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Distributed Observer-Based Optimal Cooperative Output Regulation With Control Effectiveness Faults

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2024年第3期71卷 1361-1365页

作者： Chen, Zitao Zhong, Weifeng Xie, Shengli Zhang, Yun Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Seoul Natl Univ Dept Math Sci Seoul 08826 South Korea Guangdong Univ Technol Sch Automat Guangdong Key Lab IoT Informat Technol Guangzhou 510006 Peoples R China Guangdong Univ Technol Key Lab Intelligent Informat Proc & Syst Integrat Minist Educ Guangzhou 510006 Peoples R China Guangdong Univ Technol Guangdong Hong Kong Macao Joint Lab Smart Discrete Guangzhou 510006 Peoples R China

Partial access to the reference state is one of the most practical obstacles in cooperative control. This brief studies a control effectiveness fault-tolerant cooperative output regulation problem with the limited measurement of the reference system. To achieve reference state estimation, a novel Luenberger-type distributed observer is designed in collaboration with neighbors. With the estimated state serving as the reference generator, adaptive dynamic programming is proposed to solve the decentralized output regulation in an optimal manner for the nominal system. Based on the optimal control protocol for the nominal system, a robust dynamical control law is further designed to eliminate the negative effects brought by the control faults, which guarantees that the tracking error converges to zero. A multi-RLC circuit system is used as an example to verify the effectiveness of the proposed method.

关键词： Distributed observer adaptive dynamic programming control fault cooperative output regulation

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Optimal Path Tracking Control Based on Online Modeling for Autonomous Vehicle With Completely Unknown Parameters

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第12期24卷 15207-15218页

作者： Ji, Yuan Zhang, Junzhi Lv, Chen He, Chengkun Chen, Hao Han, Jinheng Hou, Xiaohui Tsinghua Univ Sch Vehicle & Mobil Beijing 100084 Peoples R China Tsinghua Univ State Key Lab Intelligent Green Vehicle & Mobil Beijing 100084 Peoples R China Nanyang Technol Univ Sch Mech & Aerosp Engn Singapore 639798 Singapore Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China

Reliable path tracking control (PTC) method is essential for autonomous driving. However, existing PTC methods count on prior vehicle parameters to achieve good performance. This paper presents an optimal PTC method without requiring any prior vehicle parameters based on online modeling with strict parameter convergence ability. First, we build a virtual optimal control problem using adaptive dynamic programming (ADP) scheme to guide the data collection and solve two characteristic matrices containing parameter information. Then, the model construction method is derived using the solved matrices and the optimal PTC method is constructed using the constructed model. Finally, a fault-tolerant control scheme is further designed using the constructed model and the online modeling ability of the proposed method. The effectiveness of the proposed method is validated through co-simulation between Matlab/Simulink and high-fidelity vehicle dynamic simulation software CarSim (R) under both fault-free and fault-tolerant situations.

关键词： Path tracking online modelling adaptive dynamic programming fault-tolerant

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Advanced value iteration for discrete-time intelligent critic control: A survey

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ARTIFICIAL INTELLIGENCE REVIEW 2023年第10期56卷 12315-12346页

作者： Zhao, Mingming Wang, Ding Qiao, Junfei Ha, Mingming Ren, Jin 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 Univ Sci & Technol Beijing Sch Automation & Elect Engn Beijing 100083 Peoples R China

Optimal control problems are ubiquitous in practical engineering applications and social life with the idea of cost or resource conservation. Based on the critic learning scheme, adaptive dynamic programming (ADP) is regarded as a significant avenue to address the optimal control problems by combining the advanced design ideas such as adaptive control, reinforcement learning, and intelligent control. This survey introduces the recent development of ADP and related intelligent critic control with an emphasis on advanced value iteration (VI) schemes for discrete-time nonlinear systems. The theoretical results focus on convergence and stability properties for general VI, stabilizing VI, integrated VI, evolving VI, adjustable VI schemes and so on. Several significant applications are also elaborated in aspects of optimal regulation, optimal tracking, and zero-sum games. We aim to break through the bottleneck problems for VI algorithms in realizing evolving control, accelerating learning speed, and reducing the calculation expense. In addition, the prospects of new theoretical and technical fields for advanced VI schemes are looked ahead.

关键词： adaptive dynamic programming Advanced value iteration Optimal control

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Comprehensive Optimal Network Scheduling Strategies for Wireless Control Systems

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ACM TRANSACTIONS ON CYBER-PHYSICAL SYSTEMS 2024年第4期8卷 1-35页

作者： Fu, Ruijie Guo, Lancong Zou, An Chen, Cailian Guan, Xinping Ma, Yehan Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Peoples R China Shanghai Jiao Tong Univ Univ Michigan Shanghai Jiao Tong Univ Joint Inst Shanghai Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai Peoples R China Shanghai Jiao Tong Univ Shanghai Engn Res Ctr Intelligent Control & Manage Key Lab Syst Control & Informat Proc Minist Educ China Shanghai Peoples R China

Although wireless control is one of the key technologies for future industries, most wireless networks are only used for monitoring. When wireless networks are applied to transmit control commands, the uncertain link qualities and limited network resources may destroy the performance of multi-loop control systems. Hence, it is critical to allocate these resources to optimize the control performance as the network condition changes and plants evolve. This article presents comprehensive optimal scheduling strategies for wireless control systems based on adaptive dynamic programming. First, we propose an effective adaptive dynamic programming scheduling (ADPS) strategy to solve the optimal scheduling problem based on the singlestep control performance at runtime while significantly reducing computational complexity. Moreover, to overcome the "short-sightedness" of single-step performance prediction, we extend ADPS to ADPS-m (multistep prediction), which optimizes multi-step performance by incorporating a longer-horizon evolution of the plants. Furthermore, we propose ADPS-H (Heterogeneous flow scheduling) to support heterogeneous flows with different data rates and sizes and ADPS-H-m (multi-step prediction for Heterogeneous flow scheduling), which schedules heterogeneous flows in a longer prediction horizon. We prove that all these scheduling strategies can achieve optimality and stability under mild assumptions. Extensive experiments integrating TOSSIM and MATLAB/Simulink are performed to evaluate all of the proposed methods in case studies of fourand ten-loop control systems. The simulation results demonstrate that these strategies can effectively improve the control performance at lower computing costs under both cyber and physical disturbances. Under the noise level of -76 dBm, for the four-loop case, ADPS achieves the same control performance as the linear programming while saving 99.5% of the execution time. ADPS-m further improves the control performance by up t

关键词： Wireless network scheduling cyber-physical systems optimal scheduling control performance wireless control systems adaptive dynamic programming

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CAOC: Cooperative adaptive optimal control algorithm for networked direct current servo systems

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MECHATRONICS 2025年 108卷

作者： Thai-Dai, Loi Ho Tan, Luy Nguyen Le, Dung Nguyen Huynh, Lam Phan Hoang, Giap Nguyen Ho Chi Minh City Univ Technol HCMUT Vietnam Natl Univ Ho Chi Minh City VNU HCM Ho Chi Minh City Vietnam

This letter proposes a novel cooperative adaptive optimal control (CAOC) algorithm for networked direct current servo (DCS) systems to achieve tracking synchronization in a cooperative system, where the leader generates the desired speed and the DCS followers track its output and synchronize with their own neighbors according to the communication graph topology in real time. As consensus tracking error dynamics is affected by the control inputs of the neighboring agents, the cost function for each agent includes not only its consensus tracking error and energy, but also the energies of the neighbors. Firstly, based on the Lyapunov theory and backstepping techniques, we design feedforward controllers that generate augmented control inputs to transform local consensus tracking dynamics in strict feedback form into affine form. Secondly, based on adaptive dynamic programming (ADP), we design the CAOC algorithm to minimize the performance index function. Finally, we conduct the simulation and experiment on the STM32F103 microcontrollers to validate the effectiveness of the proposed algorithm.

关键词： Cooperative optimal control adaptive dynamic programming Backstepping Direct current servo systems

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Multi-objective optimal control for a class of nonlinear time-delay systems via adaptive dynamic programming

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SOFT COMPUTING 2013年第11期17卷 2109-2115页

作者： Song, Ruizhuo Xiao, Wendong Wei, Qinglai Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

A novel multi-objective adaptive dynamic programming (ADP) method is constructed to obtain the optimal controller of a class of nonlinear time-delay systems in this paper. Using the weighted sum technology, the original multi-objective optimal control problem is transformed to the single one. An ADP method is established for nonlinear time-delay systems to solve the optimal control problem. To demonstrate that the presented iterative performance index function sequence is convergent and the closed-loop system is asymptotically stable, the convergence analysis is also given. The neural networks are used to get the approximative control policy and the approximative performance index function, respectively. Two simulation examples are presented to illustrate the performance of the presented optimal control method.

关键词： Optimal control adaptive dynamic programming Multi-objective Approximate dynamic programming Time-delay

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Event-triggered control for input constrained non-affine nonlinear systems based on neuro-dynamic programming

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NEUROCOMPUTING 2021年 440卷 175-184页

作者： Zhang, Shunchao Zhao, Bo Zhang, Yongwei Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Beijing Normal Univ Sch Syst Sci Beijing 100875 Peoples R China

In this paper, a neuro-dynamic programming (NDP)-based event-triggered control (ETC) method is proposed for unknown non-affine nonlinear systems with input constraints. A neural network-based identifier is established with measurable input and output data to learn the unknown system dynamics. Then, a critic neural network is employed to approximate the value function for solving the event triggered Hamilton-Jacobi-Bellman equation. Furthermore, an NDP-based ETC scheme is developed, which samples the states and updates the control law when the triggering condition is violated. Compared with the traditional time-triggered control methods, the ETC method can reduce computational burden, communication cost and bandwidth. In addition, the stability of the closed-loop system and the weight error convergence of the critic neural network are provided based on the Lyapunov?s direct method. The intersamling time is proved to be bounded by a positive constant, which excludes the Zeno behavior. Finally, two case studies are provided to verify the effectiveness of the developed ETC method. CO 2021 Elsevier B.V. All rights reserved.

关键词： Neuro-dynamic programming adaptive dynamic programming Event-triggered control Neural networks Non-affine nonlinear systems Input constraints

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Safe adaptive learning algorithm with neural network implementation for H∞ control of nonlinear safety-critical system

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2023年第1期33卷 372-391页

作者： Qin, Chunbin Wang, Jinguang Zhu, Heyang Xiao, Qiyang Zhang, Dehua Henan Univ Sch Artificial Intelligence Zhengzhou Peoples R China

In this article, the H-infinity safe control problem of continuous-time affine nonlinear safety-critical systems is studied based on the barrier function (BF) and adaptive dynamic programming (ADP). We show that the safety constraints in this article occur under the condition that the system initial state is unsafe and have not been adequately addressed in the existing work. First, the H-infinity control problem is transformed into a zero-sum game problem, and a new safe Hamilton-Jacobi-Isaacs equation is proposed by combining with the BF, which makes the unsafe behavior be punished in the learning process. In addition, a damping coefficient is introduced into the BF to punish the unsafe behavior more flexibly. Aiming at the requirement that the initial system state must be strictly constrained in the safe set, a new weight updating method based on ADP is proposed, which can reasonably avoid the influence of the BF on neural network (NN) parameters when the initial system state is unsafe. Furthermore, based on the Lyapunov stability theory, it is proved that the system states of safety-critical systems and the NN parameters are uniformly ultimately bounded under the safety constraints and disturbances effect. Finally, the effectiveness of the proposed method is verified by two simulation examples.

关键词： adaptive dynamic programming barrier function H-infinity control neural network safety-critical system

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Policy Iteration for Optimal Control of Discrete-Time Time-Varying Nonlinear Systems

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IEEE/CAA Journal of Automatica Sinica 2023年第3期10卷 781-791页

作者： Guangyu Zhu Xiaolu Li Ranran Sun Yiyuan Yang Peng Zhang Beijing Research Center of Urban Traffic Information Sensing and Service Technologies Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing Jiaotong UniversityBeijing 100044China Transport Planning and Research Institute Ministry of TransportChinaBeijing 100028China

Aimed at infinite horizon optimal control problems of discrete time-varying nonlinear systems,in this paper,a new iterative adaptive dynamic programming algorithm,which is the discrete-time time-varying policy iteration(DTTV)algorithm,is *** iterative control law is designed to update the iterative value function which approximates the index function of optimal *** admissibility of the iterative control law is *** results show that the iterative value function is non-increasingly convergent to the Bellman-equation optimal *** implement the algorithm,neural networks are employed and a new implementation structure is established,which avoids solving the generalized Bellman equation in each ***,the optimal control laws for torsional pendulum and inverted pendulum systems are obtained by using the DTTV policy iteration algorithm,where the mass and pendulum bar length are permitted to be time-varying *** effectiveness of the developed method is illustrated by numerical results and comparisons.

关键词： adaptive critic designs adaptive dynamic programming approximate dynamic programming optimal control policy iteration time-varying

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adaptive neural fault-tolerant optimal control for nonlinear uncertain systems with dynamic state constraints

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2023年第14期33卷 8400-8420页

作者： Wei, Yan Fu, Jun Yan, Huaicheng Fei, Minrui Wang, Yueying Zhejiang Univ Technol Coll Informat Engn Hangzhou Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang Peoples R China East China Univ Sci & Technol Sch Informat Sci & Engn Shanghai Peoples R China Shanghai Univ Sch Mechatron Engn & Automat Shanghai Peoples R China Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200240 Peoples R China

This article investigates the issue of adaptive neural optimal fault-tolerant control for a class of nonlinear uncertain systems subject to dynamic state constraints and external disturbances. To handle more general dynamic constraints, a unified tangent-type nonlinear mapping is first proposed to transform the state-constrained system into one free of constraints. To solve the problem of actuator faults and external disturbances, a single network adaptive dynamic program method is designed, which consists of a feed-forward fault-tolerant control scheme and a feedback differential game control strategy. Neural networks are employed to approximate the uncertainties and cost function, respectively. To handle the issue of "explosion of complexity," a finite-time convergent differentiator is established to estimate the derivative of virtual control signals in the backstepping design. Via Lyapunov stability analysis, asymptotic stability of the original and transformed nonlinear system is theoretically guaranteed. Two comparative simulation examples are provided to evaluate the efficacy of the proposed control approach.

关键词： adaptive dynamic programming differential games fault-tolerant control nonlinear uncertain systems state constraint

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