, Uthansakul, Monthippa, and Anchuen, Patikorn
- Subjects
DIGITAL communications ,MIMO systems ,DIGITAL signal processing ,CONSTRAINT satisfaction ,WEB services ,TRAFFIC flow ,QUALITY of service ,RADIO frequency - Abstract
Precoding is a beamforming technique that supports multi-stream transmission in which the RF chain plays a significant role as a digital precoding at the receiver for wireless communication. The traditional precoding contains only digital signal processing and each antenna connects to each RF chain, which provides high transmission efficiency but high cost and hardware complexity. Hybrid precoding is one of the most popular massive multiple input multiple output (MIMO) techniques that can save costs and avoid using complex hardware. At present, network services are currently in focus with a wide range of traffic volumes. In terms of the Quality of Service (QoS), it is critical that service providers pay a lot of attention to this parameter and its relationship to Quality of Experience (QoE) which is the measurement of the overall level of user satisfaction. Therefore, this paper proposes hybrid precoding of a partially structured system to improve transmission efficiency and allocate resources to provide network services to users for increasing the user satisfaction under power constraints that optimize the quality of baseband precoding and radio frequency (RF) precoding by minimizing alternating algorithms. We focus on the web browsing, video, and Voice over IP (VOIP) services. Also, a Mean Opinion Score (MOS) is employed to measure the level of user satisfaction. The results show that the partially structured system provides a good user satisfaction with the network's services. The partially structured system provides high energy efficiency up to 85%. Considering web service, the partially structured system for 10 users provides MOS at 3.21 which is higher than 1.75 of fully structured system. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
40. The Complexity of Counting CSPd.
- Author
-
Lin, Jiabao
- Subjects
QUANTUM information theory ,CONSTRAINT satisfaction ,QUANTUM states ,COUNTING ,KOLMOGOROV complexity - Abstract
Counting CSP
d is the counting constraint satisfaction problem (# CSP in short) restricted to the instances where every variable occurs a multiple of d times. This paper revisits tractable structures in # CSP and gives a complexity classification theorem for # CSPd with algebraic complex weights. The result unifies affine functions (stabilizer states in quantum information theory) and related variants such as the local affine functions, the discovery of which leads to all the recent progress on the complexity of Holant problems. The Holant is a framework that generalizes counting CSP. In the literature on Holant problems, weighted constraints are often expressed as tensors (vectors) such that projections and linear transformations help analyze the structure. This paper gives an example showing that different classes of constraints distinguished by these algebraic operations may share the same closure property. [ABSTRACT FROM AUTHOR]- Published
- 2022
- Full Text
- View/download PDF
41. Risk and complexity in scenario optimization.
- Author
-
Garatti, S. and Campi, M. C.
- Subjects
CONSTRAINT satisfaction ,DISTRIBUTION (Probability theory) ,RISK assessment ,DECISION making - Abstract
Scenario optimization is a broad methodology to perform optimization based on empirical knowledge. One collects previous cases, called "scenarios", for the set-up in which optimization is being performed, and makes a decision that is optimal for the cases that have been collected. For convex optimization, a solid theory has been developed that provides guarantees of performance, and constraint satisfaction, of the scenario solution. In this paper, we open a new direction of investigation: the risk that a performance is not achieved, or that constraints are violated, is studied jointly with the complexity (as precisely defined in the paper) of the solution. It is shown that the joint probability distribution of risk and complexity is concentrated in such a way that the complexity carries fundamental information to tightly judge the risk. This result is obtained without requiring extra knowledge on the underlying optimization problem than that carried by the scenarios; in particular, no extra knowledge on the distribution by which scenarios are generated is assumed, so that the result is broadly applicable. This deep-seated result unveils a fundamental and general structure of data-driven optimization and suggests practical approaches for risk assessment. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
42. A GPU-Based Parallel Region Classification Method for Continuous Constraint Satisfaction Problems.
- Author
-
Guanglu Zhang, Wangchuan Feng, and Cagan, Jonathan
- Subjects
- *
CONSTRAINT satisfaction , *INTERVAL analysis , *CENTRAL processing units , *MULTIDISCIPLINARY design optimization , *CLASSIFICATION , *LAMINATED composite beams - Abstract
Continuous constraint satisfaction is prevalent in many science and engineering fields. When solving continuous constraint satisfaction problems, it is more advantageous for practitioners to derive all feasible regions (i.e., the solution space) rather than a limited number of solution points, since these feasible regions facilitate design concept generation and design tradeoff evaluation. Several central processing unit (CPU)-based branch-and-prune methods and geometric approximation methods have been proposed in prior research to derive feasible regions for continuous constraint satisfaction problems. However, these methods have not been extensively adopted in practice, mainly because of their high computational expense. To overcome the computational bottleneck of extant CPU-based methods, this paper introduces a GPU-based parallel region classification method to derive feasible regions for continuous constraint satisfaction problems in a reasonable computational time. Using interval arithmetic, coupled with the computational power of GPU, this method iteratively partitions the design space into many subregions and classifies these subregions as feasible, infeasible, and indeterminate regions. To visualize these classified regions in the design space, a planar visualization approach that projects all classified regions into one figure is also proposed. The GPU-based parallel region classification method and the planar visualization approach are validated through two case studies about the bird function and the welded beam design. These case studies show that the method and the approach can solve the continuous constraint satisfaction problems and visualize the results effectively and efficiently. A four-step procedure for implementing the method and the approach in practice is also outlined. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
43. The Parameterized Complexity of Guarding Almost Convex Polygons.
- Author
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Agrawal, Akanksha, Knudsen, Kristine V. K., Lokshtanov, Daniel, Saurabh, Saket, and Zehavi, Meirav
- Subjects
- *
COMPUTATIONAL geometry , *CONSTRAINT satisfaction , *POLYGONS , *COMMERCIAL art galleries - Abstract
The ArtGallery problem is a fundamental visibility problem in Computational Geometry. The input consists of a simple polygon P, (possibly infinite) sets G and C of points within P, and an integer k; the task is to decide if at most k guards can be placed on points in G so that every point in C is visible to at least one guard. In the classic formulation of ArtGallery, G and C consist of all the points within P. Other well-known variants restrict G and C to consist either of all the points on the boundary of P or of all the vertices of P. Recently, three new important discoveries were made: the above mentioned variants of ArtGallery are all W[1]-hard with respect to k [Bonnet and Miltzow in 24th Annual European Symposium on Algorithms (Aarhus 2016)], the classic variant has an O (log k) -approximation algorithm [Bonnet and Miltzow in 33rd International Symposium on Computational Geometry (Brisbane 2017)], and it may require irrational guards [Abrahamsen et al. in 33rd International Symposium on Computational Geometry (Brisbane 2017)]. Building upon the third result, the classic variant and the case where G consists only of all the points on the boundary of P were both shown to be ∃ R -complete [Abrahamsen et al. in 50th Annual ACM SIGACT Symposium on Theory of Computing (Los Angeles 2018)]. Even when both G and C consist only of all the points on the boundary of P, the problem is not known to be in NP. Given the first discovery, the following question was posed by Giannopoulos [Lorentz Workshop on Fixed-Parameter Computational Geometry (Leiden 2016)]: Is ArtGallery FPT with respect to r, the number of reflex vertices? In light of the developments above, we focus on the variant where G and C consist of all the vertices of P, called Vertex-Vertex Art Gallery. Apart from being a variant of ArtGallery, this case can also be viewed as the classic DominatingSet problem in the visibility graph of a polygon. In this article, we show that the answer to the question by Giannopoulos is positive: Vertex-VertexArtGallery is solvable in time r O (r 2) · n O (1) . Furthermore, our approach extends to assert that Vertex-BoundaryArtGallery and Boundary-VertexArtGallery are both FPT as well. To this end, we utilize structural properties of "almost convex polygons" to present a two-stage reduction from Vertex-VertexArtGallery to a new constraint satisfaction problem (whose solution is also provided in this paper) where constraints have arity 2 and involve monotone functions. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
44. Toward Characterizing Solutions to Complex Programming Problems Involving Fuzzy Parameters in Constraints.
- Author
-
El-Wahed Khalifa, Hamiden Abd, Pamučar, Dragan, and Edalatpanah, Seyed Ahmad
- Subjects
COMPUTER programming ,CONSTRAINT satisfaction ,FUZZY numbers ,SADDLEPOINT approximations ,FUZZY sets - Abstract
The current study investigates to characterize the Complex Programming Problem (CPP) solution in a fuzzy environment. The paper is divided into two parts: 1) the first presents a Fuzzy Complex Programming Problem (FCPP) with fuzzy complex constraints, and 2) the second presents the optimality criteria using the fuzzy complex cone. The CPP is suggested by involving fuzzy numbers in the constraints in parts. Using the α -cut set concepts, the problem is converted into the α -complex programming. A number of basic theorems with proofs are established concerning the basic results for the fuzzy complex set of solutions for the F-CPP, and the optimality criteria of the saddle point for F-CPP with fuzzy cones is derived. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
45. Ascent adaptive energy management method for solid rocket‐powered hypersonic vehicle under uncertainties.
- Author
-
Fu, Wenzhe, Liu, Lei, Wang, Bo, Cheng, Zhongtao, and Wang, Yongji
- Subjects
HYPERSONIC planes ,ENERGY management ,KALMAN filtering ,PARAMETER estimation ,ENERGY consumption ,CONSTRAINT satisfaction - Abstract
Summary: The solid rocket booster is widely used in the launch mission for hypersonic vehicles nowadays, whose ascent energy management is still a challenging problem due to the inherent dynamic nonlinearity. This paper casts the problem into an equivalent ascent guidance one and proposes a model predictive static programming (MPSP) based solution. The MPSP‐based method is a computationally efficient solver that adapts to different energy demands while guaranteeing the satisfaction of terminal constraints. To further improve the performance of MPSP in this problem, a Bézier spline guidance method is proposed to generate high‐quality reference trajectories such that the MPSP can converge with high accuracy while satisfying the real‐time requirement. Additionally, an online parameter estimation based on the extended Kalman filter is employed to avoid large terminal state deviations incurred by parametric uncertainties during the Bézier curve computation and linearization in MPSP. Numerical simulations with different hard terminal constraints and uncertainties are conducted to demonstrate the effectiveness and robustness of the proposed algorithm. The simulation results show that our proposed algorithm can guide the hypersonic vehicle to a given terminal state under uncertainties and outperforms several existing ascent energy management methods. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
46. Application of asymmetric barrier Lyapunov function using ADRC approach for development of autonomous vehicle lateral control.
- Author
-
Parkash, Aman and Swarup, Akhilesh
- Subjects
LYAPUNOV functions ,BACKSTEPPING control method ,CONSTRAINT satisfaction ,AUTONOMOUS vehicles ,DYNAMIC positioning systems - Abstract
This paper presents a control scheme for performance improvement in lane-keeping activity of autonomous vehicle, within guaranteed specified deviations. Such feature will provide better and safe maneuvering for autonomous vehicle. The proposed control scheme consists of Asymmetric Barrier Lyapunov Function (ABLF) based backstepping method for ensuring stability and constraints satisfaction. Since autonomous vehicle dynamics is subjected to unknown disturbances and also the states, it has been shown that the vehicle performance can be improved by the application of Active Disturbance Rejection Control (ADRC) which uses the Extended State Observer (ESO). For the proposed control scheme, the asymptotic stability and convergence of error dynamics have been established. The simulation results have been presented to illustrate the performance robustness and accuracy provided by the proposed control scheme. Further, it has been show that the vehicle motion performance is better in comparison to the performances obtained from two existing control methods. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
47. Vibration Control Using a Positive Position Feedback-based Predictive Controller Applied to a One-Bay Three-Story Scaled Shear Frame.
- Author
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Aguilar-Álvarez, P., Valencia-Palomo, G., Enríquez-Zárate, J., Morales-Valdez, J., and Hernández-González, O.
- Subjects
TUNED mass dampers ,CONSTRAINT satisfaction ,PSYCHOLOGICAL feedback - Abstract
This paper presents an Active Tuned Mass Damper (ATMD) control approach for vibration reduction applied to a one-bay three-story scaled shear frame. The ATMD is composed of a Tuned Mass Damper (TMD) driven by the combined action of a modified Positive Position Feedback (mPPF) control law and a Model Predictive Controller (MPC). The mPPF and the MPC are in an inner and outer loop configuration. In the inner control loop, the mPPF strategy compensates for the first vibration modes at low frequencies and pre-stabilizes the structure; while, in the outer loop, the MPC controller considers the actuator limits ensuring constraint satisfaction and softening the control action. A critical advantage concerning to mPPF controller is its design is based only on the natural frequencies, which are relatively easy to estimate around an experimental test. Furthermore, the MPC allows generalizing the controller design for multi-story arrangements without modifying the controller structure. Numerical results show that, for the case study, the control effort of the proposed scheme is more than seven times lower than the control effort when compared with an ATMD driven by a conventional MPC, without sacrificing performance. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
48. A tri-stage competitive swarm optimizer for constrained multi-objective optimization.
- Author
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Dong, Jun, Gong, Wenyin, and Ming, Fei
- Subjects
CONSTRAINT satisfaction ,EVOLUTIONARY algorithms ,CONSTRAINED optimization - Abstract
Objective optimization and constraint satisfaction should be considered simultaneously when dealing with constrained multi-objective optimization problems (CMOPs). But it is difficult for existing constraint multi-objective evolutionary algorithms (CMOEAs) to strike a good balance between them, especially for CMOPs with complex constraints. To address this issue, this paper proposes a tri-stage competitive swarm optimizer (CSO), namely TSCSO, where objective optimization and constraint satisfaction receive different attention in different stages. In Stage-I, the population converges to the vicinity of the unconstrained Pareto front (PF) without considering any constraints. In Stage-II, a balance strategy and ranking approach based on convergence, diversity, and feasibility are proposed to enhance the diversity of the population and explore more feasible regions. An external archive is used to store the feasible solutions explored during the evolutionary process. In Stage-III, the population is first initialized by the feasible solutions in the archive, and the CSO operator with efficient search is used to search for the feasible regions omitted in Stage-II. Statistical results on two benchmark suites with twenty-eight problems and five real-world problems indicate that the proposed algorithm performs better than other state-of-the-art CMOEAs overall. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
49. Learning‐based adaptive‐scenario‐tree model predictive control with improved probabilistic safety using robust Bayesian neural networks.
- Author
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Bao, Yajie, Chan, Kimberly J., Mesbah, Ali, and Velni, Javad Mohammadpour
- Subjects
BAYESIAN analysis ,PREDICTION models ,CONSTRAINT satisfaction ,ROBUST control ,LOW temperature plasmas - Abstract
Summary: Scenario‐based model predictive control (MPC) methods can mitigate the conservativeness inherent to open‐loop robust MPC. Yet, the scenarios are often generated offline based on worst‐case uncertainty descriptions obtained a priori, which can in turn limit the improvements in the robust control performance. To this end, this paper presents a learning‐based, adaptive‐scenario‐tree model predictive control approach for uncertain nonlinear systems with time‐varying and/or hard‐to‐model dynamics. Bayesian neural networks (BNNs) are used to learn a state‐ and input‐dependent description of model uncertainty, namely the mismatch between a nominal (physics‐based or data‐driven) model of a system and its actual dynamics. We first present a new approach for training robust BNNs (RBNNs) using probabilistic Lipschitz bounds to provide a less conservative uncertainty quantification. Then, we present an approach to evaluate the credible intervals of RBNN predictions and determine the number of samples required for estimating the credible intervals given a credible level. The performance of RBNNs is evaluated with respect to that of standard BNNs and Gaussian process (GP) as a basis of comparison. The RBNN description of plant‐model mismatch with verified accurate credible intervals is employed to generate adaptive scenarios online for scenario‐based MPC (sMPC). The proposed sMPC approach with adaptive scenario tree can improve the robust control performance with respect to sMPC with a fixed, worst‐case scenario tree and with respect to an adaptive‐scenario‐based MPC (asMPC) using GP regression on a cold atmospheric plasma system. Furthermore, closed‐loop simulation results illustrate that robust model uncertainty learning via RBNNs can enhance the probability of constraint satisfaction of asMPC. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
50. Adaptive control of nonlinear time-delay systems in the presence of output constraints and actuators faults.
- Author
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Mohammadzamani, Fatemeh, Hashemi, Mahnaz, and Shahgholian, Ghazanfar
- Subjects
ADAPTIVE control systems ,NONLINEAR systems ,ACTUATORS ,LYAPUNOV functions ,CONSTRAINT satisfaction - Abstract
This paper aims to design a controller to compensate the faults for a class of time-delay nonlinear systems. This controller is based on the back-stepping design approach. A barrier Lyapunov function has been employed to ensure the satisfaction of output constraints. This controller is proposed for nonlinear systems with parameter uncertainty, time-delay and uncertain faults in actuators while no bounds for parameters, delays and actuators faults are supposed. The boundedness of all the closed-loop signals is guaranteed and the tracking error is proved to converge to a small neighbourhood of the origin without violation of the constraint. In the end, the simulation results show the effectiveness of the proposed control method. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
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