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检索条件"主题词=Adaptive Dynamic Programming"

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Generalized Policy Iteration-based Reinforcement Learning Algorithm for Optimal Control of Unknown Discrete-time Systems 33

Generalized Policy Iteration-based Reinforcement Learning Al...

引用

33rd Chinese Control and Decision Conference (CCDC)

作者： Lin, Mingduo Zhao, Bo Liu, Derong Liu, Xi Luo, Fangchao Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Beijing Normal Univ Sch Syst Sci Beijing 100875 Peoples R China

ISBN: (纸本)9781665440899

This paper presents a novel generalized policy iteration-based reinforcement learning (RL) algorithm to deal with infinite-horizon optimal control problems of nonlinear discrete-time systems with completely unknown dynamics. In the present iterative algorithm, two iteration procedures are utilized to obtain the iterative Q-function and the iterative control policy. Furthermore, the iterative Q-function is obtained by the temporal difference learning and the policy gradient method is utilized to directly optimize the iterative control policy. Then, the convergence and optimality analysis of the generalized policy iteration-based RL algorithm are provided. To implement this algorithm, two neural networks, including a critic network and an action network, are used to approximate the iterative Q-function and the iterative control policy. Finally, a numerical simulation example is provided to illustrate the effectiveness of the proposed control method.

关键词： adaptive dynamic programming Reinforcement learning Generalized policy iteration Neural networks Optimal control Unknown Discrete-time Systems

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Data-Driven Policy Learning Strategy for Nonlinear Robust Control With Unknown Perturbation 33

Data-Driven Policy Learning Strategy for Nonlinear Robust Co...

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33rd Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Fang, Sai Wang, Ding Liu, Derong Ha, Mingming Univ Sci & Technol Beijing Sch Automat Beijing Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China Guangdong Univ Technol Sch Automat Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781538672556

In this paper, we propose a robust optimal control policy for nonlinear systems with bounded unknown perturbation by using data-driven policy learning strategy. The robust control problem is transformed into a corresponding optimal control design with specific cost function. Neural-network-based data driven policy learning strategy is presented to solve the problem without system dynamics. The solution of the optimal control problem can asymptotically stabilize the unknown system. An example is given to illustrate the established method.

关键词： adaptive dynamic programming optimal control robust control learning control policy iteration

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Online Value Iteration for Discrete-Time Nonlinear Optimal Regulation with Stability Guarantee 4

Online Value Iteration for Discrete-Time Nonlinear Optimal R...

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4th International Conference on Control and Robotics (ICCR)

作者： Wang, Yuan Wang, Ding Wu, Junlong Zhao, Mingming Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Univ Technol Beijing Key Lab Computat Intelligence & Intellige Beijing 100124 Peoples R China Beijing Univ Technol Beijing Lab Smart Environm Protect Beijing 100124 Peoples R China Beijing Univ Technol Beijing Inst Artificial Intelligence Beijing 100124 Peoples R China

ISBN: (纸本)9781665486415

In this paper, the intelligent and online value iteration (VI) algorithms are developed to solve the optimal control problem for nonlinear discrete-time systems. First, the intelligent VI algorithm combines the advantages of traditional VI initialized by the zero cost function and stabilizing VI initialized by the admissible control policy. The traditional VI is easy to implement and can provide the initial admissible control policy for the stabilizing VI. Meanwhile, stabilizing VI can guarantee all control policies are admissible. Second, based on the concept of the attraction domain, an online value iteration algorithm is proposed to regulate the closed-loop system by using immature control policies rather than the fixed optimal control policy. It ensures that the state trajectory converges to the origin of the attraction domain. Finally, simulations are carried out and the results show the effectiveness of the two new VI algorithms.

关键词： adaptive dynamic programming asymptotic stability attraction domain online value iteration

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Fault-tolerant Pursuit-evasion Games for Quadrotor Helicopters Based on a Fully-actuated System Approach 3

Fault-tolerant Pursuit-evasion Games for Quadrotor Helicopte...

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3rd Conference on Fully Actuated System Theory and Applications (FASTA)

作者： Xu, Jianfeng Xu, Yuhang Jiang, Bin Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 211106 Peoples R China

ISBN: (纸本)9798350373707;9798350373691

This paper proposes a fault-tolerant control scheme for the pursuit-evasion (PE) game between two quadrotor helicopters. Initially, the quadrotor helicopters are modeled based on the high-order fully-actuated (HOFA) approach, allowing to eliminate the nonlinear dynamics of the quadrotor helicopters. Subsequently, the game strategies for the pursuer, faults and the evader are designed. The optimal control rate is obtained by solving the Hamilton-Jacobi-Isaacs (HJI) equation using a critic neural network. It is demonstrated that the proposed fault-tolerant control (FTC) law assists the fault-affected pursuer in successfully capturing the evader and estimating the upper bound of bias fault. Finally, the effectiveness of the fault-tolerant PE game control scheme is validated through numerical simulations.

关键词： Fully-actuated systems adaptive fault-tolerant control pursuit-evasion game quadrotors adaptive dynamic programming

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Event-Triggered Robust Control for Fully Cooperative Games of Uncertain Nonlinear Multi-player Systems 19

Event-Triggered Robust Control for Fully Cooperative Games o...

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19th IEEE International Conference on Mechatronics and Automation (IEEE ICMA)

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

ISBN: (数字)9781665408530

ISBN: (纸本)9781665408530;9781665408523

This paper develops an adaptive dynamic programming-based event-triggered robust control method for fully cooperative games of nonlinear multi-player systems with uncertainties. By designing a modified value function, the robust control problem is transformed into a fully cooperative (FC) game of the nominal system. Then, a critic neural network is constructed to solve the Hamilton-Jacobi-Bellman equation of the FC game, and the optimal control laws of all players are obtained. Under the event-triggered mechanism, the designed event-triggering condition is employed to determine whether the control laws should be updated or not. The developed method saves computation and communication resources, since both the updating frequency of control laws and the communication between controllers and actuators are reduced. Furthermore, the closed-loop system is proven to be uniformly ultimately bounded thanks to the Lyapunov's direct method. A numerical example is employed to verify the effectiveness of the developed method.

关键词： adaptive dynamic programming Event-triggered control Fully cooperative games Uncertain multiplayer systems

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Direct adaptive Control for Stochastic Systems with Risk-Sensitive Indices * 22

Direct Adaptive Control for Stochastic Systems with Risk-Sen...

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22nd World Congress of the International Federation of Automatic Control (IFAC)

作者： Qiao, Nan Li, Tao East China Normal Univ Shanghai Key Lab Pure Math & Math Practice Sch Math Sci 5005 South Lianhua Rd Shanghai 200241 Peoples R China NYU Shanghai NYU ECNU Inst Math Sci 3663 Zhongshan Rd North Shanghai 200062 Peoples R China

ISBN: (纸本)9781713872344

We propose a direct adaptive control law based on the adaptive dynamic programming (ADP) algorithm for continuous-time stochastic linear systems with partially unknown system dynamics and infinite horizon quadratic risk-sensitive indices. A control design methodology is employed to iteratively solve the generalized algebraic Riccati equation by using the online information of the state and input, and to directly learn the optimal control law. We prove the convergence of the online ADP algorithm and show that the direct adaptive control law approximates the optimal control law as time goes on. Finally, a numerical simulation example is presented to demonstrate the effectiveness of our algorithm. Copyright (c) 2023 The Authors.

关键词： adaptive dynamic programming generalized algebraic Riccati equation risk-sensitive control direct adaptive control.

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Decentralized Control for a Class of Nonlinear Interconnected Systems Using Integral Policy Iteration 11

Decentralized Control for a Class of Nonlinear Interconnecte...

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11th World Congress on Intelligent Control and Automation

作者： Li, Chao Liu, Derong Li, Hongliang Wang, Ding Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

ISBN: (纸本)9781479958252

In this paper, we establish a decentralized control law to stabilize a class of nonlinear interconnected large-scale systems using a neural-network-based online model-free integral policy iteration algorithm. The model-free approach can solve the decentralized control problem for the interconnected system which has unknown dynamics. The stabilizing decentralized control strategy is derived based on the optimal control policies of the isolated subsystems. The online model-free integral policy iteration algorithm is developed to solve the optimal control problems for the isolated subsystems with unknown system dynamics. The actor-critic technique and the least squares implementation method are used to obtain the optimal control policies. A simulation example is given to verify the applicability of the decentralized control strategy.

关键词： adaptive dynamic programming decentralized control optimal control policy iteration neural networks

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Decentralized Robust Optimal Control for Modular Robot Manipulators Based on Zero-Sum Game with ADP 16th

Decentralized Robust Optimal Control for Modular Robot Manip...

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16th International Symposium on Neural Networks (ISNN)

作者： Dong, Bo An, Tianjiao Zhou, Fan Wang, Shenquan Jiang, Yulian Liu, Keping Liu, Fu Lu, Huiqiu Li, Yuanchun Changchun Univ Technol Dept Control Sci & Engn Changchun Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Jilin Univ Dept Control Sci & Engn Changchun Peoples R China

ISBN: (纸本)9783030228088;9783030228071

In this paper, a decentralized robust zero-sum optimal control method is proposed for modular robot manipulators (MRMs) based on the adaptive dynamic programming (ADP) approach. The dynamic model of MRMs is formulated via joint torque feedback (JTF) technique that is deployed for each joint module, in which the local dynamic information is used to design the model compensation controller. An uncertainty decomposition-based robust control is developed to compensate the model uncertainties, and then the robust optimal control problem of MRMs with uncertain environments can be transformed into a two-player zero-sum optimal control one. According to the ADP algorithm, the Hamilton-Jacobi-Isaacs (HJI) equation is solved by constructing actioncritic neural networks (NNs) and then the approximate optimal control policy derivation is possible. Experiments are conducted to verify the effectiveness of the proposed method.

关键词： Modular robot manipulators adaptive dynamic programming Optimal control Zero-sum game

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Analyses for Optimal Control of Discrete Time-Delay Systems Based on ADP Algorithm with Finite-Horizon Performance Index 28

Analyses for Optimal Control of Discrete Time-Delay Systems ...

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28th Chinese Control and Decision Conference

作者： Song, Ruizhuo Xing, Shi Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China

ISBN: (纸本)9781467397148

An optimal control scheme is obtained for a class of nonlinear discrete time-delay systems in this paper. Based on adaptive finite-horizon dynamic programming (ADP) algorithm, we focus on two different initial control conditions, and then the value iteration method is used to get the iterative performance index function and iterative control. Two neural networks, which are named as critic network and action network are used to approximate the performance index function and compute the optimal control policy. The iteration termination error and the performance index function are analyzed based on different initial states. In the simulation study, we analysis the control results of different initial states and different maximal iterative steps.

关键词： adaptive dynamic programming optimal control nonlinear systems approximate dynamic programming

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Mobile Robot Control Based on Hybrid Neuro-Fuzzy Value Gradient Reinforcement Learning

Mobile Robot Control Based on Hybrid Neuro-Fuzzy Value Gradi...

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International Joint Conference on Neural Networks (IJCNN)

作者： Al-Dabooni, Seaar Wunsch, Donald Missouri Univ Sci & Technol Dept Elect & Comp Engn Rolla MO 65401 USA

ISBN: (纸本)9781509061822

This paper uses value gradient learning (VGL) to track a reference trajectory under uncertainties, by computing the optimal left and right torque values for a nonholonomic mobile robot. VGL is a high-performance algorithm in adaptive dynamic programming (ADP). Here, it is used as a critic function after fitting a first-order Sugeno fuzzy neural network (FNN) structure to critic and actor networks. Moreover, this work handles the impacts of unmodeled bounded disturbances with various friction values. The simulation is introduced to compare two approaches. The first uses an actor network that confirms the ability of the mobile robot dynamic model to follow a desired trajectory. This approach demonstrates a significant enhancement of the robot's capability to absorb unstructured disturbance signals and friction effects. The second type of results use a critic-optimal-control approach, calculating the optimal control signal for the affine dynamic model of the robot. This completely removes the actor network to exploit reduced computational complexity with faster responses. The simulation is introduced to compare both cases.

关键词： Nonholonomic dynamic mobile robot fuzzy neural network adaptive dynamic programming

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