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43rd Chinese Control Conference (CCC)

作者： Kong, Jie Zhao, Bo Beijing Normal Univ Sch Syst Sci Beijing 100875 Peoples R China

ISBN: (纸本)9798350366907;9789887581581

In this paper, a fractional-order online policy iteration (FOOPI)-based optimal control strategy is developed for a class of fractional-order nonlinear systems (FONSs). The fractional Taylor expansion and the property of Hadamard product are utilized to derive the fractional Hamilton-Jacobi-Bellman equation, which is solved by a FOOPI method consisting of policy evaluation and policy improvement. Through constructing a critic neural network (NN), whose weight vector is adjusted by the gradient descent algorithm, the cost function and the fractional control policy are obtained approximately. Then, the state trajectories of closed-loop FONSs and the critic NN weight vector approximation error dynamics are guaranteed to be uniformly ultimately bounded. Simulation results illustrate the effectiveness of the presented fractional optimal control scheme.

关键词： adaptive dynamic programming reinforcement learning optimal control neural networks fractional-order nonlinear systems

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Distributed Finite-Time ADP-Based Optimal Control for Nonlinear Multiagent Systems

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2023年第12期70卷 4534-4538页

作者： Zhang, Longjie Chen, Yong Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Peoples R China Sichuan Prov Engn Technol Res Ctr Elect Vehicle Dr Chengdu 611731 Peoples R China

In this brief, the finite-time stability of the optimal control is considered and the ADP-based finite-time distributed optimal controller (DFTOC) is proposed for the nonlinear multiagent systems. Firstly, to obtain the candidate optimal value function with finite-time convergence space, the sign function is applied to map the value function into the finite-time convergence space. The finite-time optimal controller is obtained by solving the redesigned Hamilton-Jacobi-Bellman (HJB) equation with the mapped value function, and the finite-time stability is illustrated theoretically. Moreover, to solve the proposed finite-time optimal controller, the finite-time adaptive dynamic programming (FTADP) algorithm is further implemented based on the finite-time optimization method. Finally, compared with optimal control under the sense of asymptotic stability, the result analysis with Van der Pol's oscillator system shows the superiority of the proposed DFTOC.

关键词： Optimal control finite-time stability distributed control adaptive dynamic programming

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Event-triggered neural experience replay learning for nonzero-sum tracking games of unknown continuous-time nonlinear systems

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2023年第12期33卷 6553-6575页

作者： Cui, Xiaohong Peng, Binbin Wang, Binrui Wang, Lina China Jiliang Univ Coll Mech & Elect Engn Hangzhou Peoples R China China Jiliang Univ Key Lab Intelligent Mfg Qual Big Data Tracing & An Hangzhou Peoples R China China Jiliang Univ Coll Mech & Elect Engn Hangzhou 310000 Peoples R China

In this paper, an online event-triggered integral reinforcement learning is proposed to solve the nonzero-sum tracking games of the two-player nonlinear system with unknown dynamics and constrained input. Firstly, an augmented system of the nonzero-sum game is constructed to describe the tracking problem. Thus, the optimal tracking problem is treated as solving the coupled Hamilton-Jacobi (HJ) equations of the augmented system. To restrict the number of sampling states, a novel triggering threshold containing the augmented states is designed, and it avoids the existence of Zeno behavior. Secondly, a single-critic network is utilized to approximate the solution of the coupled HJ equations. The critic network turning law with experience replay technology relaxes the dependence on the persistence of excitation (PE) conditions in traditional integral reinforcement learning (IRL) by using historical data in the stack. Moreover, the augmented system states and the critic NN weight errors are uniformly ultimate boundedness (UUB) by Lyapunov theory. Simulation examples are provided to demonstrate the availability of the proposed algorithm.

关键词： adaptive dynamic programming event-triggered control integral reinforcement learning nonzero-sum tracking control

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programming-Based Optimal Learning Sliding Mode Control for Cooperative Dual Ship-Mounted Cranes Against Unmatched External Disturbances

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2023年第2期20卷 969-980页

作者： Qian, Yuzhe Hu, Die Chen, Yuzhu Fang, Yongchun Hebei Univ Technol Coll Artificial Intelligence Tianjin 300401 Peoples R China Nankai Univ Coll Artificial Intelligence Inst Robot & Automat Informat Syst IRAIS Tianjin 300353 Peoples R China Nankai Univ Tianjin Key Lab Intelligent Robot tjKLIR Tianjin 300353 Peoples R China

When lifting and transporting large payloads in the marine environment, the dual ship-mounted crane system plays a very important role for cargos transportation, which exhibits strong load capacity and high flexibility. However, apart from the nonlinearity and underactuation characteristic, some unknown or uncertain unmatched wave disturbances may also cause positioning errors, which may induce various risks during the transportation process;besides, lots of existing methods ignore a part of the cooperative crane motions, and the control issue of dual ship-mounted crane system with five degrees of freedoms (5 DOF) is still open. In terms of the aforementioned problems, an adaptive dynamic programming (ADP)-based optimal learning sliding mode controller is proposed in this paper. Specifically, under the frame of adaptive dynamic programming, the Hamilton-Jacobi-Bellman (HJB) equation can be addressed. Then, based on the gradient attenuation algorithm, critic neural networks (NNs) are trained depending on the designed updating law and the approximate optimal learning controller can be obtained. Lyapunov techniques and Lasalle's invariance principle are used to guarantee asymptotic stability of the dual ship-mounted cranes system. Finally, a series of simulation results are depicted to show the effectiveness of the proposed optimal learning sliding mode controller. Note to Practitioners-In this paper, the control problem of a 5 DOF dual ship-mounted cranes system with unmatched wave disturbance is studied. Due to the complex nonlinear characteristics of the dual ship-mounted cranes and the need to consider the cooperative lifting and wave disturbances, the operation of the dual ship-mounted cranes are very challenging. Moreover, it brings more difficulties to the design of this kind of controller due to its nonlinear underactuated property. Existing control methods for dual ship-mounted cranes are based on linearized or oversimplified crane models, or require accurate

关键词： Cranes Mathematical models Sliding mode control Optimal control Nonlinear dynamical systems Robots Artificial neural networks Dual ship-mounted cranes system nonlinear system optimal learning sliding mode control adaptive dynamic programming critic neural network

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Observer-based event triggered ADP approach for input-constrained nonlinear systems with disturbances

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2023年第10期33卷 5179-5197页

作者： Hu, Chuanhao Zou, Yuanyuan Li, Shaoyuan Shanghai Jiao Tong Univ Dept Automat Shanghai Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China

This paper proposes an ADP-based event-triggered control (ETC) approach for input-constrained nonlinear systems with external disturbances to effectively save computational cost. An observer is established to estimate unknown disturbances, and a novel performance index function is introduced to suppress the negative effect of disturbances and tackle the control constraints. Under the event-triggered mechanism, the corresponding nonquadratic Hamilton-Jacobi-Bellman equation is further formulated. With the aid of the modified performance index, an event-triggered condition is designed to reduce the controller updating times with guaranteed system stability. Furthermore, a single critic neural-network (NN) is constructed to approximate the optimal control policy by using the ADP technique. In order to relax the requirement of initial admissible control, a modification term is added to the critic weight updating law. According to the Lyapunov stability theorem, the closed-loop nonlinear system and the weight estimation error of the critic NN are both guaranteed to be uniformly ultimately bounded. Finally, simulation results and comparison studies justify the effectiveness of the proposed ADP-based ETC method.

关键词： adaptive dynamic programming constrained input event-triggered control uncertain nonlinear systems

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Event-triggered adaptive fuzzy optimal control of modular robot manipulators using zero-sum differential game through value iteration

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INTERNATIONAL JOURNAL OF adaptive CONTROL AND SIGNAL PROCESSING 2023年第9期37卷 2364-2379页

作者： Dong, Bo Feng, Zhian Cui, Yiming Zhu, Xinye An, Tianjiao Changchun Univ Tehnol Dept Control Sci & Engn Changchun Peoples R China Changchun Univ Tehnol Dept Control Sci & Engn Yanan Ave 2055 N Changchun Peoples R China

In this article, a novel event-triggered zero-sum differential game through value iteration (VI)-based adaptive fuzzy control is proposed for modular robot manipulators (MRMs) with uncertain disturbance. The subsystem dynamic of MRMs is established by joint torque feedback (JTF) technology. An identifier based on fuzzy logic model (FLM) is established to identify unknown system dynamics. Then, the trajectory tracking control issue of MRMs with uncertain disturbance is transformed into a two-player zero-sum differential game. On the basis of event-triggered mechanism and VI algorithm, the event-triggered Hamilton-Jacobi-Issacs (HJI) equation is approximately solved through establishment of actor-critic structure. Different from the traditional adaptive dynamic programming (ADP) algorithm, this scheme is based on adaptive fuzzy optimal control through an actor-critic structure, which refers to adaptation of both optimal cost function and optimal control policy. Moreover, tracking errors of closed-loop MRM system are demonstrated to be uniformly ultimately bounded (UUB) under Lyapunov stability theorem. At last, experimental examples and comparisons show the reliability and effectiveness of this method.

关键词： adaptive dynamic programming event-triggered mechanism fuzzy optimal control modular robot manipulators value iteration zero-sum differential game

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Event-triggered constrained neural critic control of nonlinear continuous-time multiplayer nonzero-sum games

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INFORMATION SCIENCES 2023年第1期631卷 412-428页

作者： Li, Menghua Wang, Ding Zhao, Mingming Qiao, Junfei Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Univ Technol Beijing Key Lab Computat Intelligence & Intelligen Beijing 100124 Peoples R China Beijing Univ Technol Beijing Inst Artificial Intelligence Beijing 100124 Peoples R China Beijing Univ Technol Beijing Lab Smart Environm Protect Beijing 100124 Peoples R China

In this article, by exploiting the event-triggered neural critic control scheme, the optimal control question of nonlinear continuous-time multiplayer nonzero-sum games with asymmetric control constraints is investigated. First, to satisfy the asymmetric constraints, we propose an appropriate nonquadratic function. Also, the time-based coupled Hamilton-Jacobi (HJ) equations and the optimal control policies are deduced. To save communication resources and improve control efficiency, the event-driven mechanism is innovated. Further, the event-triggered optimal control strategies and the event-triggered coupled HJ equations are given. Afterwards, a novel triggering condition is established, and the critic neural networks are built to evaluate the optimal cost functions, thereby obtaining the event-triggered near-optimal controls. During neural critic learning, a new weight adjustment rule is proposed. Moreover, by exploiting the Lyapunov approach, the stability of the system state and the weight estimation errors is guaranteed. Finally, two examples are given to verify the efficacy of the established event-triggered neural critic control scheme.

关键词： adaptive dynamic programming Asymmetric input constraints Event-based optimal control Multiplayer nonzero-sum games Neural critic control

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Disturbance Compensation-Based Optimal Tracking Control for Perturbed Nonlinear Systems

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IEEE ACCESS 2023年 11卷 50619-50630页

作者： Gao, Yanping Liu, Zuojun Hebei Univ Technol Sch Artificial Intelligence Tianjin Peoples R China Hebei Univ Technol Sch Artificial Intelligence Tianjin Peoples R China

This paper investigates the disturbance compensation-based optimal tracking control for nonlinear systems in the presence of uncertain dynamic drift and extraneous disturbance by using the adaptive dynamic programming (ADP). First, an extended state observer (ESO)-based disturbance rejection controller is designed to estimate the comprehensive disturbances of system. Then, a novel composite controller capable of online learning is developed based on disturbance rejection controller and optimal regulation law, where the optimal regulation law is conducted by ADP framework to stabilize the dynamics of tracking error and minimize predefined value function. Particularly, an improved critic-only weight updating algorithm is inserted in ADP for ensuring the finite time convergence of critic weight without resorting to traditional actor-critic structures enduring remarkable computational burden. Based on Lyapunov analysis, it is proved that the tracking errors and weight estimation errors of critic network are uniformly ultimately bounded and the pursued controller approximates to the optimal policy. Finally, simulation results are shown to check the superiority of involved strategy, and the value function can be decreased by 25% with consistent tracking performance.

关键词： Artificial neural networks Uncertainty Convergence Optimal control Regulation Vehicle dynamics Trajectory Disturbance compensation extended state observer perturbed nonlinear system critic-only adaptive dynamic programming

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Attitude optimization control of hypersonic flight vehicle considering partially unknown control direction

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2023年第7期45卷 1337-1350页

作者： Guo, Jianguo Wang, Jiaqi Guo, Zongyi Su, Yalu Northwestern Polytech Univ Inst Precis Guidance & Control Xian Peoples R China Northwestern Polytech Univ Ningbo Inst Sch Astronaut Xian Peoples R China Peking Univ Coll Engn Beijing Peoples R China North western Polytech Univ Ningbo Inst Sch Astronaut 127 Youyi West Rd Xian 710072 Peoples R China

In this paper, a control method based on periodic sliding mode technology and adaptive dynamic programming algorithm is proposed for the attitude dynamics of hypersonic flight vehicle. The method takes into account a case that there is unknown control direction in a single channel. First, under the premise that the disturbance observers estimate the coupling and other disturbances, the attitude dynamics are decomposed into mathematical models of pitch channel and yaw-roll channel, respectively. Then, a periodic sliding mode control law with both stability and rapidity is designed for the unknown control direction in pitch channel. In the yaw-roll channel, an optimal attitude control law satisfying cost function is designed based on adaptive dynamic programming algorithm. What's more, the closed-loop system is proved to be stable by Lyapunov stability theorem. Finally, the simulation results show that the attitude angle can track the command stably, which proves the effectiveness of the control scheme. Compared with the backstepping control scheme, the simulation results show that the proposed scheme can effectively avoid the divergence of the system because of the control direction switch, and the system have better dynamic performance and robustness.

关键词： Hypersonic flight vehicle disturbance observers unknown control direction adaptive dynamic programming stability robustness

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ADP-Based Optimal Control for Systems With Mismatched Disturbances: A PMSM Application

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2023年第6期70卷 2057-2061页

作者： Fan, Zhong-Xin Li, Shihua Liu, Rongjie Southeast Univ Sch Automat Nanjing 210096 Peoples R China Southeast Univ Sch Automat Nanjing 210096 Peoples R China Southeast Univ Key Lab Measurement & Control Complex Syst Engn Minist Educ Nanjing 210096 Peoples R China Florida State Univ Dept Stat Tallahassee FL 32306 USA

In this brief, in order to optimize the tracking accuracy and enhance the robustness of the tracking process for permanent magnet synchronous motor (PMSM), an adaptive dynamic programming (ADP)-based optimal control strategy combining with anti-disturbance control method is proposed. We consider the unknown, mismatched and time-varying load torque as a disturbance and a disturbance observer is then designed. Based on the output of the disturbance observer, a feedforward control can compensate the disturbance in the PMSM in real time. For the easy implementation of optimal control, this brief adopts the actor-critic neural network to approximate the value function and the optimal controller, respectively. A composite controller is then proposed to ensure that the speed can track the reference signal in an optimal and robust way. Finally, an experiment based on a motor towed platform is given to prove the optimality and robustness.

关键词： Optimal control Neural networks Disturbance observers Robustness Permanent magnet motors Biological neural networks Torque adaptive dynamic programming actor-critic neural network disturbance observer optimal control PMSM

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