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2024 International Conference on Machine Learning, Pattern Recognition and automation engineering, MLPRAE 2024

作者： Rao, Jinjun Wang, Cong Liu, Mei Lei, Jingtao Giernacki, Wojciech Department of Precision Mechanical Engineering School of Mechatronic Engineering and Automation Shanghai University China Institute of Robotics and Machine Intelligence Faculty of Automatic Control Robotics and Electrical Engineering Poznan University of Technology Poland

ISBN: (纸本)9798400709876

Aiming at the problem that the combination of self-play (SP) and deep reinforcement learning (DRL) only involves two-party games and the policy learning of each party is limited, a multi-party asymmetric self-play algorithm (MASP) is proposed. Firstly, by improving the ELO scoring system, the ELO scoring takes into account each party of the unmanned surface vehicle (USV) clusters, and the imbalance of the number of USV clusters, so that the frequency of exchanging strategies of all USV clusters in the confrontation process is equal. Secondly, it ensures that USV clusters have a balanced combat ability, and at the same time ensures that the combat ability of all parties is strong and weak and the gap is not too wide. In addition, the parameters are dynamically set to reduce the update frequency of the policy of the stronger party. The experimental results show that the MASP can make the USV clusters learn more effective policies, have a shorter game time, and obtain higher rewards and ELO scores in the simple 2v2 adversarial game scenario and the three-party game scenario of a warship escort mission. © 2024 ACM.

关键词： Deep reinforcement learning

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Optimal control Synthesis of Markov Decision Processes for Efficiency with Surveillance Tasks 63

Optimal Control Synthesis of Markov Decision Processes for E...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Chen, Yu Yin, Xuanyuan Ye, Hao Li, Shaoyuan Yin, Xiang Shanghai Jiao Tong University Department of Automation and Key Laboratory of System Control and Information Processing Shanghai200240 China Nanyang Technological University School of Chemistry Chemical Engineering and Biotechnology Singapore Tsinghua University Department of Automation China

ISBN: (纸本)9798350316339

We investigate the problem of optimal control synthesis for Markov Decision Processes (MDPs), addressing both qualitative and quantitative objectives. Specifically, we require the system to fulfill a qualitative surveillance task in the sense that a specific region of interest can be visited infinitely often with probability one. Furthermore, to quantify the performance of the system, we consider the concept of efficiency, which is defined as the ratio between rewards and costs. This measure is more general than the standard long-run average reward metric as it aims to maximize the reward obtained per unit cost. Our objective is to synthesize a control policy that ensures the surveillance task while maximizes the efficiency. We provide an effective approach to synthesize a stationary control policy achieving -optimality by integrating state classifications of MDPs and perturbation analysis in a novel manner. Our results generalize existing works on efficiency-optimal control synthesis for MDP by incorporating qualitative surveillance tasks. © 2024 IEEE.

关键词： Markov processes

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Safety-Critical Randomized Event-Triggered Learning of Gaussian Process With Applications to Data-Driven Predictive control

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IEEE Transactions on Automatic control 2024年第6期70卷 3920-3935页

作者： Zheng, Kaikai Shi, Dawei Shi, Yang Beijing Institute of Technology MIIT Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing100081 China University of Victoria Department of Mechanical Engineering Faculty of Engineering VictoriaBCV8N 3P6 Canada

Safety and data efficiency are important concerns in data-driven control, especially for nonlinear systems with unknown dynamics and subject to disturbances. In this work, we consider a class of control-affine nonlinear systems with partially unknown dynamics and aim to introduce an event-triggered learning-based control approach with guaranteed safety and improved data utilization efficiency. Specifically, a randomized learning approach is employed to evaluate the safety of state trajectories by defining and estimating its confidence interval, with data from a multisample of randomly generated state trajectories. Using the proposed randomized learning algorithm, a nominal trajectory with a high probability safety guarantee is designed, thus ensuring the disturbed system states to remain within a pre-specified range around the nominal trajectory with a sufficiently high probability. Through removing irrelevant data, a local prediction model around the nominal trajectory is learned with satisfactory precision, and is updated online using an event-triggered learning strategy. Based on the learned model, an efficient data-driven predictive controller is designed to force the system states to evolve within the vicinity of the designed safety nominal trajectory. The effectiveness of the proposed event-triggered learning and data-driven control approaches is validated through comprehensive simulation studies. © 1963-2012 IEEE.

关键词： Predictive control systems

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Augmented Reality Application in Digital automation Laboratory

Augmented Reality Application in Digital Automation Laborato...

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2023 International Symposium on Electrical and Electronics engineering, ISEE 2023

作者： Khang, Nguyen Trong Tran, Ngoc-Huy Phan, Nguyen-Phuc-Quoc VNU-HCM Faculty of Electrical & Electronics Engineering Department of Control & Automation Ho Chi Minh City Viet Nam

ISBN: (纸本)9798350315486

Traditional automation engineering education using physical training equipment while quite effective, suffers from several limitations in the case there is no equipment access, which can be due to many reasons such as social distancing or financial difficulties. This article proposes a solution to overcome these limitations with the combinational use of mixed reality (MR), which helps the student get used to training equipment they have no access to, and augmented reality (AR), which brings intuitive visualization into real equipment practice. © 2023 IEEE.

关键词： Augmented reality

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Implementation of Neural Networks in Real-Time Swarm Robotics Applications

Implementation of Neural Networks in Real-Time Swarm Robotic...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Yazici, Emre Temeltas, Hakan Istanbul Technical University Department of Control and Automation Engineering Istanbul Turkey Software Development Department Niso Software Technologies Izmir Turkey

ISBN: (纸本)9798350337020

Over the last few decades, significant attention has been devoted to the coordinated motion of swarm systems, which involve multiple autonomous robots. These systems have a range of potential applications, such as creating formations with physical or non-physical bonds, navigating to desired locations while maintaining formation, and preventing collisions. To achieve coordinated motion, recent researches have focused on the potential function method. We previously validated novel potential functions and developed a sliding-mode speed controllers to promote collective behavior. In this study, we implement an accepted neural network with one hidden and one output layer to update potential function weights with decentralized manner in real-time, thereby enhancing swarm behavior for formation creation and maintenance. The effectiveness of this approach is demonstrated through simulation and processor-in-the-loop tests using two different micro-controllers as well as measuring execution time of neural networks' models. By successfully validating neural networks implementation in this context, our study extends the current understanding of swarm behavior and opens up new possibilities for real-time swarm applications. © 2023 IEEE.

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Distributed Resilient control of Cyber-Physical Multi-Agent Systems Under Attacks

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 241-250页

作者： Xu, Yong Zhang, Wenyu Zhang, Yifang Wu, Zheng-Guang Beijing Institute of Technology School of Automation Beijing100081 China The University of Hong Kong Department of Mechanical Engineering Hong Kong Hong Kong Zhejiang University Institute of Cyber-Systems and Control Hangzhou310027 China

This paper delves into the distributed resilient state estimation-based secure control in multi-agent systems under false-data injection attacks. Firstly, we propose a novel adaptive distributed output observer approach that is based on resilient outputs. The key feature of our proposed observer is its sole dependence on the coefficients of the minimal polynomial of the leader's system matrix, rather than on individual entries of the leader's system. Subsequently, to identify the target attacker, we assign both trust and confidence values to each agent and its neighbors, thereby enabling attack detection and localization. Furthermore, we propose a distributed finite-time resilient control strategy, grounded in trust and confidence, to safeguard against attackers while ensuring secure output estimation and maintaining a bounded upper limit for finite-time convergence. Additionally, we introduce a piecewise function-based approximation method to achieve a less conservative upper bound for the convergence time, compared to traditional methods. Finally, we provide a numerical example to demonstrate the effectiveness of our proposed theoretical analysis. © 2023 IEEE.

关键词： Cyber attacks

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Distributed Privacy-Preserving State-of-Charge Balance control for Multiple Energy Storage Systems in Smart Grid

Distributed Privacy-Preserving State-of-Charge Balance Contr...

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2023 China automation Congress, CAC 2023

作者： Liu, Weijia Guo, Fanghong Xing, Lantao Su, Hongye Wang, Lei College of Control Science and Engineering Zhejiang University Hangzhou China Zhejiang University of Technology Department of Automation Hangzhou China School of Control Science and Engineering Shandong University Jinan China

ISBN: (纸本)9798350303759

This paper studies the distributed state of charge (SoC) balance control strategy in battery energy storage systems (BESSs), satisfying privacy-preserving requirement of BESSs privacy-sensitive data, i.e., configurations and SoC, which may be transmitted among BESSs. Considering each BESS communicates with its neighboring BESS, we use a distributed dynamic average consensus algorithm to do parameter estimations. Using obtained parameters, we set proper power output of each BESS and maintains overall maximum capability. Different from common algorithms, this algorithm not only converges to the exact average of reference signals and thus help set control power outputs, but also ensures the desired differential privacy by adding zero mean Laplace noises, thus protecting privacy-sensitive data from possible adversaries. Effectiveness of the presented approach is demonstrated by simulations. © 2023 IEEE.

关键词： Charging (batteries)

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Two-Degree-of-Freedom Feedback Loop Factorization controller for Oscillating Plant with Uncertain Time Delay and Astatism Using Structured Singular Value

Two-Degree-of-Freedom Feedback Loop Factorization Controller...

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2024 International Conference on control, automation and Diagnosis, ICCAD 2024

作者： Dlapa, Marek Tomas Bata University In Zlin Faculty of Applied Informatics Department of Automation and Control Engineering Nad Stranemi 4511 Zlin760 05 Czech Republic

ISBN: (纸本)9798350361025

Application of Robust control Toolbox for Time Delay Systems implemented in the Matlab system to the oscillating plant with uncertain time delay and astatism using the D-K iteration and algebraic approach. The algebraic approach combines the structured singular value, algebraic theory and algorithm of global optimization solving remaining issues in structured singular value framework. The algorithm for global optimization can be alternated with direct search methods such as Nelder-Mead simplex method giving solutions for problems with one local extreme. As a global optimization method, Differential Migration is used proving to be reliable in solving this type of problems. The D-K iteration represents a standard method in the structured singular value theory. The results obtained from the D-K iteration are compared with the algebraic approach. © 2024 IEEE.

关键词： Global optimization

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Design of Supervisory Model Predictive control for HVAC Systems and Two Zones with Consideration of Energy Efficiency and Thermal Comfort 23

Design of Supervisory Model Predictive Control for HVAC Syst...

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23rd International Conference on control, automation and Systems, ICCAS 2023

作者： Srisurapanon, Panithan Banjerdpongchai, David Chulalongkorn University Center of Excellence in Intelligent Control Automation for Process Systems Faculty of Engineering Department of Electrical Engineering Pathumwan Bangkok10330 Thailand

ISBN: (纸本)9788993215274

This paper presents the design of supervisory model predictive control for HVAC systems and two zones. The supervisory control (SC) is designed to find the optimal reference temperature of each zone. The design criteria consist of the total operating cost (TOC) and the thermal comfort cost (TCC). Then, the model predictive control (MPC) is designed to track the optimal reference signals. When SC and MPC are designed for two zones simultaneously, they are referred to centralized SC and centralized MPC. On the other hand, when SC and MPC are designed for two zones separately, they referred to decentralized SC and decentralized MPC. We then compare the results between the decentralized control and the centralized control. The trade-off curve shows that the performance of centralized control is better than that of decentralized control. In particular, centralized MPC improves the performance in tracking reference signals. Furthermore, TOC of centralized control is lower than that of decentralized control. It can be concluded that the centralized control is appropriate for two zones in terms of the tracking of reference and the electrical charge. © 2023 ICROS.

关键词： Model predictive control

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Editorial:special issue on frontiers of control and automation,dedicated to *** *** 60th birthday

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control Theory and Technology 2023年第3期21卷 263-264页

作者： Jie Chen Jie Huang Lihua Xie Department of Control Science and Engineering Tongji UniversityShanghaiChina Department of Mechanical and Automation Engineering The Chinese University of Hong KongHong KongChina Center for Advanced Robotics Technology Innovation School of Electrical and Electronic EngineeringNanyang Technological UniversitySingaporeSingapore

It is our great pleasure and honor to organize this special issue"Frontiers of control and automation"in honor of the 60th birthday of our long-time colleague and friend Professor Ben *** Chen obtained his *** in mathemat-ics and computer science in 1983 from Xiamen University,China;*** in electrical engineering in 1988 from Gonzaga University,Spokane,Washington,USA;and *** in electrical and computer engineering in 1991 from Washington State University,Pullman,Washington,USA.

关键词： computer Xiamen frontier

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