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Time-varying formation tracking control of high-order multi-agent systems with multiple leaders and multiplicative noise

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Science China(Information Sciences) 2024年第12期67卷 191-207页

作者： Ruru JIA Xiaofeng ZONG Qing WANG College of Artificial Intelligence Nankai University School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Automation Science and Electrical Engineering Beihang University

This work discusses the time-varying formation(TVF) tracking control problem of high-order multi-agent systems(MASs) with multiple leaders and multiplicative measurement noise. With the help of Lyapunov function tools and stochastic analysis methods, the TVF tracking protocol with multiple leaders and multiplicative noise is developed based on the relative state measurements, where followers are driven to realize the target TVF while tracking the convex combination formed by multiple leaders. Then, the TVF tracking problem is converted into the mean square asymptotic stability problem of a stochastic differential equation(SDE); sufficient conditions related to the control gains are given by stabilizing the corresponding stochastic system. Moreover, a TVF tracking algorithm is presented to outline the steps of protocol ***, the theoretical results are illustrated in terms of simulation examples.

关键词： multi-agent systems multiple leaders multiplicative measurement noise stochastic differential equation formation tracking control

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Constrained Networked Predictive control for Nonlinear Systems Using a High-Order Fully Actuated System Approach

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IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 478-480页

作者： Yi Huang Guo-Ping Liu Yi Yu Wenshan Hu the School of Electrical Engineering and Automation Wuhan University IEEE the Center for Control Science and Technology Southern University of Science and Technology the Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University

Dear Editor,In this letter, a constrained networked predictive control strategy is proposed for the optimal control problem of complex nonlinear highorder fully actuated (HOFA) systems with noises. The method can effectively deal with nonlinearities, constraints, and noises in the system, optimize the performance metric, and present an upper bound on the stable output of the system.

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Recent advances of few-shot learning methods and applications

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Science China(Technological Sciences) 2023年第4期66卷 920-944页

作者： WANG JianYuan LIU KeXin ZHANG YuCheng LENG Biao LU JinHu School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China

The rapid development of deep learning provides great convenience for production and ***,the massive labels required for training models limits further ***-shot learning which can obtain a high-performance model by learning few samples in new tasks,providing a solution for many scenarios that lack *** paper summarizes few-shot learning algorithms in recent years and proposes a ***,we introduce the few-shot learning task and its ***,according to different implementation strategies,few-shot learning methods in recent years are divided into five categories,including data augmentation-based methods,metric learning-based methods,parameter optimization-based methods,external memory-based methods,and other ***,We investigate the application of few-shot learning methods and summarize them from three directions,including computer vision,human-machine language interaction,and robot ***,we analyze the existing few-shot learning methods by comparing evaluation results on mini Image Net,and summarize the whole paper.

关键词： few-shot learning deep learning meta learning data augmentation parameter optimization

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Enhanced Tube-Based Event-Triggered Stochastic Model Predictive control With Additive Uncertainties

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 596-605页

作者： Chenxi Gu Xinli Wang Kang Li Xiaohong Yin Shaoyuan Li Lei Wang the School of Control Science and Engineering Shandong University IEEE the School of Electronic and Electrical Engineering University of Leeds the Qingdao University of Science and Technology the Department of Automation Shanghai Jiao Tong University the Key Laboratory of System Control and Information Processing Ministry of Education of China

This paper proposes an event-triggered stochastic model predictive control for discrete-time linear time-invariant(LTI) systems under additive stochastic disturbances. It first constructs a probabilistic invariant set and a probabilistic reachable set based on the priori knowledge of system *** with enhanced robust tubes, the chance constraints are then formulated into a deterministic form. To alleviate the online computational burden, a novel event-triggered stochastic model predictive control is developed, where the triggering condition is designed based on the past and future optimal trajectory tracking errors in order to achieve a good trade-off between system resource utilization and control performance. Two triggering parameters σ and γ are used to adjust the frequency of solving the optimization problem. The probabilistic feasibility and stability of the system under the event-triggered mechanism are also examined. Finally, numerical studies on the control of a heating, ventilation, and air conditioning(HVAC) system confirm the efficacy of the proposed control.

关键词： Event-triggered mechanism heating, ventilation, and air conditioning (HVAC) control probabilistic reachable set stochastic model predictive control

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Bidding Strategy for Hybrid PV-BESS Plants via Knowledge-data-complementary Learning

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Journal of Modern Power Systems and Clean Energy 2025年第1期13卷 365-378页

作者： Xiaoge Huang Zhenhuan Ding Zhao Liu Tianqiao Zhao Pei Zhang Xiaojun Wang Department of Electrical and Computer Engineering State University of New York at BinghamtonBinghamtonUSA School of Artificial Intelligence Anhui UniversityHefei 230601China School of Electrical Engineering Beijing Jiaotong UniversityBeijing 100044China Brookhaven National Lab UptonUSA School of Electrical Automation and Information Engineering Tianjin UniversityTianjin 300072China

The hybrid photovoltaic(PV)-battery energy storage system(BESS)plant(HPP)can gain revenue by performing energy arbitrage in low-carbon power ***,multiple operational uncertainties challenge the profitability and reliability of HPP in the day-ahead *** paper proposes two coherent models to address these ***,a knowledge-driven penalty-based bidding(PBB)model for HPP is established,considering forecast errors of PV generation,market prices,and under-generation ***,a data-driven dynamic error quantification(DEQ)model is used to capture the variational pattern of the distribution of forecast *** role of the DEQ model is to guide the knowledgedriven bidding ***,the DEQ model aims at the statistical optimum,but the knowledge-driven PBB model aims at the operational *** two models have independent optimizations based on misaligned *** address this,the knowledge-data-complementary learning(KDCL)framework is proposed to align data-driven performance with knowledge-driven objectives,thereby enhancing the overall performance of the bidding strategy.A tailored algorithm is proposed to solve the bidding *** proposed bidding strategy is validated by using data from the National Renewable Energy Laboratory(NREL)and the New York Independent System Operator(NYISO).

关键词： Hybrid power plant(HPP) photovoltaic(PV) battery energy storage system(BESS) optimization uncertainty bidding strategy error quantification knowledge-data-complementary learning

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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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On-Policy and Off-Policy Value Iteration Algorithms for Stochastic Zero-Sum Dynamic Games

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Journal of Systems Science & Complexity 2025年第1期38卷 421-435页

作者： GUO Liangyuan WANG Bing-Chang ZHANG Ji-Feng School of Control Science and Engineering Shandong UniversityJinan 250063China School of Automation and Electrical Engineering Zhongyuan University of TechnologyZhengzhou 450007China Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China

This paper considers the value iteration algorithms of stochastic zero-sum linear quadratic games with unkown ***-policy and off-policy learning algorithms are developed to solve the stochastic zero-sum games,where the system dynamics is not *** analyzing the value function iterations,the convergence of the model-based algorithm is *** equivalence of several types of value iteration algorithms is *** effectiveness of model-free algorithms is demonstrated by a numerical example.

关键词： Approximate dynamic programming on-policy off-policy stochastic zero-sum games valueiteration

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Nussbaum-based distributed containment control for nonlinear multi-agent systems with quantized inputs

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IEEE Transactions on control of Network Systems 2024年第2期12卷 1290-1299页

作者： Liu, Yang Zhang, Jiaming Seventh Research Division The Center for Information and Control School of Automation Science and Electrical Engineering Beijing100191 China

This article explores the distributed containment control problem for uncertain nonlinear multi-agent systems subject to external disturbances and input quantization, where the constant control gains and the upper bounds of the external disturbances are unknown. First, the reference generator is constructed for each follower agent to generate the virtual tracking signal, confining to the convex hull spanned by the leaders. Meanwhile, the unmeasurable state variable of each follower is estimated by adopting the K-filters based on the available output/input signals and known system function matrices. Then, a distributed adaptive output feedback controller with only one updating parameter is designed for each follower by introducing a logarithmic quantization to quantize the control inputs under the framework of prescribed performance control. The lack of priori knowledge for the control gain and the quantization gain is counteracted effectively by employing the Nussbaum function method in the adaptive backstepping design process. It is proved that the outputs of followers can enter into the convex hull of multiple leaders and the relevant tracking errors satisfy the prescribed performance index. Finally, the validity of the proposed schemes is illustrated through simulation studies on robotic systems. © 2014 IEEE.

关键词： Backstepping

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Constrained Moving Path Following control for UAV With Robust control Barrier Function

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IEEE/CAA Journal of Automatica Sinica 2023年第7期10卷 1557-1570页

作者： Zewei Zheng Jiazhe Li Zhiyuan Guan Zongyu Zuo Seventh Research Division School of Automation Science and Electrical EngineeringBeihang UniversityBeijing 100191 Beijing Institute of Control Engineering Beijing 100094China

This paper studies the moving path following(MPF)problem for fixed-wing unmanned aerial vehicle(UAV)under output constraints and wind *** vehicle is required to converge to a reference path moving with respect to the inertial frame,while the path following error is not expected to violate the predefined *** from existing moving path following guidance laws,the proposed method removes complex geometric transformation by formulating the moving path following problem into a second-order time-varying control problem.A nominal moving path following guidance law is designed with disturbances and their derivatives estimated by high-order disturbance *** guarantee that the path following error will not exceed the prescribed bounds,a robust control barrier function is developed and incorporated into controller design with quadratic program based *** proposed method does not require the initial position of the UAV to be within predefined *** the safety margin concept makes error-constraint be respected even if in a noisy *** proposed guidance law is validated through numerical simulations of shipboard landing and hardware-in-theloop(HIL)experiments.

关键词： Moving path following(MPF) robust control barrier function safety margin shipboard landing unmanned aerial vehicle(UAV)

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Predefined-time distributed multiobjective optimization for network resource allocation

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Science China(Information Sciences) 2023年第7期66卷 58-72页

作者： Kunpeng ZHANG Lei XU Xinlei YI Zhengtao DING Karl H.JOHANSSON Tianyou CHAI Tao YANG State Key Laboratory of Synthetical Automation for Process Industries Northeastern University School of Electrical Engineering and Computer Science KTH Royal Institute of Technology School of Electrical and Electronic Engineering The University of Manchester

We consider the multiobjective optimization problem for the resource allocation of the multiagent network, where each agent contains multiple conflicting local objective functions. The goal is to find compromise solutions minimizing all local objective functions subject to resource constraints as much as possible, i.e., the Pareto optimums. To this end, we first reformulate the multiobjective optimization problem into one single-objective distributed optimization problem by using the weighted Lppreference index,where the weighting factors of all local objective functions are obtained from the optimization procedure so that the optimizer of the latter is the desired Pareto optimum of the former. Next, we propose novel predefined-time algorithms to solve the reformulated problem by time-based generators. We show that the reformulated problem is solved within a predefined time if the local objective functions are strongly convex and smooth. Moreover, the settling time can be arbitrarily preset since it does not depend on the initial values and designed parameters. Finally, numerical simulations are presented to illustrate the effectiveness of the proposed algorithms.

关键词： distributed optimization multiobjective optimization predefined-time algorithms time-based generators weighted ${L_p}$ preference index

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