Search

This document discusses the societal impact of applying artificial intelligence (AI) to traffic and transportation. Sustainable transport is considered crucial for sustainable cities and communities, and AI has the potential to significantly influence daily life in urban areas. The document highlights the interdisciplinary challenges of understanding and coordinating transportation systems, which involve various players with conflicting goals and constraints. It also presents a selection of innovative solutions related to traffic management, including machine learning, reinforcement learning, and optimization approaches. The document concludes by expressing gratitude to the authors, reviewers, and committee members involved in the creation of the special issue. [Extracted from the article]

With the rapid development of new-generation cyber–physical system (CPS) technologies, the smart operation and management of the broad bean harvesting system are enabled. This paper proposed a smart broad bean harvesting system (SBHS) and a self-adaptive control method based on CPS technologies. Firstly, the overall architecture of the SBHS is designed, which consists of four main components, namely optimal intelligent perception environment configuration, digital twin model construction, virtual simulation and real-time optimization, self-adaptive adjustment and control. Then, three key enabling technologies are discussed in detail, namely, intelligent perception environment configuration for the SBHS, digital twin model construction for the SBHS, colored Petri net (CPN)-based self-adaptive analysis and control of the harvesting system. Lastly, a proof-of-concept experiment based on a cooperative company is presented to illustrate the main work logic and advantage of the proposed SBHS. After the edge–cloud cooperative intelligent harvesting environment is configured, the CPN model for the workflow of the SBHS is created to analyze and optimize the harvesting processes. In addition, a management and control platform are developed to further illustrate the implementation of the proposed SBHS and the self-adaptive control method. [ABSTRACT FROM AUTHOR]

Nanoscale integration is difficult on rough surfaces despite their ubiquity and usefulness. Paper is an emerging material system because it is renewable, flexible, and lightweight. However, owing to the rough surface of paper, it is difficult to use conventional nanoscale integration methods. Herein, nanoscale printing of CdSe/ZnS quantum dots on paper using a 3D printing method with surface adaptive control of the femtoliter meniscus is demonstrated. The approach allows the direct integration of light‐emitting materials on the intact surface of paper with nanoscale lateral confinement. The 3D‐printed nanostructures can be treated as a planar pattern of the nanoscale dots when viewed from the top. The individually addressable nature of the 3D printing method enables the equalization of the printed heights, which is essential for practical use in general planar systems. This method can be used in many areas that require paper such as paper electronics, security printing, biomedical engineering, and possibly other material systems with rough surfaces. [ABSTRACT FROM AUTHOR]

Abstract: Advanced neuro-fuzzy modeling, namely an adaptive network-based fuzzy inference system (ANFIS), was employed to develop models for the prediction of suspended solids (SS) and chemical oxygen demand (COD) removal of a full-scale wastewater treatment plant treating process wastewaters from a paper mill. In order to improve the network performance, fuzzy subtractive clustering was used to identify model''s architecture and optimize fuzzy rule, meanwhile principal component analysis (PCA) was applied to reduce the input variable dimensionality. Input variables were reduced from six to four for COD and SS models, by considering PCA results and linear correlation matrices among input and output variables. The results indicate that reasonable forecasting and control performances have been achieved through the developed system. The minimum mean absolute percentage errors of 1.003% and 0.5161% for CODeff and SSeff could be achieved using ANFIS. The maximum correlation coefficient values for CODeff and SSeff were 0.9912 and 0.9882, respectively. The minimum mean square errors of 1.2883 and 0.0342, and the minimum RMSEs of 1.135 and 0.1849 for CODeff and SSeff could also be achieved. [Copyright &y& Elsevier]

Consensus tracking control problem of the multi-agent systems with both noise and disturbances is studied in this paper. A robust adaptive control scheme is designed according to Lyapunov stability theory. The main contribution of this paper is that the consensus is realized in multi-agent systems with both noise and external disturbances. Numerical simulations are carried out for the proposed scheme to demonstrate the effectiveness of the control strategy proposed in this paper. [ABSTRACT FROM AUTHOR]

Aiming at the poor transient convergence and real-time performance of the horizontal vibration state variables of the high-speed elevator car system, and the low control accuracy and stability, this paper proposes an adaptive control strategy to ensure the transient response of the elevator car system. First, a prescribed preset transient performance function is introduced into the controller design to control the variation range of the state variables and ensure the steady transient performance of the elevator car; Second, representing the information of observation error/control error through algebraic operations, designing an adaptive law based on e-correction, estimating unknown parameters in the elevator car system, and achieving online parameter updates; Then, using neural networks to learn and compensate for unknown dynamics in the elevator car system, and solving the online estimation problem of neural network weights through adaptive laws, so that the tracking error and weight estimation error converge to a tight set near zero; Finally, using MATLAB/SIMULINK to compare and analyze the four control algorithms of passive control, PID control, adaptive control based on gradient descent method and transient response adaptive control proposed in this paper under two different rail excitations: Random excitation and pulse excitation. The simulation results show that the adaptive control strategy proposed in this paper effectively suppresses the horizontal vibration of the elevator car, makes the state variables have faster convergence speed and smaller convergence error, and ensures the stable and transient performance of the elevator car system. [ABSTRACT FROM AUTHOR]

Copyright of China Pulp & Paper is the property of China Pulp & Paper Magazines Publisher 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.)

Abstract: Using advanced machine learning techniques as an alternative to conventional double-entry volume equations known as classical allometric models, regression models of the inside-bark volume (dependent variable) for standing Eucalyptus globulus trunks (or main stems) have been built as a function of the following three independent variables: age, height and outside-bark diameter at breast height (D). The allometric models of volume, biomass or carbon support the estimation of carbon storage in forests and agroforestry systems. On one hand, this paper presents the construction of allometric models of the inside-bark volume for E. globulus trees. On the other hand, the experimental observed data (age, height, D and inside-bark volume) for 142 trees (E. globulus) were measured and a nonlinear model was built using a data-mining methodology based on multivariate adaptive regression splines (MARS) technique and multilayer perceptron networks (MLP) for regression problems. Coefficients of determination and Furnival’s indices indicate the superiority of the MARS technique over the allometric regression models and the MLP network. The agreement of the MARS model with observed data confirmed the good performance of the same one. Finally, conclusions of this innovative research are exposed. [Copyright &y& Elsevier]

In this paper, we present several remarks on the paper by Yao et al. (citeyearcite.nine). The results presented in the present paper are interesting improvements on the main results of Yao et al. [ABSTRACT FROM AUTHOR]

Due to the complex internal working process of circulating cooling water systems, most traditional control methods struggle to achieve stable and precise control. Therefore, this paper presents a novel adaptive control structure for the Twin Delayed Deep Deterministic Policy Gradient algorithm, which is based on a reference trajectory model (TD3-RTM). The structure is based on the Markov decision process of the recirculating cooling water system. Initially, the TD3 algorithm is employed to construct a deep reinforcement learning agent. Subsequently, a state space is selected, and a dense reward function is designed, considering the multivariable characteristics of the recirculating cooling water system. The agent updates its network based on different reward values obtained through interactions with the system, thereby gradually aligning the action values with the optimal policy. The TD3-RTM method introduces a reference trajectory model to accelerate the convergence speed of the agent and reduce oscillations and instability in the control system. Subsequently, simulation experiments were conducted in MATLAB/Simulink. The results show that compared to PID, fuzzy PID, DDPG and TD3, the TD3-RTM method improved the transient time in the flow loop by 6.09s, 5.29s, 0.57s, and 0.77s, respectively, and the Integral of Absolute Error(IAE) indexes decreased by 710.54, 335.1, 135.97, and 89.96, respectively, and the transient time in the temperature loop improved by 25.84s, 13.65s, 15.05s, and 0.81s, and the IAE metrics were reduced by 143.9, 59.13, 31.79, and 1.77, respectively. In addition, the overshooting of the TD3-RTM method in the flow loop was reduced by 17.64, 7.79, and 1.29 per cent, respectively, in comparison with the PID, the fuzzy PID, and the TD3. [ABSTRACT FROM AUTHOR]

A reliable human-robot workcell relies on accurate and nearly real-time updated models, especially in a constrained yet dynamic environment. This paper investigates digital twin-driven human-robot collaborative assembly enabled by function blocks. Leveraging sensor data, digital models are developed to precisely mimic physical human-robot collaborative settings supported by a digital-twin architecture. An advance-execution twin system based on the current status through real-time condition monitoring performs assembly planning and adaptive robot control using a network of function blocks. An augmented reality-based interaction method using HoloLens further facilitates human-centric assembly. An engine-assembly case study is performed to validate the effectiveness of the system. [ABSTRACT FROM AUTHOR]

The October 2023 issue of the IETE Journal of Research contains 70 articles covering various topics in the fields of Communications, Electromagnetics, Optoelectronics, Computers and Computing, Control Engineering, Electronic Circuits, Devices and Components, Instrumentation and Measurement, Medical Electronics, and Power Electronics. The issue includes articles on topics such as the performance of hybrid FSO/RF systems, complexity reduction in UFMC systems, lossless compression of hyperspectral imagery, miniaturized substrate integrated waveguide filters for 5G applications, object tracking using thermal and visible imaging, MIMO diversity antennas for UWB applications, metamaterial-based SWB-MIMO antennas, and more. The issue also features articles on spintronic ripple carry adders, lattice-shifted twist-induced photonic crystal waveguides, software project effort estimation, neural machine translation, supply chain financial risk management, brain computer interfaces, reversible data hiding schemes, IoT-based airshed management, lemmatizers, content-based image retrieval, and named entity recognition. [Extracted from the article]

When a robot performs tasks such as assembly or human–robot interaction, it is inevitable for it to collide with the unknown environment, resulting in potential safety hazards. In order to improve the compliance of robots to cope with unknown environments and enhance their intelligence in contact force-sensitive tasks, this paper proposes an improved admittance force control method, which combines classical adaptive control and machine learning methods to make them use their respective advantages in different stages of training and, ultimately, achieve better performance. In addition, this paper proposes an improved Deep Deterministic Policy Gradient (DDPG)-based optimizer, which is combined with the Gaussian process (GP) model to optimize the admittance parameters. In order to verify the feasibility of the algorithm, simulations and experiments are carried out in MATLAB and on a UR10e robot, respectively. The experimental results show that the algorithm improves the convergence speed by 33% in comparison to the general model-free learning method, and has better control performance and robustness. Finally, the adjustment time required by the algorithm is 44% shorter than that of classical adaptive admittance control. [ABSTRACT FROM AUTHOR]

To achieve frequency stability and economic efficiency in isolated microgrids, grid operators face a trade-off between multiple performance indicators. This paper introduces a data-driven adaptive load frequency control (DD-ALFC) approach, where the load frequency controller is modeled as an agent that can balance different objectives autonomously. The paper also proposes a priority replay soft actor critic (PR-SAC) algorithm to implement the DD-ALFC method. The PR-SAC algorithm enhances the policy randomness by using entropy regularization and maximization, and improves the learning adaptability and generalization by using priority experience replay. The proposed DD-ALFC method based on the PR-SAC algorithm can achieve higher adaptability and robustness in complex microgrid environments with multiple performance indicators, and improve both the frequency control and the economic efficiency. The paper validates the effectiveness of the proposed method in the Zhuzhou Island microgrid. [ABSTRACT FROM AUTHOR]

In modern engineering, the dynamics of many practical problems can be described by hyperbolic distributed parameter systems. This paper is devoted to the adaptive prescribed performance control for a class of typical uncertain hyperbolic distributed parameter systems, since uncertainties are inevitable in practice. The systems in question simultaneously have unknown in-domain spatially varying damping coefficient and unknown boundary constant damping coefficient. Moreover, dynamic boundary condition is considered in the present paper. These characteristics make the control problem in the paper essentially different from those in the related works. To solve the problem, using adaptive technique based projection operator, backstepping method developed for ODEs and Lyapunov stability theories, a powerful adaptive prescribed performance control scheme is proposed to successfully guarantee that all states of the resulting closed-loop system are bounded, furthermore, the original system state converges to an arbitrary prescribed small neighborhood of the origin. Compared with the existing results, the developed control schemes can not only effectively handle the serious uncertainties, but also overcome the technical difficulties in the infinite-dimensional backstepping control design method caused by the dynamic boundary condition and guarantee prescribed performance. [ABSTRACT FROM AUTHOR]

The low carbon park islanded microgrid faces operational challenges due to the high variability and uncertainty of distributed renewable energy sources. These sources cause severe random disturbances that impair the frequency control performance and increase the regulation cost of the islanded microgrid, jeopardizing its safety and stability. This paper presents a data-driven intelligent load frequency control (DDI-LFC) method to address this problem. The method replaces the conventional LFC controller with an intelligent agent based on a deep reinforcement learning algorithm. To adapt to the complex islanded microgrid environment and achieve adaptive multi-objective optimal frequency control, this paper proposes the quantum-inspired maximum entropy actor-critic (QIS-MEAC) algorithm, which incorporates the quantum-inspired principle and the maximum entropy exploration strategy into the actor-critic algorithm. The algorithm transforms the experience into a quantum state and leverages the quantum features to improve the deep reinforcement learning's experience replay mechanism, enhancing the data efficiency and robustness of the algorithm and thus the quality of DDI-LFC. The validation on the Yongxing Island isolated microgrid model of China Southern Grid (CSG) demonstrates that the proposed method utilizes the frequency regulation potential of distributed generation, and reduces the frequency deviation and generation cost. [ABSTRACT FROM AUTHOR]

In this paper, we dealt with the tracking control problem of a class of fractional-order uncertain systems with time delays. In order to handle the effects brought by the uncertainties, external disturbances, time-delay terms, and to overcome the obstacles caused by inputs saturation, the tracking controller, which consisted of linear control law, nonlinear law, and robust control law proposed in this paper, was designed by combining the composite nonlinear feedback control method and the properties of fractional order operators. Furthermore, the validation of this tracking controller was proved. [ABSTRACT FROM AUTHOR]

This paper discusses the congestion control challenges in a network employing multi-bottleneck Transmission Control Protocol/Active Queue Management (TCP/AQM). The study specifically focuses on networks characterized by input nonlinearity and unknown disturbances. We regard the network as a whole, and consider the influence between multiple nodes and unknown disturbance, a dynamic model of multi-bottleneck network is established. And the impact of dead zone and saturation on the system is taken into account for the first time in the model, the builded TCP/AQM model is more practicable. Based on the characteristics of fuzzy logic systems (FLS), combined with backstepping technology and Lyapunov function, an adaptive congestion control algorithm is designed to make full use of the link resources of each node and improve the network utilization. Ultimately, the proposed algorithm's efficacy and superiority are substantiated through simulation. [ABSTRACT FROM AUTHOR]

A complex dynamical network (CDN) can be considered as the composition system with the nodes subsystem (NS) and the links subsystem (LS), and both subsystems are coupled with each other. In this paper, two vector differential equations (VDE) are used to describe the dynamical behaviours of NS and LS, respectively, in which the dynamical behaviour of NS is considered as the VDE with the second derivative term (SDT). This paper mainly focuses on the dynamics of LS, which is represented as VDE with the intuitive topologic feature of outgoing links, and investigates the design of the tracking controller for NS and the auxiliary tracking objectives (ATO) for LS. Firstly, the dynamical models of NS and LS in CDN are proposed, and the corresponding assumptions are given. Secondly, based on Lyapunov stability theory, the controller of NS and the ATO of LS are designed so that the state of NS can asymptotically track the given reference signal. Finally, the effectiveness of the proposed control strategy in this paper is verified by the numerical simulation example with N two-links robots. Abbreviations: ATO: auxiliary tracking objectives; CDN: complex dynamical network; LS: links subsystem; MDE: matrix differential equation; NS: nodes subsystem; SDT:second derivative term; VDE: vector differential equation; [ABSTRACT FROM AUTHOR]

With the continuous development and progress of wireless self-organizing network communication technology, how to carry out long-distance cooperative control of multiple intelligences under the framework of an air–ground integrated wireless high-mobility self-organizing network has become a hot and difficult topic that needs to be solved urgently. This paper takes the air–ground integrated wireless high-mobility self-organizing network system as the basic framework and focuses on solving the long-distance cooperative fault-tolerant control of multi-intelligent bodies and the topological stability of a wireless mobile self-organizing network. To solve the above problems, a direct neural network with a robust adaptive fault-tolerant controller is designed in this paper. By constructing a symmetric population neural network model and combining it with the Lyapunov stabilization criterion, the system feedback matrix K has the ability of autonomous adaptive learning, and symmetrically distorts, rotates, or scales the training data to instantly adjust the system's fault-tolerant corrections and adaptive adjusting factors to resist the unknown disturbances and faults, to achieve the goals of multi-intelligent body stable control and the stable operation of a wireless high-mobility self-organizing network topology. Simulation results show that with the feedback adjustment of the multi-system under the designed controller, the multi-system as a whole has good fault-tolerant performance and autonomous learning approximation performance, and the tracking error asymptotically converges to zero. The experimental results show that the multi-flight subsystems fly stably, the air–ground integrated wireless high-mobility self-organizing network topology has good stability performance, and the maximum relative improvement of the topology stability performance is 50%. [ABSTRACT FROM AUTHOR]

In this paper, the robust stability of nonlinear system with unknown perturbation is considered combining operator-based right coprime factorization and fuzzy control method from the input-output view of point. In detail, fuzzy logic system is firstly combined with operator-based right coprime factorization method to study the uncertain nonlinear system. By using the operator-based fuzzy controller, the unknown perturbation is formulated, and a sufficient condition of guaranteeing robust stability is given by systematic calculation, which reduces difficulties in designing controller and calculating inverse of Bezout identity. Implications of the results related to former results are briefly compared and discussed. Finally, a simulation example is shown to confirm effectiveness of the proposed design scheme of this paper. [ABSTRACT FROM AUTHOR]

Controlling variable wind speed turbine (VWT) system based on a doubly-fed induction generator (DFIG) is a challenging task. It requires a control law that is both adaptable and robust enough to handle the complex dynamics of the closed control loop system. Sliding mode control (SMC) is a robust control technology that has shown good performance when employed as a passive fault-tolerant control for wind energy systems. To improve the closed control loop of VWT based on DFIG with the aim of improving energy efficiency, even in presence of nonlinearities and a certain range of bounded parametric uncertainties, whether electrically or mechanically, an adaptive passive fault tolerant control (AP-FTC) based on a self-tuning fractional integral sliding mode control law (ST-FISMC) developed from a novel hyperbolic fractional surface is proposed in this paper. ST-FISMC introduces a nonlinear hyperbolic function into the sliding manifold for self-tuning adaptation of control law, while fractional integral of the control law smooths discontinuous sign function to reduce chattering. Additionally, this work introduces an adaptive observer, developed and proved based on a chosen Lyapunov function. This observer is designed to estimate variations in electrical parameters and stator flux, ensuring sensorless decoupling in indirect field-oriented control (SI-FOC) of DFIG. Lyapunov theory is also used to prove stability of states vectors in closed control loop with presence of bounded parameters uncertainties or external disturbances. Simulation results show that the proposed approach offers better performance in capturing optimal wind energy, as well as the ability to regulate active/reactive power and high resilience in presence of occurring parameter uncertainties or external disturbances. [ABSTRACT FROM AUTHOR]

In this paper, we proposed a reachable set control method for a class of Markov jump cyber-physical systems (MJCPSs) with time-varying delays, which addressed the challenges posed by false data injection (FDI) attacks to system security. The goal was to find the set of regions where all MJCPSs states were reachable from the origin in the presence of bounded disturbances. The adaptive event-triggered control strategy was introduced to save network resources. It also reduced the impact of FDI attacks and external disturbances on system security. The conservatism of the results were reduced by constructing the Lyapunov-Krasovskii (L-K) functional with time-varying delays. Difference terms were estimated by using the discrete Wirtinger inequality and the improved extended reciprocally convex matrix inequality, and the ellipsoid reachable set of the MJCPS was obtained. Then, the reachable set controller was obtained by linear matrix inequalities (LMIs) solving technique. Finally, an example simulation proved the validity of the results. [ABSTRACT FROM AUTHOR]

Based on the adaptive control structure of neural networks, this paper proposes a novel output voltage control strategy for DC converters. The strategy regulates the inductor current to maintain a constant voltage by adjusting the duty cycle of four-switch buck—boost (FSBB) converters. A nonlinear average model for the FSBB converter, derived from its energy consumption, is introduced, and its effectiveness is demonstrated through simulations. The simulations confirm that the FSBB converter enables zero-voltage switching (ZVS) of the four switches across the entire operating voltage range. The comparative simulation results show that the proposed control strategy achieves faster voltage regulation while ensuring ZVS, leading to improved converter performance across the full power range. [ABSTRACT FROM AUTHOR]

Control technology for wheeled mobile robots is one of the core focuses in the current field of robotics research. Within this domain, model-free adaptive control (MFAC) methods, with their advanced data-driven strategies, have garnered widespread attention. The unique characteristic of these methods is their ability to operate without relying on prior model information of the control system, which showcases their exceptional capability in ensuring closed-loop system stability. This paper extensively details three dynamic linearization techniques of MFAC: compact form dynamic linearization, partial form dynamic linearization and full form dynamic linearization. These techniques lay a solid theoretical foundation for MFAC. Subsequently, the article delves into some advanced MFAC schemes, such as dynamic event-triggered MFAC and iterative learning MFAC. These schemes further enhance the efficiency and intelligence level of control systems. In the concluding section, the paper briefly discusses the future development potential and possible research directions of MFAC, aiming to offer references and insights for future innovations in control technology for wheeled mobile robots. [ABSTRACT FROM AUTHOR]

This paper addresses a class of uncertain nonlinear systems with disturbances that are challenging to model by proposing a novel model-free adaptive sliding mode control (MFASMC) scheme based on a discrete-time extended state observer (DESO). Initially, leveraging the pseudo partial derivative (PPD) concept in the model-free adaptive control (MFAC) framework, the discrete-time nonlinear model is converted into a full-form dynamic linearization (FFDL) model. Secondly, using the FFDL data model, a discrete sliding mode controller is designed. A discrete integral sliding mode surface is chosen to mitigate chattering during the reaching phase, and a hyperbolic tangent function with minimal slope variation is selected for smoother switching control. Furthermore, a DESO is designed to estimate uncertainties in the discrete system, enabling real-time compensation for the controller. Finally, a genetic optimization algorithm is employed for parameter tuning to minimize the time cost associated with selecting control parameters. The design process of this scheme relies solely on the data of the controlled system, without depending on a mathematical model. The proposed DESO-MFASMC scheme is tested through simulations using a typical numerical equation and the existing EFG-BC/320 electric heavy-duty forklift from the Quzhou Special Equipment Inspection Center. Simulation results show that the proposed method is significantly superior to the traditional MFAC and PID control methods in tracking accuracy and robustness when dealing with nonlinear disturbance of the system. The DESO-MFASMC scheme proposed in this paper not only shows its advantages in theory but also verifies its effectiveness and practicability in engineering through practical application. [ABSTRACT FROM AUTHOR]

The fluctuations in power output from distributed power sources are rapid and dramatic, causing voltage fluctuations in the distribution network that threaten the safety of electricity consumption. Soft open points (SOPs) can replace traditional contact switches and are expected to suppress voltage fluctuations. However, traditional power regulation methods based on the reference values are unable to address rapid and large voltage fluctuations. Therefore, this paper proposes an adaptive power control method for SOPs based on virtual impedance. The SOP is modeled as a series link of adjustable impedance and a voltage source. Then, the voltage difference component is used to calculate the reference for the DQ domain to regulate the power flow in real time automatically. By doing so, the proposed method can smooth the voltage fluctuations in the distribution network. Additionally, the virtual impedance is also optimized to minimize the power loss. Finally, the method is validated through simulation and experiments, demonstrating that this method can automatically regulate power and significantly reduce voltage fluctuations. [ABSTRACT FROM AUTHOR]

This paper presents a new adaptive modification of active disturbance rejection control (ADRC) with parameter estimation based on a recursive least-squares (RLS) method. The common ADRC used in many applications relies on the simple approach, which assumes the simplification of the object into an integral chain form. However, this model-free ADRC does not guarantee the stability of a closed-loop system in the presence of noticeable modeling uncertainties, so it is compared in this paper to another approach, in which the linear part of the system is included in the ADRC framework (generalized ADRC). This incorporation of the model is examined in the paper for a wide range of model and controller parameters, considering also the presence of external disturbances as well as parameter uncertainties, pointing out the limitations of fixed-gain algorithms. Then, the adaptive modification of the model-based ADRC is proposed, which is equipped with a real-time estimation of model parameters by means of the RLS method in continuous time. The stability conditions of the proposed modification of the algorithm in the closed control loop are also analyzed. It can be concluded that, under appropriate conditions, the inclusion of information about known plant parameters into the ADRC can noticeably improve the conditions of the control system. The proposed adaptive model-based approach enables quality improvement during the control process even with initially unknown parameters, for time-varying parameters, and in the presence of parametric uncertainties and external disturbances. The tests were performed on a real plant—the task of controlling the angular velocity of the direct current (DC) motor was considered. [ABSTRACT FROM AUTHOR]

In the traditional iterative learning control (ILC) method, the operational time interval is conventionally fixed to facilitate a seamless learning process along the iteration axis. However, this condition may frequently be contravened in real-time applications owing to unknown uncertainties and unpredictable factors. In essence, replicating a control system at a consistent time interval proves challenging in practical scenarios. This paper proposes an adaptive iterative learning control (AILC) method for the multi-input–multi-output (MIMO) nonlinear system with nonuniform trial lengths and an invertible control gain matrix. Compared to the existing AILC research that features nonuniform trial lengths, the control gain matrix of the system in this paper is assumed to be invertible. Hence, the general requirement in the conventional AILC method that the control gain matrix of the system is positive-definite (or negative-definite) or even known is relaxed. Moreover, the tracking reference allows it to be iteration-varying. Finally, to prove the convergence of the system, the composite energy function is introduced and to verify the validity of the AILC method, a robot movement imitation with an uncalibrated camera system is used. The simulation results show that the actual output can track the desired reference trajectory well, and the tracking error converges to zero after 30 iterations. [ABSTRACT FROM AUTHOR]

Object tracking is one of the major tasks for mobile robots in many real-world applications. Also, artificial intelligence and automatic control techniques play an important role in enhancing the performance of mobile robot navigation. In contrast to previous simulation studies, this paper presents a new intelligent mobile robot for accomplishing multi-tasks by tracking red-green-blue (RGB) colored objects in a real experimental field. Moreover, a practical smart controller is developed based on adaptive fuzzy logic and custom proportional-integral-derivative (PID) schemes to achieve accurate tracking results, considering robot command delay and tolerance errors. The design of developed controllers implies some motion rules to mimic the knowledge of experienced operators. Twelve scenarios of three colored object combinations have been successfully tested and evaluated by using the developed controlled image-based robot tracker. Classical PID control failed to handle some tracking scenarios in this study. The proposed adaptive fuzzy PID control achieved the best accurate results with the minimum average final error of 13.8 cm to reach the colored targets, while our designed custom PID control is efficient in saving both average time and traveling distance of 6.6 s and 14.3 cm, respectively. These promising results demonstrate the feasibility of applying our developed image-based robotic system in a colored object-tracking environment to reduce human workloads. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications 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.)

In this paper, for strict-feedback nonlinear systems with positive odd rational powers, the problem of adaptive fixed-time tracking control (FTTC) is considered. With the support of fixed-time theorem and adaptive backstepping method, this paper proposes a new globally adaptive FTTC strategy, which can overcome the singular problems caused by the derivation of virtual control signal. In addition, the proposed control scheme is a universal approach that can be applied to other types of nonlinear systems. Furthermore, the designed adaptive fixed-time tracking controller ensures the boundedness of all signals in the controlled system, and it guarantees the tracking error converges to a small neighbourhood near equilibrium in fixed-time. Finally, the simulation result further verifies the feasibility of the presented control scheme. [ABSTRACT FROM AUTHOR]

Lower limb augmentation exoskeletons (LLAE) have been applied in several domains to enforce human walking capability. As humans can adjust their joint moments and generate different amounts of mechanical energy while walking on different terrains, the LLAEs should provide adaptive augmented torques to the wearer in multi-terrain environments, which requires LLAEs to implement accurate terrain parameter recognition. However, the outputs of previous terrain parameter recognition algorithms are more redundant, and the algorithms have higher computational complexity and are susceptible to external interference. Therefore, to resolve the above issues, this paper proposed a neural network regression (NNR)-based algorithm for terrain slope parameter recognition. In particular, this paper defined for the first time a unified representation of terrain parameters: terrain slope (TS), a single parameter that can provide enough information for exoskeleton control. In addition, our proposed NNR model uses only basic human parameters and LLAE joint motion posture measured by an Inertial Measurement Unit (IMU) as inputs to predict the TS, which is computationally simpler and less susceptible to interference. The model was evaluated using K-fold cross-validation and the results showed that the model had an average error of only 2.09 ∘ . To further validate the effectiveness of the proposed algorithm, it was verified on a homemade LLAE and the experimental results showed that the proposed TS parameter recognition algorithm only produces an average error of 3.73 ∘ in multi-terrain environments. The defined terrain parameters can meet the control requirements of LLAE in urban multi-terrain environments. The proposed TS parameter recognition algorithm could facilitate the optimization of the adaptive gait control of the exoskeleton system and improve user experience, energy efficiency, and overall comfort. [ABSTRACT FROM AUTHOR]

Low power brushless direct current (BLDC) motors are used in many consumer appliances. These motors have a relatively high winding resistance and therefore current control loop can be avoided in some cases, but fast and accurate speed regulation can be still needed. To minimize harmonics and generated sound noise, improved sinusoidal pulse width modulation (PWM) has been tested in the paper. As the most suitable commutation type, the sine wave with the third harmonic component has been selected. This type of communication reduced the torque ripple of the motor. This paper analyses the possibility to improve traditional proportional-integral-derivative (PID) speed regulator with Fuzzy logic block. A simulation model of BLDC motor, inverter, speed detection circuit and controller have been created. Simulation results showed that by applying the Fuzzy-based PID controller, the transient time can be reduced from 0.2 s to 0.05 s and overshoot can be avoided in comparison with traditional PID controller. Experimental results show a significant improvement in the motor dynamics—the overshoot and transient time were reduced twice. The difference with simulation results and experimental ones can be explained by delays introduced by the microcontroller. [ABSTRACT FROM AUTHOR]

In order to effectively prevent and combat online public opinion crises triggered by major emergencies, this paper explores the dissemination mechanism of uncertain information on online social platforms. According to the decisionmaking behavior of netizens after receiving uncertain information, they are divided into eight categories. Considering that there will be a portion of netizens who clarify uncertain information after receiving it, this paper proposes a SEFTFbTbMR model of uncertain information clarification behavior. The propagation dynamics equations of the model are given based on the theory of differential equations, the basic regeneration number R0 of the model is calculated, and the existence and stability of the equilibrium point of the model are analyzed. The theoretical analysis of the model is validated using numerical simulation software, and sensitivity analysis is performed on the parameters related to R0. In order to reduce the influence caused by uncertain information, the optimal control strategy of the model is proposed using the Hamiltonian function. It is found that the dissemination of uncertain information among netizens can be suppressed by strengthening the regulation of social platforms, improving netizens' awareness of identifying the authenticity of information, and encouraging netizens to participate in the clarification of uncertain information. The results of this work can provide a theoretical basis for future research on the uncertain information dissemination mechanism triggered by major emergencies. In addition, the results can also provide methodological support for the relevant government departments to reduce the adverse effects caused by uncertain information in the future. [ABSTRACT FROM AUTHOR]

Introduction: Shape memory alloy (SMA) actuators are attractive options for robotic applications due to their salient features. So far, achieving precise control of SMA actuators and applying them to human-robot interaction scenarios remains a challenge. Methods: This paper proposes a novel approach to deal with the control problem of a SMA actuator. Departing from conventional mechanism models, we attempt to describe this nonlinear plant using a gray-box model, in which only the input current and the output displacement are measured. The control scheme consists of the model parameters updating and the control law calculation. The adaptation algorithm is founded on the multi-innovation concept and incorporates a dead-zone weighted factor, aiming to concurrently reduce computational complexities and enhance robustness properties. The control law is based on a PI controller, the gains of which are designed by the pole assignment technique. Theoretical analysis proves that the closed-loop performance can be ensured under mild conditions. Results: The experiments are first conducted through the Beckhoff controller. The comparative results suggest that the proposed adaptive PI control strategy exhibits broad applicability, particularly under load variations. Subsequently, the SMA actuator is designed and incorporated into the hand rehabilitation robot. System position tracking experiments and passive rehabilitation training experiments for various gestures are then conducted. The experimental outcomes demonstrate that the hand rehabilitation robot, utilizing the SMA actuator, achieves higher position tracking accuracy and a more stable system under the adaptive control strategy proposed in this paper. Simultaneously, it successfully accommodates hand rehabilitation movements for multiple gestures. Discussion: The adaptive controller proposed in this paper takes into account both the computational complexity of the model and the accuracy of the control results, Experimental results not only demonstrate the practicality and reliability of the controller but also attest to its potential application in humanmachine interaction within the field of neural rehabilitation. [ABSTRACT FROM AUTHOR]

IEEE SASO (Self-Adapting and Self-Organizing Systems) is the premier international conference for computer systems and networks that autonomously change some aspect of themselves: code, form, function, shape, structure, components, and so on. Over the past 10 years, it has emerged as a key multidisciplinary event for sharing theoretical insights and technical innovations across the numerous scientific fields and application domains impacted by this research. In 2013, the conference was hosted by Drexel University in Philadelphia, Pennsylvania, and offered an extensive program of high-quality papers. This special issue of ACM TAAS presents selected, revised, and extended best articles, which showcase the rich variety and depth of the SASO scientific community. [ABSTRACT FROM AUTHOR]

This paper explores finite-time formation control of multi-agent systems (MASs) with high-order nonaffine nonlinear dynamics and saturated input. Based on active disturbance rejection control theory, extended state observer is employed to identify unknown nonaffine nonlinear functions in MASs. The proposed control law consisting of backstepping control, tracking differentiator, and finite-time performance function is adopted for MASs to achieve the desired formation while reaching performance requirements. An auxiliary dynamic compensator is introduced to correct the control deviation caused by input saturation. Lyapunov stability theory is utilized to analyze the stability of the closed-loop system, which guarantees that the formation tracking error can asymptotically converge to an arbitrarily small neighborhood around zero in finite time. Finally, the simulation results show that compared to the adaptive, cooperative learning, and virtual structure methods, the proposed control algorithm has stronger tracking ability and faster setting time (1.8 s) under the influence of nonaffine nonlinear uncertainties. The integral square error for the formation control strategy in this paper is 0.16, which is much smaller than the abovementioned methods and is therefore provided to manifest the validity and feasibility of the proposed control strategy. [ABSTRACT FROM AUTHOR]

Grey balance plays an important role in determining the device values needed to reproduce colours which appear achromatic throughout the tonal range. However, complete observer adaptation to the media white rarely occurs, and these designated device values can still appear non-neutral. This poses a problem for cross-media reproductions, where a mismatch in neutral colours is often the most noticeable difference between them. This paper presents two related experiments which investigate a means of gaining better visual agreement between reproductions which have different background colours or media whites. The first quantifies the degree of adjustment (the degree of media relative transform) needed to make an appearance match between grey patches on a white background and on background colours of various hues and colourfulness. It was found that the degree of adjustment was near-linearly related to the luminance of the patch itself, with lighter patches requiring greater adjustment towards the background colour. Neither the hue nor the chroma of the patch's background had any significant effect on the underlying function. In the second experiment, this concept is applied to pictorial images on paper-coloured backgrounds. Three pixelwise rendering strategies were compared. In side-by-side viewing, the adaptive control of neutrals outperformed the media relative transform in all cases. Even for modest differences in paper colour (1Eab of 3), images with significant neutral content benefited from the adaptive approach. [ABSTRACT FROM AUTHOR]

It is very challenging for robots to perform grinding and polishing tasks on surfaces with unknown geometry. Most existing methods solve this problem by modeling the relationship between the force sensing information and surface normal vectors by analyzing the forces on special end tools such as spherical tools and cylindrical tools and simplified friction model. In this paper, we propose a normal vectors learning method to simultaneously control end-effector force and direction on unknown surfaces. First, the relation that mapping the force sensing information to the surface normal vectors is learned from the demonstrated data on the known plane using locally weighted regression. Next, the learned relation is used to estimate surface normal vectors on the unknown surface. To improve the force control precision on the unknown geometry surface, the adaptive force control is developed. To improve the direction control precision due to friction, the iterative learning control is developed. The proposed method is verified by comparative simulations and experiments using the Franka robot. Results show that the end-effector can be controlled perpendicular to the surface with a certain force. [ABSTRACT FROM AUTHOR]

In the model (m:n), to improve the autonomous collaborative interception capability for air vehicle, a new autonomous cross-collaborative interception algorithm based on GTSMC (Global Terminal Sliding Mode Control) and real-time virtual geometry is proposed in this paper. Firstly, the conception of an autonomous cross-collaboration is defined and the multi-air vehicle for the multi- object interception problem is formulated. Then, this paper presents the dynamic situation assessment function, which considers the real-time flight status and cooperative status of the air vehicle during the interception of the object. At the same time, this paper states the condition of whether the air vehicle is in a cooperative state and proves it. After completing the dynamic situation assessment, and considering the dynamic of the air vehicles, a new controller is designed by using GTSMC and the idea of backstepping method. Simultaneously, this paper gives a stability analysis of the closed-loop system by using Lyapunov theory. Finally, to demonstrate the effectiveness of the proposed algorithm, several simulation cases which consider different interception scenarios are given. The simulation results show that the new collaborative interception algorithm can provide better autonomous cross-collaborative interception capability and higher accuracy. [ABSTRACT FROM AUTHOR]

The IEEE International Conference on Self-Adapting and Self-Organizing Systems (SASO) is the main forum for studying and discussing the foundations of a principled approach to engineering systems, networks, and services based on self-adaptation and self-organization. Over the past decade, it has consolidated as the primary scientific conference for sharing ideas on algorithms, technologies, tools and applications across a wide range of scientific fields. In 2016, the conference was hosted by the University of Augsburg, in Augsburg, Germany; its scientific program comprised full papers, short papers, poster and demo presentations, workshops, doctoral symposium and tutorials. This special issue of ACM TAAS champions some of the most solid research results of SASO 2016, presenting selected, revised and extended best articles. [ABSTRACT FROM AUTHOR]

Assigning repetitive and physically demanding construction tasks to robots can alleviate human workers' exposure to occupational injuries, which often result in significant downtime or premature retirement. However, the successful delegation of construction tasks and the achievement of high-quality robot-constructed work requires transferring necessary dexterous and adaptive construction craft skills from workers to robots. Predefined motion planning scripts tend to generate rigid and collision-prone robotic behaviors in unstructured construction site environments. In contrast, imitation learning (IL) offers a more robust and flexible skill transfer scheme. However, the majority of IL algorithms rely on human workers repeatedly demonstrating task performance at full scale, which can be counterproductive and infeasible in the case of construction work. To address this concern, in this paper, we propose an immersive and Cloud Robotics-based virtual demonstration framework that serves two primary purposes. First, it digitalizes the demonstration process, eliminating the need for repetitive physical manipulation of heavy construction objects. Second, it employs a federated collection of reusable demonstrations that are transferable for similar tasks in the future and can, consequently, reduce the requirement for repetitive illustration of tasks by human agents. In addition, to enhance the trustworthiness, explainability, and ethical soundness of the robot training, this framework utilizes a hierarchical imitation learning (HIL) model to decompose human manipulation skills into sequential and reactive subskills. These two layers of skills are represented by deep generative models; these models enable adaptive control of robot action. The proposed framework has the potential to mitigate technical adoption barriers and facilitate the practical deployment of full-scale construction robots to perform a variety of tasks with human supervision. By delegating the physical strains of construction work to human-trained robots, this framework promotes the inclusion of workers with diverse physical capabilities and educational backgrounds within the construction industry. [ABSTRACT FROM AUTHOR]

Fretting wear in the contact area between the aero-engine blade tenon and turbine disk mortise has an important influence on the performance of the aero-engine. In this paper, the tenon joint structure of the DZ125/FGH99 superalloy material is taken as the research object, and the finite element model of the fir-tree tenon joint structure is established. Through subroutine invocation and mesh adaptive control technology, the fretting wear problem of dissimilar material contact pairs under composite load is numerically studied. The results show that for the specific tenon joint structure and load and boundary conditions studied in this paper, the maximum wear occurs on the contact surface of the first tooth, and the surface will show different partial slip states in different load cycles. The slip region always extends from the two contact edges to the interior, and the upper side has a larger range. Wear has a significant effect on the stress distribution and stick–slip state of the contact surface. The second and third teeth have a small amount of wear and are basically in a stick state during the entire wear process. Therefore, wear has little effect on the stress distribution and the stick–slip state of the contact surface. This study reveals the coupling relationship between the fretting wear and contact state of the tenon joint structure. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive switching anti-disturbance attitude control scheme based on improved snake optimizer (SO) is proposed for quadrotor attitude control when a quadrotor unmanned aerial vehicle is affected by measurement noise. The adaptive switching disturbance rejection controller (AWDRC) is composed of linear active disturbance rejection control and adaptive switching extended state observer which is used to achieve accurate signals reconstruction performance under measurement noise. Then, the improved SO (ISO) algorithm is developed with quadratic interpolation and comprehensive learning strategies to obtain the optimal parameters of the quadrotor attitude controller. The performance validity of ISO is demonstrated here by experiments on the CEC-2017 and the CEC-2020 benchmark functions with several state-of-the-art meta-heuristic algorithms. Secondly, the proposed ISO-based AWDRC algorithm is used in quadrotor attitude tracking control and compared with three other excellent active disturbance rejection controllers in a comparative experiment, and the experimental results show the effectiveness of the proposal. Finally, the robustness of the proposed method to parameters perturbation of the quadrotor attitude system is analyzed by Monte Carlo experiments. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of spacing control between adjacent trains in train formation and proposes a distributed train-formation speed-convergence cooperative-control algorithm based on barrier Lyapunov function. Considering practical limitations such as communication distance and bandwidth constraints during operation, not all trains can directly communicate with the leader and obtain the expected trajectory it sends, making it difficult to maintain formation consistency as per the predetermined ideal state. Furthermore, to address the challenge of unknown external disturbances encountered by trains during operation, this paper designs a distributed observer deployed on each train in the formation. This observer can estimate and dynamically compensate for unknown reference trajectories and disturbances solely based on the states of adjacent trains. Additionally, to ensure that the spacing between adjacent trains remains within a predefined range, a safety hard constraint, this paper encodes the spacing hard constraint using barrier Lyapunov function. By integrating nonlinear adaptive control theory to handle model parameter uncertainties, a barrier Lyapunov function-based adaptive control method is proposed, which enables all trains to track the reference trajectory while ensuring that the spacing between them remains within the preset interval, therefore guaranteeing the asymptotic stability of the closed-loop system. Finally, a practical example using data from the Guangzhou Metro Line 22, specifically the route from Shiguang Road Station to Chentougang Station over three stations and two sections, is utilized to validate the effectiveness and robustness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Photovoltaic inverters are widely utilized in microgrid systems working as the key equipment for converting solar energy into usable electricity. This paper presents a fuzzy sliding mode control (FSMC) method for the photovoltaic inverter in a microgrid. The inverter module uses voltage control to achieve stable AC output voltage. Moreover, to deal with system uncertainty and non-linearity in the photovoltaic inverter, a fuzzy controller was designed to realize real-time adaptation of the gains of the constant term and the reaching term in the sliding mode control law, which serves as a compensating controller for traditional sliding mode control. The gains of the exponential reaching law can be adjusted according to the system state instead of fixed gains, which can effectively reduce the chattering phenomenon and improve the robustness of the photovoltaic microgrid. Finally, the Lyapunov stability theory was used to ensure the stability of the entire control system, achieving a high-performance independent power supply for loads in a microgrid. Simulation results show that the designed control system is more robust to load disturbances and has superior dynamic performance. [ABSTRACT FROM AUTHOR]

In this paper, a Multi-Index Nonlinear Robust Adaptive Control (MINRAC) method was proposed for ultra-complex multi-input multi-output power systems with uncertain parameter factors and external disturbances. The controller designed by this method has excellent static and dynamic characteristics. Under the condition of the uncertainty of system parameters and external disturbance at the same time, the MINRAC method can ensure that the multiple indexes concerned by ultra-complex power systems can be controlled at their expected values. The simulation results showed that the control mechanism of MINRAC method was consistent in both single-machine infinite bus system and multi-machine interconnected coupling system. The output function chooses power angle, angular frequency and terminal voltage as constraints. When the system has parameter uncertainty and external interference, the uncertain parameter values are adjusted by adaptive control to force these indicators to tend to the given expected value. For three-phase short circuit, which is the most serious fault in power system, the use of multi index nonlinear robust controller can ensure that the system is stable in a wide range and has better dynamic performance. [ABSTRACT FROM AUTHOR]

This paper focuses on the study of the tracking performance of a novel two-degrees-of-freedom (two-DOF) voice coil motor (VCM). Intelligent control algorithms estimate the motor control model by the relationship between the input and output values, and it is hard to achieve excellent tracking performance due to the wider variational range of motor electromagnetic parameters (MEPs). This paper proposes estimated methods for MEPs and an adaptive feedforward control algorithm based on estimated MEPs (ACBE). ACBE can alter its control parameters from inside out in time with the change in MEPs. Experiments are carried out to verify that the proposed ACBE realized excellent tracking performance upon the novel two-DOF VCM. This study indicates that an adaptive control algorithm with high-precision control parameters has better tracking performance upon a motor control model with large variation. [ABSTRACT FROM AUTHOR]