Search

Copyright of HMD: Praxis der Wirtschaftsinformatik is the property of Springer Nature 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.)

Pesticides are essential for removing plant pests and sustaining good yields on agricultural land. Excessive use has detrimental repercussions, such as the depletion of soil fertility and the proliferation of immune insect species, such as Nilaparvata lunges and Nezara viridula. Unmanned aerial vehicle (UAV) variable-rate spraying offers a precise and adaptable alternative strategy for overcoming these challenges. This study explores research trends in the application of semi-automatic approaches and land-specific platforms for precision spraying. The employment of an autonomous control system, together with a selection of hardware such as microcontrollers, sensors, pumps, and nozzles, yields the performance necessary to accomplish spraying precision, UAV performance efficacy, and flexibility in meeting plant pesticide requirements. This paper discusses the implications of ongoing and developing research. The comparison of hardware, control system approaches, and data acquisition from the parameters of each study is presented to facilitate future research. Future research is incentivized to continue the precision performance of the variable rate development by combining it with cropland mapping to determine the need for pesticides, although strict limits on the amount of spraying make it difficult to achieve the same, even though the quality is very beneficial.

The purpose of the paper is to propose a new implementation of the PID (proportional–integral–derivative) algorithm in digital hardware. The proposed structure is optimized for cost. It follows a serialized, rather than parallel, scheme. It uses only one arithmetic block, performing the multiply-and-add operation. The calculations are carried out in a sequentially cyclic manner. The proposed circuit operates on standard single-precision (32-bit) floating-point numbers. It implements an extended PID formula, containing a non-ideal derivative component, and weighting coefficients, which enable reducing the influence of setpoint changes in the proportional and derivative components. The circuit was implemented in a Cyclone V FPGA (Field-Programmable Gate Array) device from Intel, Santa Clara, CA, USA. The proper operation of the circuit was verified in a simulation. For the specific implementation, which is reported in the paper, the sampling period of 516 ns was obtained, which means that the proposed solution is comparable in terms of speed with other hardware implementations of the PID algorithm operating on single-precision floating-point numbers. However, the presented solution is much more efficient in terms of cost. It uses 1173 LUT (Look-up Table) blocks, 1026 registers, and 1 DSP (Digital Signal Processing) block, i.e., about 30% of logic resources required by comparable solutions. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Robotics is currently the most cutting-edge international science and technology, as well as a highvalue-added core technology. Robots are widely used in a variety of industrial fields, as a new direction in the development of robotics, and play an important role in solving the current employment problems in China. OBJECTIVES: This paper combines its research results, introduces the machine learning method in the robot control system, and establishes the employment index system in the robot working environment by combining the employment factors with the environmental relationship analysis. METHODS: This paper combines its research results, introduces the machine learning method in the robot control system, and establishes the employment index system in the robot working environment by combining the employment factors with the environmental relationship analysis. RESULTS: The study found that the willingness of university students to choose a job gradually increases as their education level rises; the lower the level of education, the weaker their willingness to look for a job; the higher the level of education the more sensitive they are to the quality of education and educational specialities, the higher their willingness to work. CONCLUSION: Based on the robot control system design the factors that have an impact on the environment in real economic activities (e.g., age, gender, occupation, education level, etc.) play a role in promoting the future application and development of robotics in China. [ABSTRACT FROM AUTHOR]

This paper investigates the adaptive control for a class of uncertain nonlinear systems under denial-of-service (DoS) attacks. We analyze the closed-loop system stability under DoS attacks in terms of attack duration, attack frequency and resting time duration respectively. Three scenarios of DoS attacks against the system stability are considered. Firstly, it is shown that if the duration of each attack is less than a given constant, asymptotical convergence of system output is still preserved. Secondly, if the bounds on the frequency and duration of attacks with respect to overall intervals meet certain conditions, the proposed event-triggered control scheme guarantees that all the closed-loop signals are globally bounded and the stabilization error converges to a ball with a radius arbitrarily small. Thirdly, if resting time duration meets certain conditions after an arbitrarily long attack, closed-loop boundedness is still preserved. Simulation results are shown to illustrate the effectiveness of the proposed control schemes. [ABSTRACT FROM AUTHOR]

Hardware-in-the-Loop (HiL) testing has emerged as a pivotal technique for validating and enhancing the performance of control systems across various industries, notably in automotive and embedded manufacturing sectors. This paper provides an extensive exploration of HiL testing methodologies and their application in the Internet of Things (IoT) domain. HiL testing involves simulating real-world conditions by integrating specialized simulation software and hardware components, facilitating realistic evaluation of controllers' functionality and behaviour. Different variants of HiL, such as Model-in-the-Loop (MiL), Software-in-the-Loop (SiL), Processor-in-the-Loop (PiL), and Hardware-in-the-Loop (HiL), are discussed within the broader context of XiL (Anything-in-the-Loop) models. The paper underscores the significance of HiL testing in optimizing the development process, reducing costs, and minimizing hazards. It elaborates on the utilization of HiL testing to validate intricate control algorithms, simulate fault scenarios, and assess system behaviour under diverse conditions. The integration of HiL testing with IoT applications is examined, highlighting the potential benefits in areas like smart cities, industrial IoT, healthcare, agriculture, and energy management. The study presents a novel fault injection framework within HiL testing, allowing real-time analysis of complex systems' responses to simulated faults. This framework aids in identifying vulnerabilities and evaluating system reliability, thereby enhancing safety and performance. The authors introduce the concept of sensor schema, Observations & Measurements (O&M), and Sensor Web Enablement (SWE) as key components for efficient sensor data integration, sharing, and interoperability. The paper concludes by emphasizing the indispensable role of Hardware-in-the-Loop testing in fostering innovation, safety, and efficiency in the IoT landscape. By offering a comprehensive overview, analysis, and practical insights into the application of HiL testing methodologies, this research contributes to the advancement of robust and reliable IoT systems. [ABSTRACT FROM AUTHOR]

While using multirotor UAVs for transport of suspended payloads, there is a need for stability along the desired path, in addition to avoidance of any excessive payload oscillations, and a good level of precision in maintaining the desired path of the vehicle. However, due to the nonlinear and underactuated nature of the system, in addition to the presence of mismatched uncertainties, the development of a control system for this application poses an interesting research problem. This paper proposes a control architecture for a multirotor slung load system by integrating a Multi-Surface Sliding Mode Control, aided by a Radial Basis Function Neural Network, with a Deep Q-Network Reinforcement Learning agent. The former will be used to ensure asymptotic tracking stability, while the latter will be used to suppress payload oscillations. First, we will present the dynamics of a multirotor slung load system, represented here as a quadrotor with a single pendulum load suspended from it. We will then propose a control method in which a multi-surface sliding mode controller, based on an adaptive RBF Neural Network for trajectory tracking of the quadrotor, works in tandem with a Deep Q-Network Reinforcement Learning agent whose reward function aims to suppress the oscillations of the single pendulum slung load. Simulation results demonstrate the effectiveness and potential of the proposed approach in achieving precise and reliable control of multirotor slung load systems. [ABSTRACT FROM AUTHOR]

As an integral part of the European strategy for advancing fusion-generated electricity, IFMIF-DONES represents a high-intensity neutron irradiation plant with the main purpose of assessing the suitability of materials for fusion reactor applications. Its primary mission is to examine how materials respond to irradiation within a neutron flux that mimics the conditions expected in the first wall of the proposed DEMO reactor, which is intended to succeed ITER. Consequently, IFMIF-DONES, whose construction is slated to commence shortly, plays a pivotal role in aiding the development, approval, and safe operation of DEMO, as well as future fusion power plants. This paper provides a quick overview of the current development of the IFMIF-DONES neutron source with a particular snapshot of the present engineering design status for what concerns the instrumentation and control systems together with its complex diagnostics, that guarantees the safe monitoring, supervision and regulation of all operations. The current status of design, after the completion of the preliminary design phase is presented, as well as the existing and future plans for their integration also using some of the new capabilities offered by Artificial Intelligence tools. [ABSTRACT FROM AUTHOR]

The technical maturity of autonomous driving enables the discussion of beneficial use cases to leverage its full potential. In this paper, we target one such use case: Platooning is the efficient convoying of vehicles by making use of self-driving capabilities and inter-vehicle communication. Many advantages arise from grouping vehicles in platoons with a small inter-vehicle distance, such as energy savings, congestion reduction, and safety improvements. However, due to the diversity of involved stakeholders, numerous objectives have to be balanced to leverage the full potential of platooning. Furthermore, these objectives also depend on various factors that influence their optimization. The vast majority of existent literature only targets a subset of related objectives and underlying factors. This paper provides an overview which categorizes objectives and influencing factors. Additionally, metrics for the evaluation of objective attainment are proposed. [ABSTRACT FROM AUTHOR]

In this paper, a new modulation strategy based on the carrier-based switching strategy for the non-square direct matrix converters (MC) is proposed to control the active power of distributed generation (DG) units. In this strategy, the active power of DGs is controlled by the central input current control of the non-square direct MC independent from the voltage and frequency. Conventionally, each DG has a converter, and for supplying a load with N number of DGs, N number of converters are needed and each converter has its own modulation switching and control strategy to control the power output of each DG. Needless to say, in a microgrid with N number of DGs, the control strategy of each converter has more complex structure than that of a microgrid with one converter, and surely the former strategy entails more volume and price. Using the proposed converter in this paper, it is possible to supply a load with N number of DGs through one converter. Also, the power outputs of all DGs are controlled by a central control strategy. The proposed central control strategy is described and simulated for a typical microgrid. Experimental and simulation results validate the effectiveness of the proposed converter and the proposed strategy. The results demonstrate the applicability and efficiency of the system and verify the theoretical analysis. [ABSTRACT FROM AUTHOR]

Semiconductor vertical furnaces must achieve even faster and more precise temperature control due to the demand for ever-reducing the minimum feature size or critical dimension in semiconductor chips. Also, the insulation performance of vertical furnaces has improved to reduce power consumption; as a side effect, temperature reduction operation takes longer without active cooling. Therefore, in addition to heaters, vertical furnaces equipped with coolers have emerged to achieve higher productivity, more precise temperature control, and lower power consumption. However, the inability to generate positive and negative control inputs for one of the heaters or coolers poses a challenge for an intuitive controller parameter design. The aim of this paper is to address these issues by proposing an intuitive method for designing a controller in the frequency domain which virtually integrates heaters and coolers. We implemented the controller in a full-scale actual semiconductor vertical furnace and first confirmed that the linearity is high. Furthermore, we experimentally verified that the controller achieves both high-speed, high-precision temperature control and low power consumption. [ABSTRACT FROM AUTHOR]

Automated Guided Vehicles (AGVs) are essential elements of manufacturing intralogistics and material handling. Improving the position accuracy along the AGV trajectory allows the vehicle to work on narrower aisles with lower error tolerance. Despite the increasing number of papers in AGVs and mobile robots' position control research area, there is a lack of curatorial work presenting and analyzing the control strategies applied in the problem domain. Therefore, the main objective is to analyze the published researches of the past seven years on the position control of AGVs to recognize research patterns, gaps, and tendencies, outlining the research field. The paper proposes a systematic literature review to investigate the research field from the controller design perspective. Its protocol and procedures are presented in detail. Four main research topics were addressed: the control strategies used in the AGV position control problem, how the literature presents the AGV operating requirement of position accuracy, how the literature validate the proposed controller and present their results regarding the system's position accuracy, and the technological tendencies the proposed solutions reveals. Besides, within the main topics, other points were investigated, such as the AGV application area, the considered mathematical model, the sensors and guidance system used, and the maximum payload of the vehicle and operation under different load conditions. The data synthesis shows the predominant control strategies applied to the problem and the interaction among distinct control theory areas, indicating a notable interaction of Intelligent Control techniques to the other strategies. The paper's contributions are using a systematic literature review method over the AGV position control publications, presenting an overview of the research area, analyzing the research question topics from selected articles, and proposing a research agenda. [ABSTRACT FROM AUTHOR]

This paper presents an experimental study conducted on employing Active Learning strategies towards enhancing the attainment of learning outcomes of a course "Linear Control Systems" in the undergraduate (UG) Electrical and Electronics Engineering (EEE) programme. The existing UG programme curriculum is retrofitted into outcome-based curriculum. The Programme Outcomes (POs) for this programme are derived from the Graduate Attributes of Washington Accord (WA). In order to meet the POs, Course Learning Outcomes (CLOs) are written predominantly in the Cognitive (Revised Bloom's taxonomy) and Psychomotor (Dave's taxonomy) domains for lecture-based courses and laboratory courses, respectively and very few are written in Affective (Bloom's taxonomy) domain. In this paper, four CLOs (i) Discuss the various classification of control systems (K2), (ii) Define the time and frequency response of the system (K2), (iii) Derive the overall transfer function of the given Block diagram of a system (K3) and (iv) Derive the overall transfer function of a Signal Flow Graph (SFG) of a system (K3) are considered to measure the effectiveness of active learning methods in attaining the outcome. Active learning methods such as Jigsaw, Think-pair-share and Peer instruction, and traditional lecture-based methods were employed. In addition, ClassComm software with student interactive devices was used to measure the students' learning in the classroom and subsequently the attainment of CLOs. This study compares the effectiveness of active learning methods with the traditional lecture-based instruction method in relation to the attainment of CLOs. The overall analysis results reveal that both instructors and students received the active learning methods very well, and they are very effective in enhancing the attainment of learning outcomes. However, instructors are of the view that the duration of the contact period should be at least two hours. [ABSTRACT FROM AUTHOR]

This paper proposes a two-level control strategy based on a super-twisting sliding-mode algorithm (STA) to optimally allocate power imbalances in shipboard microgrids (SMGs) while achieving frequency regulation. The strategy employs an STA observer to estimate the unknown power load demand imbalances in finite time. This estimate is then passed to an online high-level optimal control framework to periodically determine the optimal sequence of power reference values for each energy storage device (ESS), minimising the operational cost of the SMG. The online optimised power reference values are interpolated and passed to the low-level STA control strategy to control the output power of each ESS. The efficacy of the proposed methods is demonstrated through numerical simulations conducted on a prototypical model of an SMG equipped with two ESSs, namely batteries and fuel cells with associated hydrogen storage. [ABSTRACT FROM AUTHOR]

Although smart home has received wide attention in recent years, numerous scholars focus more on energy optimization strategy than energy dispatch hardware device (named energy router). Meanwhile, this energy router should have several features, i.e., high renewable energy utilization, energy multi-port and low volume. Thus, this paper designs a nine-port energy router regarding smart home and proposes a multimode hierarchical management strategy for this energy router. First, for the multi-port demand of wind, solar, storage and utilization, this paper presents a nine-port energy router to improve the renewable energy consumption and power supply flexibility. In addition, to reduce the volume of the energy router, a non-isolated AC/DC hybrid topology is constructed through embedding the integrated power electronic converters, which achieves the miniaturization of the energy router. In order to improve the renewable energy utilization rate, the decentralized module control is proposed for the components of energy router to provide the voltage and frequency support for system, and realizes the power sharing of distributed generations (DGs). Furthermore, the power exchange control with three-mode switching is proposed to guarantee the global energy flow balance under complex conditions. Eventually, the feasibility of the energy router is verified by the simulation and experiments. [ABSTRACT FROM AUTHOR]

This paper proposes a trajectory optimization approach for high-speed trains to reduce traction energy consumption and increase riding comfort. Besides, the proposed approach can also achieve energy-saving effects by optimizing the operation time between stations. First, an optimization model is developed by defining the objective function as a trade-off function of the traction energy consumption and riding comfort. In addition to constraints in the classic optimal train control model, three new factors–the discrete throttle settings, neutral zones, and sectionalized tunnel resistance–are considered. Then, the model is discretized and turned into a multi-step decision optimization problem. All the nonlinear constraints are approximated using piecewise affine (PWA) functions, and the trajectory optimization problem is turned into a mixed integer linear programming (MILP) problem which can be solved by existing solvers CPLEX and YALMIP. Finally, some case studies with real-world data sets are conducted to present the effectiveness of the proposed approach. The simulation results are compared with the practical running data of trains, which shows that the proposed model and the optimization approach save energy and improve the riding comfort. [ABSTRACT FROM AUTHOR]

Microgrid's voltage regulation is of particular importance during both grid-connected and islanded modes of operation. Especially, during the islanded mode, when the support from the upstream grid is lost, stable voltage regulation is vital for the reliable operation of critical loads. This paper proposes a robust and data-driven control approach for secondary voltage control of AC microgrids in the presence of uncertainties. To this end, unfalsified adaptive control (UAC) is utilized to select the best stabilizing controller from a set of pre-designed controllers with the minimum knowledge required from the microgrid. Two microgrid test systems are simulated in MATLAB to verify the effectiveness of the proposed method under different scenarios like load change and communication link failure. [ABSTRACT FROM AUTHOR]

Background: Petri nets are a formal specification technique for modelling of control processes and modern flexible manufacturing systems. Interpreted Petri nets take into account input and output signals, allowing to apply them in any control system or even in control part of a cyber-physical system. Due to the fact that Petri nets are not used in the industrial practice, the students sometimes lack motivation to learn them. Contributions: In the paper we propose how to help students learn interpreted Petri nets with Minecraft (as a game-based learning). We show how interpreted Petri nets can be modelled in Minecraft and how they communicate with the surrounding environment via input and output signals to visualize control processes. The proposed approach has been validated experimentally among university students. Hypotheses: (1) Creating interpreted Petri net models with Minecraft helps to understand the basic principles; (2) Minecraft makes the course more attractive. Methodology: Students were divided into an experimental group (with game-based learning) and a control group (with traditional learning). The experimental group filled in a knowledge test twice (on the entry and on the exit) and a questionnaire. The control group filled in the same knowledge test at the end of the course. Findings: The observations confirm that the Minecraft-based teaching of interpreted Petri nets allows to gain better results in final tests, making at the same time the course more attractive and enjoyable. [ABSTRACT FROM AUTHOR]

This paper proposes an adaptive self-tuned current controller design for an LCL-filtered LC-tuned single-phase shunt hybrid active power filter (HAPF), which offers adaptability and flexibility of the control system to filter parameters changes. System modelling and online identification of parameters are presented. Then, a systematic self-tuned controller (STC) design is proposed to calculate the controller parameters and obtain the final control law adaptively. The exact HAPF parameters design is described and the overall closed-loop stability evaluation with considering digital control delay is discussed in this paper. To confirm the theoretical achievements, simulations and experimental tests on a real prototype system are performed. The results are in good accordance and demonstrate the effectiveness and superior performance of the suggested control technique. Furthermore, a comprehensive comparison between the proposed control technique and other controllers is provided to show the superiority of the proposed system from different aspects. [ABSTRACT FROM AUTHOR]

Compositional Optimization (CompOpt) was recently proposed for optimization of discrete-event systems of systems. A modular optimization model allows CompOpt to divide the optimization into separate sub-problems, mitigating the state space explosion problem. This paper presents the Modular Optimization Learner (MOL), a method that interacts with a simulation of a system to automatically learn these modular optimization models. MOL uses a modular learning that takes as input a hypothesis structure of the system and uses the provided structural information to split the acquired learning into a set of modules, and to prune parts of the search space. Experiments show that modular learning reduces the state space by many orders of magnitude compared to a monolithic learning, which enables learning of much larger systems. Furthermore, an integrated greedy search heuristic allows MOL to remove many sub-optimal paths in the individual modules, speeding up the subsequent optimization. Note to Practitioners—Automation systems are becoming increasingly large and complex and the automation of more and more advanced tasks often requires the coordination of multiple subsystems. Optimization can have a great impact on the efficiency of these systems in terms of cost and operation speed. Finding optimal solutions is, however, a difficult task. As the number of tasks and subsystems of the automation system increases, the search space of the optimization problems tends to grow exponentially. Compositional Optimization (CompOpt) is a method specifically designed for the optimization of large-scale automation systems. A challenge with the application of CompOpt is that it takes as input a specific type of optimization model that divides the system into subsystems; like machines, vehicles, etc. Formulating these models requires a high level of expertise and system knowledge. This paper addresses this challenge with an algorithm that learns these models from a simulation of the system. The simulation can be implemented in any software as long as a suitable interface exists or can be constructed. To divide the learning into subsystems, the algorithm uses an initial plant structure hypothesis (PSH). This can be viewed as a meta-model that includes known structural information, such as the number of subsystems and which actions that affect each subsystem. The more structural information that is added to PSH, the more efficient the learning and subsequent optimization will be. The purpose of this is to reduce the level of expertise and system knowledge needed in the application of optimization, to simplify the transition to Industry 4.0. [ABSTRACT FROM AUTHOR]

The field of human-computer interaction has progressed very rapidly over the last few decades, creating another area of research - human-robot interaction (HRI). This paper describes the implementation of HRI using developed Lithuanian language synthesis and recognition tools. Lithuanian language has a very small population of native speakers, which makes training the synthesizers and automated speech recognition tools very complex. Different Lithuanian language dialects and non-native speakers also experience severely deteriorated language recognition performance. There are still many issues concerning the lower level of the HRI field. The current automated Lithuanian speech recognition does not take into account the speaker's emotions. The issues of HRI are being considered. The task of developing HRI control system is discussed. HRI with the NAO V6 robot, a control system for verbal interaction, is discussed. The contribution of our research is related to the application of HRI to the pedagogical problem of teaching and motivating school children in the classroom. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the controllability conditions of linear control systems involving distinct local fractional derivatives. Sufficient conditions for controllability using Kalman rank conditions and the Gramian matrix are presented. We show that the controllability of the local fractional system can be determined by the invertibility of the Gramian matrix and the full rank of the Kalman matrix. We also show that the local fractional system involving distinct orders is controllable if and only if the Gramian matrix is invertible. Illustrative examples and an application are provided to demonstrate the validity of the theoretical findings. [ABSTRACT FROM AUTHOR]

The on-ground validation of control systems designed for manipulators working in orbit is very difficult due to the necessity of simulating the microgravity environment on Earth. In this paper, we present the possibilities of utilising the KUKA KUBE test-bed with industrial robots to experimentally verify space systems using hardware-in-the-loop tests. The fixed-base KUKA industrial robot is operated in gravitational environment, while the space system model plant is solved in real time parallel to on-ground experiment. The test-bed measurements are the input of the model plant, and the output of the model is treated as an input for the industrial robot actuation. In the performed experiment, the control system based on the Dynamic Jacobian is validated. The desired point that is reached by the manipulator's end-effector is constant in the simulated environment and moving with respect to the test-bed frame. The position of the space manipulator's end-effector is calculated by evaluating dynamics of the satellite in real-time model. The results show that the control system applied to the KUKA robot works correctly. The measurements from the torque sensors mounted in KUKA robot's joints are in accordance with the simulation results. This fact enhances the possibilities of gravity compensation, thus simulating microgravity environment on the test-bed. [ABSTRACT FROM AUTHOR]

This paper presents a method for controlling the voltage of inverter-based Microgrids by proposing a new scale-free distributed cooperative controller. The main contribution of this paper is that the proposed distributed cooperative controller is scale-free where is independent of any information about the communication system and the number of distributed generators, as such it works for any Microgrids with any size. Moreover, the communication network is modeled by a general time-varying graph which enhances the resilience of the proposed protocol against communication link failure, data packet loss, and arbitrarily fast plug and play operation in the presence of arbitrarily finite communication delays as the protocol does not require the knowledge of the upper bound on the delay. The stability analysis of the proposed protocol is provided. The proposed method is simulated on the CIGRE medium voltage Microgrid test system. The simulation results demonstrate the feasibility of the proposed scale-free distributed nonlinear protocol for regulating voltage of Microgrids in the presence of communication failures, data packet loss, noise, and degradation. [ABSTRACT FROM AUTHOR]

This paper proposes a direct damping feedback control method against power system oscillations under small or large disturbances. For a targeted oscillation mode, this control method continuously minimizes the difference between the real-time estimated damping ratio and a desired value by changing power outputs of selected inverter-based resources. The method adopts a proportional-integral controller whose parameters are tuned using a nonlinear single-input-single-output model on damping estimation and control with a single-oscillator equivalent regarding the targeted power system mode. Also, optimization of these controller parameters considers both robustness and time performance in damping control. The paper also proposes utilizing a “zero-th order” parametric resonance phenomenon to simplify the controller design. Tests on a small power system and a 140-bus 48-machine Northeast Power Coordinating Council system validate the effectiveness of the proposed damping controller utilizing battery-based energy storage systems. [ABSTRACT FROM AUTHOR]

Uncertain cut-in maneuver of vehicles from adjacent lanes makes it difficult for vehicle's automatic speed control strategy to make judgments and effective control decisions. In this paper, an intelligent speed control strategy for uncertain cut-in scenarios is established based on a basic autonomous driving system. This strategy judges cut-in maneuver from surrounding vehicles and outputs adaptive control action under current environment according to Q value of state-action pair based on a Q network. In addition, according to the analysis of cut-in scenarios, the Q network is trained based on a novel reinforcement learning method named as experience screening deep Q-learning network (ES-DQN). The proposed ES-DQN is an extension of double deep Q-learning network (DDQN) algorithm, and includes two parts: experience screening and policy learning. Based on the experience screened from the experience screening part, the proposed learning method can train an intelligent speed control strategy which has stronger adaptability and control effect in uncertain cut-in scenarios. According to simulation results, the proposed intelligent speed control strategy trained by ES-DQN has better performance under uncertain cut-in scenarios than DDQN method and traditional ACC strategy. Meanwhile, by adjusting weight value in reward function, the system can realize different control target. [ABSTRACT FROM AUTHOR]

Nowadays, energy generation systems that include renewable energies, substations, distribution, transmission, control, measurement, and storage applications, among others, and are interrelated are known as Smart Grids. All these techniques and technologies involve extensive research and development, which allows for the solving of key aspects, such as control, diagnosis, and fault recovery, as well as communication systems focused directly on the operation of the electrical networks. Due to the relevance of knowledge concerning developments in these areas of Smart Grids, this paper presents a review of the research related to the control systems applied to Smart Grids and Micro Grids, both in supply and demand. Likewise, some control models relate to different processes, with a special focus on techniques related to Petri nets. The paper shows, among other outcomes, the advances in the control of smart grids, the types of generation and their influence on the design of transmission lines, integrated circuits applied based on sensors, communication technologies, and automation schemes in all levels of the electrical network. Finally, patents from 1950 to 2019 related to Smart Grid in energy systems are traced and presented. [ABSTRACT FROM AUTHOR]

Material flow analysis (MFA) is the main methodology to assess material flow circularity. It is essentially a data-analysis-based approach whose physical foundations consist of conservation of mass. To improve both the accuracy and the repeatability of MFA models, in this paper we leverage compartmental dynamical thermodynamics merged with graph theory and control theory. The key idea is that the thermodynamic compartments and their connections can be added, removed or modified as needed to achieve a circular material flow. Thus, our methodology consists of designing thermodynamical material networks (TMNs). We also provide a physics-based definition of circularity and implement a nonlinear compartmental control, which has been possible since TMNs are highly dynamic models based on differential calculus (i.e. ordinary differential equations) rather than on arithmetic as is typical for MFA models. As we envision scalable and repeatable designs of TMNs, we made publicly available the paper source code.1 [ABSTRACT FROM AUTHOR]

This paper presents a controller design technique for cascade control systems based on the grey wolf optimization (GWO) algorithm. In the proposed control scheme, the proportional-integral-proportional-derivative (PI-PD) controller structure is used to control both the open-loop unstable process in the primary loop and the stable process in the secondary loop. To determine the optimal controller parameters, a new optimization algorithm is used in which the Euclidean distance function is used as a multi-objective function. A symmetry property of the Euclidean distance function is that its distance does not depend on the starting point and destination. The multi-objective function is designed according to system time response specifications such as settling time, overshoot, and steady-state error. Thus, the optimization algorithm allows the simultaneous determination of all controller parameters according to the desired output response. Three simulation studies are presented in the paper and the results are compared with studies using various methods based on internal model control, a modified Smith predictor, and a genetic algorithm. The simulation results reveal that the proposed method improves the performances of the systems in the control of cascade processes. [ABSTRACT FROM AUTHOR]