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Robust Optimization-Based Iterative Learning Control for Nonlinear Systems With Nonrepetitive Uncertainties

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IEEE/CAA Journal of Automatica Sinica 2021年第5期8卷 1001-1014页

作者： deyuan meng Jingyao Zhang the Seventh Research Division Beihang University(BUAA)Beijing 100191 the School of Automation Science and Electrical Engineering Beihang University(BUAA)Beijing 100191

This paper aims to solve the robust iterative learning control(ILC)problems for nonlinear time-varying systems in the presence of nonrepetitive uncertainties.A new optimization-based method is proposed to design and analyze adaptive ILC,for which robust convergence analysis via a contraction mapping approach is realized by leveraging properties of substochastic *** is shown that robust tracking tasks can be realized for optimization-based adaptive ILC,where the boundedness of system trajectories and estimated parameters can be ensured,regardless of unknown time-varying nonlinearities and nonrepetitive *** simulation tests,especially implemented for an injection molding process,demonstrate the effectiveness of our robust optimization-based ILC results.

关键词： Adaptive iterative learning control(ILC) nonlinear time-varying system robust convergence substochastic matrix

Disagreement and Antagonism in Signed Networks: A Survey

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1166-1187页

作者： Yuxin Wu deyuan meng Zheng-Guang Wu Seventh Research Division Beihang University(BUAA)and also with the School of Automation Science and Electrical EngineeringBeihang University(BUAA)Beijing 100191China National Laboratory of Industrial Control Techno-logy Institute of Cyber-Systems and ControlZhejiang UniversityHangzhou 310027China IEEE

Signed networks refer to a class of network systems including not only cooperative but also antagonistic interactions among *** to the existence of antagonistic interactions in signed networks,the agreement of nodes may not be established,instead of which disagreement behaviors generally *** paper reviews several different disagreement behaviors in signed networks under the single-integrator linear dynamics,where two classes of topologies,namely,the static topology and the dynamic topology,are *** the static signed networks with the adjacency weights as(time-varying)scalars,we investigate the convergence behaviors and the fluctuation behaviors with respect to fixed topologies and switching topologies,respectively,and give some brief introductions on the disagreement behaviors of general time-varying signed ***,several classes of behavior analysis approaches are also *** the dynamic signed networks with the adjacency weights as transfer functions or linear time-invariant systems,we show the specific descriptions and characteristics of them such that the disagreement behaviors can be obtained by resorting to the derived static signed ***,we give their applications to the behavior analysis of static signed networks in the presence of high-order dynamics or communication delays.

关键词： Antagonistic interaction disagreement behavior dynamic topology signed network static topology

Segment-wise learning control for trajectory tracking of robot manipulators under iteration-dependent periods

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Science China(Information Sciences) 2024年第3期67卷 155-166页

作者： Fan ZHANG deyuan meng Kaiquan CAI The Seventh Research Division Beihang University School of Automation Science and Electrical Engineering Beihang University School of Electronics and Information Engineering Beihang University

This paper is concerned with the amplitude boundedness problem of adaptive iterative learning control(AILC) for robot manipulators operating with iteration-dependent periods. By introducing virtual memory slots for storing historical data, a practical AILC method is proposed to achieve the segmentwise learning. This method requires less memory storage for historical information of previous iterations,especially in comparison with that of the conventional AILC methods using point-wise learning *** is shown that not only the energy boundedness but also the amplitude boundedness of estimates and inputs of practical AILC can be guaranteed. Moreover, the practical AILC method can achieve the perfect tracking objective regardless of iteration-dependent periods when the robot manipulators have a persistent full learning property. In addition, a solution to the visual manipulator platform is provided and deployed based on Coppeliasim and Matlab, which helps to show the amplitude boundedness of learning results and the perfect tracking performances of the proposed practical AILC method for robot manipulators.

关键词： amplitude boundedness iteration-dependent period iterative learning control robot manipulator segment-wise virtual memory slot

A survey on distributed iterative learning control for transient formation

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Control Theory and Technology 2021年第2期19卷 295-297页

作者： deyuan meng Yuxin Wu The Seventh Research Division Beihang University(BUAA)Beijing100191China School of Automation Science and Electrical Engineering Beihang University(BUAA)Beijing100191China

Distributed control is a hot topic in many research *** to the development of communication and computation technologies,plenty of complex engineering problems can resort to the mechanism of distributed *** note is that most of the distributed control design only leverages the real-time information of agents,but as a consequence,only some specific steady-state objectives and/or system performances can be ***,in many real scenarios,of particular importance are the transient behaviors of control *** deal with such problems in conventional situations.

关键词： transient iterative consequence

Resilient observer-based event-triggered control for cyber-physical systems under asynchronous denial-of-service attacks

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Science China(Information Sciences) 2022年第4期65卷 182-196页

作者： Yifang ZHANG Zheng-Guang WU Zongze WU deyuan meng State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and ControlZhejiang University School of Automation Guangdong University of Technology Seventh Research Division Beihang University (BUAA) School of Automation Science and Electrical Engineering Beihang University (BUAA)

We develop the resilient observer-based event-triggered control update strategy in this *** is utilized to achieve the input-to-state stability(ISS) of cyber-physical systems(CPSs) when there is an asynchronous denial-of-service(DoS) attack, which is launched by malicious adversaries both on measurement channel and control channel in a random attack strategy. To estimate the unmeasurable states while saving the limited networked bandwidth, an H∞observer-based event-triggered control scheme is first designed to guarantee the ISS of CPSs with economic communication. Moreover, the occurrence of Zeno behavior can be eliminated by providing the existence of a lower positive bound of any two inter-event times. Then, a recursive model of asynchronous DoS attacks is introduced. A resilient control update strategy is proposed and further analyzed to derive the stability criterion of CPSs. At last, a numerical simulation example is given to demonstrate the effectiveness of the introduced control update policy.

关键词： cyber-physical systems input-to-state stability asynchronous DoS attacks $H_\infty~$ observer event-triggered control

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Adaptive Robust Motion Trajectory Tracking Control of Pneumatic Cylinders with LuGre Model-based Friction Compensation

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Chinese Journal of Mechanical Engineering 2014年第4期27卷 802-815页

作者： meng deyuan TAO Guoliang LIU Hao ZHU Xiaocong State Key Laboratory of Fluid Power Transmission and Control Zhejiang University Department of Mechanical Engineering The Hong Kong Polytechnic University

Friction compensation is particularly important for motion trajectory tracking control of pneumatic cylinders at low speed movement. However, most of the existing model-based friction compensation schemes use simple classical models, which are not enough to address applications with high-accuracy position requirements. Furthermore, the friction force in the cylinder is time-varying, and there exist rather severe unmodelled dynamics and unknown disturbances in the pneumatic system. To deal with these problems effectively, an adaptive robust controller with LuGre model-based dynamic friction compensation is constructed. The proposed controller employs on-line recursive least squares estimation（RLSE） to reduce the extent of parametric uncertainties, and utilizes the sliding mode control method to attenuate the effects of parameter estimation errors, unmodelled dynamics and disturbances. In addition, in order to realize LuGre model-based friction compensation, the modified dual-observer structure for estimating immeasurable friction internal state is developed. Therefore, a prescribed motion tracking transient performance and final tracking accuracy can be guaranteed. Since the system model uncertainties are unmatched, the recursive backstepping design technology is applied. In order to solve the conflicts between the sliding mode control design and the adaptive control design, the projection mapping is used to condition the RLSE algorithm so that the parameter estimates are kept within a known bounded convex set. Finally, the proposed controller is tested for tracking sinusoidal trajectories and smooth square trajectory under different loads and sudden disturbance. The testing results demonstrate that the achievable performance of the proposed controller is excellent and is much better than most other studies in literature. Especially when a 0.5 Hz sinusoidal trajectory is tracked, the maximum tracking error is 0.96 mm and the average tracking error is 0.45 mm. This pa

关键词： servo-pneumatic system tracking control sliding mode control adaptive control LuGre model

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

Feedback iterative learning control for time-delay systems based on 2D analysis approach

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控制理论与应用（英文版） 2010年第4期8卷 457-463页

作者： deyuan meng Yingmin JIA Junping DU Shiying YUAN The Seventh Research Division Beihang University Beijing 100191 China Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia School of Computer Science and Technology Beijing University of Posts and Telecommunications Beijing 100876 China School of Electrical Engineering and Automation Henan Polytechnic University Jiaozuo Henan 454000 China

This paper deals with the iterative learning control （ILC） design for multiple-input multiple-output （MIMO）,time-delay systems （TDS）.Two feedback ILC schemes are considered using the so-called two-dimensional （2D） analysis *** shows that continuous-discrete 2D Roesser systems can be developed to describe the entire learning dynamics of both ILC schemes,based on which necessary and sufficient conditions for their stability can be provided.A numerical example is included to validate the theoretical analysis.

关键词： Iterative learning control Time-delay systems 2D analysis approach

Indirect adaptive fuzzy-regulated optimal control for unknown continuous-time nonlinear systems

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Frontiers of Information Technology & Electronic Engineering 2021年第2期22卷 155-169页

作者： Haiyun ZHANG deyuan meng Jin WANG Guodong LU State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang UniversityHangzhou 310027China Department of Mechatronic Engineering China University of Mining and TechnologyXuzhou 221116China

We present a novel indirect adaptive fuzzy-regulated optimal control scheme for continuous-time nonlinear systems with unknown dynamics,mismatches,and ***,the Hamilton-Jacobi-Bellman(HJB)equation associated with its performance function is derived for the original nonlinear *** existing adaptive dynamic programming(ADP)approaches,this scheme uses a special non-quadratic variable performance function as the reinforcement medium in the actor-critic *** adaptive fuzzy-regulated critic structure is correspondingly constructed to configure the weighting matrix of the performance function for the purpose of approximating and balancing the HJB equation.A concurrent self-organizing learning technique is designed to adaptively update the critic *** on this particular critic,an adaptive optimal feedback controller is developed as the actor with a new form of augmented Riccati equation to optimize the fuzzy-regulated variable performance function in real *** result is an online indirect adaptive optimal control mechanism implemented as an actor-critic structure,which involves continuous-time adaptation of both the optimal cost and the optimal control *** convergence and closed-loop stability of the proposed system are proved and *** examples and comparisons show the effectiveness and advantages of the proposed method.

关键词： Indirect adaptive optimal control Hamilton-Jacobi-Bellman equation Fuzzy-regulated critic Adaptive optimal control actor Actor-critic structure Unknown nonlinear systems