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Distributed optimal control for continuous-time nonaffine nonlinear interconnected systems

INTERNATIONAL JOURNAL OF CONTROL 2022年第12期95卷 3462-3476页

作者： Farzanegan, Behzad Suratgar, Amir Abolfazl Menhaj, Mohammad Bagher Zamani, Mohsen Amirkabir Univ Technol Tehran Polytech Dept Elect Engn Tehran Iran Univ Newcastle Sch Elect Engn & Comp Sci Newcastle NSW Australia

In this paper, a new distributed optimal control strategy for continuous-time nonaffine nonlinear systems with unknown dynamics is presented. We exploit a paradigm based on an Extended adaptive dynamic programming (EADP), which will be fully stated in the paper, to provide a distributed optimal control strategy in the presence of subsystem interactions. A proper design of optimal control law is further fully investigated through the framework of Hamilton-Jacobi-Bellman (HJB). The proposed EADP algorithm can now be employed in order to solve iteratively the HJB equation associated with each subsystem. The proposed distributed optimal control system mainly consisting of three weighted basis functions for each subsystem is fully developed to handle the issue associated with the availability of unknown dynamics. The convergence analysis of the proposed algorithms is also established. Finally, two numerical examples are given to confirm the effectiveness of the proposed control scheme.

关键词： adaptive dynamic programming nonlinear interconnected systems distributed control systems data-driven optimal control reinforcement learning

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Multiplayer zero-sum games optimal control for modular robot manipulators with interconnected dynamic couplings

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INTERNATIONAL JOURNAL OF adaptive CONTROL AND SIGNAL PROCESSING 2022年第12期36卷 3254-3270页

作者： Zhu, Xinye An, Tianjiao Dong, Bo Changchun Univ Technol Dept Control Sci & Engn Changchun Peoples R China

This article presents a multiplayer zero-sum game optimal control for modular robot manipulators (MRMs) via adaptive dynamic programming (ADP). The dynamic model of MRMs with interconnected dynamic couplings (IDCs) is formulated. The control law and IDC effect are regarded as players in each module. The optimal tracking control problem of the MRM system can be transformed into a multiplayer zero-sum game. Base on ADP algorithm, the Hamilton-Jacobi-Issacs (HJI) equation is solved via critic neural network. Then, the optimal control law can be derivated. The closed-loop robotic system is proved to be asymptotic stable and experiments are conducted to verify the effectiveness of the control method.

关键词： adaptive dynamic programming modular robot manipulators multiplayer zero-sum game optimal control

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Firefly optimized neural network-based trajectory tracking control of partially unknown multiplayer nonlinear systems

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2024年第18期34卷 12187-12206页

作者： Wu, Qiuye Zhao, Bo Liu, Derong Guangdong Police Coll Sch Publ Secur & Traff Management Guangzhou Peoples R China Beijing Normal Univ Sch Syst Sci Beijing Peoples R China Southern Univ Sci & Technol Sch Syst Design & Intelligent Mfg Shenzhen Peoples R China Univ Illinois Dept Elect & Comp Engn Chicago IL USA

In this paper, we develop an integral reinforcement learning (IRL)-based trajectory tracking control (TTC) scheme via firefly optimized neural networks for partially unknown multiplayer nonlinear systems. Under the developed TTC scheme, IRL is proved to be equivalent to the classical policy iteration, which guarantees the convergence of the IRL algorithm. By implementing the IRL method, the requirement of the drift dynamics is obviated. The TTC policy for each player is obtained by solving the coupled Hamilton-Jacobi equation with a critic neural network, whose weight vector is tuned by the firefly algorithm. The tracking error of the closed-loop system is guaranteed to be stable via the Lyapunov's direct method. Simulation results illustrate the effectiveness and superiority of the present IRL-based TTC scheme, and show that the success rate of system operation is increased by introducing the firefly algorithm.

关键词： adaptive dynamic programming firefly algorithm integral reinforcement learning multiplayer nonlinear systems neural networks trajectory tracking control

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adaptive Optimal Control via Q-Learning for Multi-Agent Pursuit-Evasion Games

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2024年第6期71卷 3056-3060页

作者： Dong, Xu Zhang, Huaguang Ming, Zhongyang Northeastern Univ Sch Informat Sci & Engn Shenyang 110004 Liaoning Peoples R China

The multi-agent pursuit-evasion (PE) games are solved in this brief to find the best possible strategic options for every participant. In these games, numerous pursuers aim to catch numerous evaders which are trying to escape arrest. In order to determine distributed control rules for each agent, a graph-theoretic technique is used to examine how the agents with restricted sensing capabilities interact with one another. Further, the online and real-time solution of Hamilton-Jacobi-Bellman (HJB) equations is necessary to achieve the optimal actions. By introducing the Q -learning technique's policy iteration, we are able to solve this issue. Once capture, the game is over. The simulation results are shown to demonstrate the viability of the suggested techniques.

关键词： Multi-agent systems Mathematical models Games Technological innovation Symmetric matrices Sensors Q-learning adaptive dynamic programming Pursuit-evasion game

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Neurodynamic programming and Tracking Control for Nonlinear Stochastic Systems by PI Algorithm

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2022年第6期69卷 2892-2896页

作者： Ming, Zhongyang Zhang, Huaguang Li, Weihua Luo, Yanhong Northeastern Univ Sch Informat Sci & Engn Shenyang 110004 Liaoning Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110004 Liaoning Peoples R China

In this paper, for nonlinear stochastic systems, the optimal tracking control problem (OTCP) is solved by adaptive dynamic programming (ADP) techniques. Firstly, the complex OTCP is transformed into a stable control optimization problem by reconstructing a new stochastic augmented system. Then, simplifying the actor-critic architecture and reducing the computational load, critic neural networks (NNs) is used in iterative learning. And by using Lyapunov method, the ultimate uniform boundedness (UUB) of the tracking system is proved. To be precise, no literature has been published on the OTCP for nonlinear Ito type stochastic systems via the ADP method. This work is the first attempt in this field. Finally, in simulation, the method is applied to sinusoidal waveform and periodic rectangular step signal, and even unbounded exponential waveform.

关键词： Stochastic systems adaptive systems Optimal control Trajectory Stability analysis Nonlinear dynamical systems Indium tin oxide Stochastic systems adaptive dynamic programming optimal tracking control problem neural network

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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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Online adaptive Integral Reinforcement Learning for Nonlinear Multi-Input System

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2023年第11期70卷 4176-4180页

作者： Lv, Yongfeng Chang, Huimin Zhao, Jun Taiyuan Univ Technol Coll Elect & Power Engn Taiyuan 030024 Peoples R China Shanxi Univ Sch Math Sci Taiyuan 030006 Peoples R China Shandong Univ Sci & Technol Coll Transportat Qingdao 266590 Peoples R China

In this brief article, a novel adaptive integral reinforcement learning (AIRL) scheme is proposed for the continuous-time (CT) system. Moreover, it is used to learn the optimal controls of the partially unknown multi-input nonlinear system. Firstly, the Nash equilibrium of multi-input is defined. Two neural networks (NN) are used to approximate the cost functions with the integral reinforcement signal, which can avoid directly solving the Hamilton-Jacobi-Bellman (HJB) equation such that dynamic information and derivatives of NN activations are not needed. Then, a novel learning algorithm is used to update the unknown NN weights. The studied weights are used to obtain the optimum multi-policies. The learned weight convergence is proved. Finally, two examples are presented to verify the system performance with the proposed AIRL scheme.

关键词： Integral reinforcement multi-input system adaptive dynamic programming adaptive law Nash equilibrium

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Advanced optimal tracking integrating a neural critic technique for asymmetric constrained zero-sum games ☆

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NEURAL NETWORKS 2024年 177卷 106388页

作者： Li, Menghua Wang, Ding Ren, Jin 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

This paper investigates the optimal tracking issue for continuous -time (CT) nonlinear asymmetric constrained zero -sum games (ZSGs) by exploiting the neural critic technique. Initially, an improved algorithm is constructed to tackle the tracking control problem of nonlinear CT multiplayer ZSGs. Also, we give a novel nonquadratic function to settle the asymmetric constraints. One thing worth noting is that the method used in this paper to solve asymmetric constraints eliminates the strict restriction on the control matrix compared to the previous ones. Further, the optimal controls, the worst disturbances, and the tracking Hamilton-Jacobi-Isaacs equation are derived. Next, a single critic neural network is built to estimate the optimal cost function, thus obtaining the approximations of the optimal controls and the worst disturbances. The critic network weight is updated by the normalized steepest descent algorithm. Additionally, based on the Lyapunov method, the stability of the tracking error and the weight estimation error of the critic network is analyzed. In the end, two examples are offered to validate the theoretical results.

关键词： adaptive dynamic programming Asymmetric input constraints Multiplayer zero-sum games Neural critic technique Optimal tracking control

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Optimal tracking control for completely unknown nonlinear discrete-time Markov jump systems using data-based reinforcement learning method

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NEUROCOMPUTING 2016年 194卷 176-182页

作者： Jiang, He Zhang, Huaguang Luo, Yanhong Wang, Junyi Northeastern Univ Coll Informat Sci & Engn Box 134 Shenyang 110819 Peoples R China

In this paper, we develop a novel optimal tracking control scheme for a class of nonlinear discrete-time Markov jump systems (MJSs) by utilizing a data-based reinforcement learning method. It is not practical to obtain accurate system models of the real-world MJSs due to the existence of abrupt variations in their system structures. Consequently, most traditional model-based methods for MJSs are invalid for the practical engineering applications. In order to overcome the difficulties without any identification scheme which would cause estimation errors, a model-free adaptive dynamic programming (ADP) algorithm will be designed by using system data rather than accurate system functions. Firstly, we combine the tracking error dynamics and reference system dynamics to form an augmented system. Then, based on the augmented system, a new performance index function with discount factor is formulated for the optimal tracking control problem via Markov chain and weighted sum technique. Neural networks are employed to implement the on-line ADP learning algorithm. Finally, a simulation example is given to demonstrate the effectiveness of our proposed approach. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Optimal tracking control Markov jump systems Data-based Reinforcement learning adaptive dynamic programming Neural networks

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A Unified Framework for Data-Driven Optimal Control of Connected Vehicles in Mixed Traffic

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第8期8卷 4131-4145页

作者： Liu, Tong Cui, Leilei Pang, Bo Jiang, Zhong-Ping NYU Tandon Sch Engn Dept Elect & Comp Engn Control & Networks Lab Brooklyn NY 11201 USA

This article presents a unified approach to the problem of learning-based optimal control of connected human-driven and autonomous vehicles in mixed-traffic environments including both the freeway and ring road settings. The stabilizability of a string of connected vehicles including multiple autonomous vehicles (AVs) and heterogeneous human-driven vehicles (HDVs) is studied by a model reduction technique and the Popov-Belevitch-Hautus (PBH) test. For this problem setup, a linear quadratic regulator (LQR) problem is formulated and a solution based on adaptive dynamic programming (ADP) techniques is proposed without a priori knowledge on model parameters. To start the learning process, an initial stabilizing control law is obtained using the small-gain theorem for the ring road case. It is shown that the obtained stabilizing control law can achieve general L-p string stability under appropriate conditions. Besides, to minimize the impact of external disturbance, a linear quadratic zero-sum game is introduced and solved by an iterative learning-based algorithm. Finally, the simulation results verify the theoretical analysis and the proposed methods achieve desirable performance for control of a mixed-vehicular network.

关键词： Traffic control Roads Optimal control Connected vehicles Behavioral sciences Games Stability criteria Connected and autonomous vehicles (CAVs) stabilizability adaptive dynamic programming optimal control disturbance attenuation

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