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A Recursive System Identification With Non-Uniform Temporal Feedback Under Coprime Collaborative Sensing

ASME Letters in Dynamic Systems and control 2023年第2期3卷 021010页

作者： Ouyang, Jinhua Chen, Xu Mechatronics Automation and Control Systems Laboratory Department of Mechanical Engineering University of Washington SeattleWA98195 United States Mechatronics Automation and Control Systems Laboratory Department of Mechanical Engineering University of Washington SeattleWA98195 United States

We present a system identification method based on recursive least-squares (RLS) and coprime collaborative sensing, which can recover system dynamics from non-uniform temporal data. Focusing on systems with fast input sampling and slow output sampling, we use a polynomial transformation to reparameterize the system model and create an auxiliary model that can be identified from the non-uniform data. We show the identifiability of the auxiliary model using a Diophantine equation approach. Numerical examples demonstrate successful system reconstruction and the ability to capture fast system response with limited temporal feedback. Copyright © 2023 by ASME.

关键词： Feedback

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Reinforcement learning-based unknown reference tracking control of HMASs with nonidentical communication delays

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Science China(Information Sciences) 2023年第7期66卷 46-57页

作者： Yong XU Zheng-Guang WU Wei-Wei CHE Deyuan MENG School of Automation Beijing Institute of Technology Institute of Cyber-Systems and Control Zhejiang University College of Mathematics and Computer Science Zhejiang Normal University Department of Automation Qingdao University School of Automation Science and Electrical Engineering Beihang University(BUAA)

This paper focuses on the optimal output synchronization control problem of heterogeneous multiagent systems(HMASs) subject to nonidentical communication delays by a reinforcement learning *** with existing studies assuming that the precise model of the leader is globally or distributively accessible to all or some of the followers, the leader's precise dynamical model is entirely inaccessible to all the followers in this paper. A data-based learning algorithm is first proposed to reconstruct the leader's unknown system matrix online. A distributed predictor subject to communication delays is further devised to estimate the leader's state, where interaction delays are allowed to be nonidentical. Then, a learning-based local controller, together with a discounted performance function, is projected to reach the optimal output synchronization. Bellman equations and game algebraic Riccati equations are constructed to learn the optimal solution by developing a model-based reinforcement learning(RL) algorithm online without solving regulator equations, which is followed by a model-free off-policy RL algorithm to relax the requirement of all agents' dynamics faced by the model-based RL algorithm. The optimal tracking control of HMASs subject to unknown leader dynamics and communication delays is shown to be solvable under the proposed RL algorithms. Finally, the effectiveness of theoretical analysis is verified by numerical simulations.

关键词： heterogeneous multiagent systems HMAS reinforcement learning RL optimal output synchronization communication delays

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An Optimal control-Based Distributed Reinforcement Learning Framework for A Class of Non-Convex Objective Functionals of the Multi-Agent Network

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IEEE/CAA Journal of Automatica Sinica 2023年第11期10卷 2081-2093页

作者： Zhe Chen Ning Li Department of Automation Shanghai Jiao Tong UniversityShanghai 200240 Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai 200240 Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240China Department of Automation Tsinghua UniversityBeijing 100084China IEEE

This paper studies a novel distributed optimization problem that aims to minimize the sum of the non-convex objective functionals of the multi-agent network under privacy protection, which means that the local objective of each agent is unknown to others. The above problem involves complexity simultaneously in the time and space aspects. Yet existing works about distributed optimization mainly consider privacy protection in the space aspect where the decision variable is a vector with finite dimensions. In contrast, when the time aspect is considered in this paper, the decision variable is a continuous function concerning time. Hence, the minimization of the overall functional belongs to the calculus of variations. Traditional works usually aim to seek the optimal decision function. Due to privacy protection and non-convexity, the Euler-Lagrange equation of the proposed problem is a complicated partial differential ***, we seek the optimal decision derivative function rather than the decision function. This manner can be regarded as seeking the control input for an optimal control problem, for which we propose a centralized reinforcement learning(RL) framework. In the space aspect, we further present a distributed reinforcement learning framework to deal with the impact of privacy protection. Finally, rigorous theoretical analysis and simulation validate the effectiveness of our framework.

关键词： Distributed optimization multi-agent optimal control reinforcement learning(RL)

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Proton Exchange Membrane Fuel Cell Thermal System Modeling and control 10

Proton Exchange Membrane Fuel Cell Thermal System Modeling a...

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10th International Conference on Electrical and Electronics engineering, ICEEE 2023

作者： Özdemir, Tuna Han Yasar, Claudia Fernanda Yildiz Technical University Avl Research and Engineering Department of Control and Automation Engineering Istanbul Turkey Yildiz Technical University Department of Control and Automation Engineering Istanbul Turkey

ISBN: (纸本)9798350304299

The urgent issue of global warming and its adverse effects, including wildfires, water scarcity, and the spread of diseases, calls for concerted efforts to mitigate emissions and curb climate change. Among the effective strategies is the reduction of carbon emissions, for which hydrogen-powered proton exchange membrane fuel cell systems are vital. These systems generate electricity through chemical reactions involving hydrogen, with only water and heat as byproducts, hence their environmentally friendly nature. Fuel cell efficiency is higher than conventional systems depending on the type and the operating temperature. Proper thermal control is essential for maintaining high efficiency. This research paper aims to examine standard models of proton exchange membrane fuel cell thermal systems and suggest an efficient control approach. The system dynamics are derived from fundamental models, and an adaptive PID controller is simulated with MATLAB/Simulink. The paper also presents a comparative analysis between traditional PID control and adaptive PID, an advanced control technique. © 2023 IEEE.

关键词： Three term control systems

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Distributed Optimization and Scaling Design for Solving Sylvester Equations

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Journal of Systems Science & Complexity 2024年第6期37卷 2487-2510页

作者： CHENG Songsong YU Xin ZENG Xianlin LIANG Shu HONG Yiguang Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and AutomationAnhui UniversityHefei 230601China Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of TechnologyBeijing 100081China Department of Control Science and Engineering Tongji UniversityShanghai 201804China Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghai 201210China

This paper develops distributed algorithms for solving Sylvester *** authors transform solving Sylvester equations into a distributed optimization problem,unifying all eight standard distributed matrix *** the authors propose a distributed algorithm to find the least squares solution and achieve an explicit linear convergence *** results are obtained by carefully choosing the step-size of the algorithm,which requires particular information of data and Laplacian *** avoid these centralized quantities,the authors further develop a distributed scaling technique by using local information *** a result,the proposed distributed algorithm along with the distributed scaling design yields a universal method for solving Sylvester equations over a multi-agent network with the constant step-size freely chosen from configurable ***,the authors provide three examples to illustrate the effectiveness of the proposed algorithms.

关键词： Distributed optimization least squares solution linear convergence rate step-size interval Sylvester equation

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A zeroth-order algorithm for distributed optimization with stochastic stripe observations

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Science China(Information Sciences) 2023年第9期66卷 297-298页

作者： Yinghui WANG Xianlin ZENG Wenxiao ZHAO Yiguang HONG School of Automation and Electrical Engineering University of Science and Technology Beijing Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Department of Control Science and Engineering Tongji University

Recent years have seen a rising interest in distributed optimization problems because of their widespread applications in power grids, multi-robot control, and regression *** the last few decades, many distributed algorithms have been developed for tackling distributed optimization problems. In these algorithms, agents over the network only have access to their own local functions and exchange information with their neighbors.

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Research on Attack Detection Method for V2V System Based on Multi-dimensional Taylor Series Expansion Framework

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IEEE Sensors Journal 2025年第11期25卷 19576-19591页

作者： Wang, Di Wen, Chenglin Liu, Weifeng Shaanxi University of Science and Technology School of Electrical and Control Engineering Xi’an710021 China Guangdong University of Petrochemical Technology Department of Automation Maoming525000 China

Strongly stealthy cyber-attacks in networked vehicle queue (V2V) network environments often manifest themselves as small changes to data, which makes it difficult for existing methods to effectively detect such attacks. Although the initial impact of these attacks is minimal, they are capable of significantly disrupting the system over time. To address this issue, this paper proposes a new data representation model for attack detection, aiming to overcome the limitations of existing detection methods. For subtle and persistent attack features, we present a multi-level data representation framework, which enables effective comparison of attack features at different scales by extracting features at various levels. Through hierarchical processing of features, these subtle changes are revealed at the micro level, thereby enhancing the system’s sensitivity to stealthy attacks. The experimental results show that the method in this paper can effectively detect the covert false data injection (FDI) attacks. Meanwhile, we compared the proposed method with the most recent competing methods, and the results demonstrated that our method has higher detection accuracy and stronger robustness compared with the existing methods (Figure presented). © 2001-2012 IEEE.

关键词： Cyber attacks

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A Feature Weighted Mixed Naive Bayes Model for Monitoring Anomalies in the Fan System of a Thermal Power Plant

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 719-727页

作者： Min Wang Li Sheng Donghua Zhou Maoyin Chen IEEE the Department of Automation Tsinghua University the College of Control Science and Engineering China University of Petroleum(East China) the College of Electrical Engineering and Automation Shandong University of Science and Technology

With the increasing intelligence and integration,a great number of two-valued variables(generally stored in the form of 0 or 1)often exist in large-scale industrial ***,these variables cannot be effectively handled by traditional monitoring methods such as linear discriminant analysis(LDA),principal component analysis(PCA)and partial least square(PLS)***,a mixed hidden naive Bayesian model(MHNBM)is developed for the first time to utilize both two-valued and continuous variables for abnormality *** the MHNBM is effective,it still has some shortcomings that need to be *** the MHNBM,the variables with greater correlation to other variables have greater weights,which can not guarantee greater weights are assigned to the more discriminating *** addition,the conditional P(x j|x j′,y=k)probability must be computed based on historical *** the training data is scarce,the conditional probability between continuous variables tends to be uniformly distributed,which affects the performance of *** a novel feature weighted mixed naive Bayes model(FWMNBM)is developed to overcome the above *** the FWMNBM,the variables that are more correlated to the class have greater weights,which makes the more discriminating variables contribute more to the *** the same time,FWMNBM does not have to calculate the conditional probability between variables,thus it is less restricted by the number of training data *** with the MHNBM,the FWMNBM has better performance,and its effectiveness is validated through numerical cases of a simulation example and a practical case of the Zhoushan thermal power plant(ZTPP),China.

关键词： Abnormality monitoring continuous variables feature weighted mixed naive Bayes model(FWMNBM) two-valued variables thermal power plant

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Design Software in Tasks of Algorithmization of Manipulation Robots in Assembly of Parts with a Shaft-Bushing Connection Method

Design Software in Tasks of Algorithmization of Manipulation...

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2024 International Russian Smart Industry Conference, SmartIndustryCon 2024

作者： Arkhipov, Maksim V. Malkarov, Artem Yu. Matrosova, Vladlena V. Moscow Polytechnic University Faculty of Mechanical Engineering Department of Automation and Control Moscow Russia

ISBN: (纸本)9798350395044

This article presents developments by its authors: the IndustrialKit library package and the RCWorkspace application based on it. The library allows developers of third-party applications to use the functionality of programming and modeling robotic technological complexes in their products. The object-oriented concept of the package provides opportunities for offline training of manipulation robots in a developed simulation environment, subsequent transfer and launch of programs on real robots, organization of feedback control and obtaining statistical data from industrial equipment - common models of robots and various equipment devices, which is achieved through a unified structure of program modules. The connection of external visual models of production system components - robots, tools and parts - is demonstrated. An example of performing the task of assembling parts with spline joints is also given. © 2024 IEEE.

关键词： Computer software

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Data-driven output regulation control for constrained linear systems

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Science China(Information Sciences) 2025年第3期68卷 338-353页

作者： Chaoyu XIA Yi DONG Chaoli WANG Shengyuan XU Shanghai Research Institute for Intelligent Autonomous Systems Tongji University College of Electronic and Information Engineering Shanghai Research Institute for Intelligent Autonomous SystemsTongji University Department of Control Science and Engineering School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and Technology Department of Automation Nanjing University of Science and Technology

This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking performance while satisfying the state and input constraints, even when system matrices are not available. We first establish a sufficient condition necessary for the existence of a solution pair to the regulator equation and propose a data-based approach to obtain the feedforward and feedback control gains for state feedback control using linear programming. Furthermore, we design a refined Luenberger observer to accurately estimate the system state, while keeping the estimation error within a predefined set. By combining output regulation theory, we develop an output feedback control strategy. The stability of the closed-loop system is rigorously proved to be asymptotically stable by further leveraging the concept of λ-contractive sets.

关键词： output regulation constrained system data-driven.

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