Adaptive Constrained Optimal Control Design for Data-Based Nonlinear Discrete-Time Systems With Critic-Only Structure
Luo, Biao1; Liu, Derong2; Wu, Huai-Ning3
2018-06-01
发表期刊IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
卷号29期号:6页码:2099-2111
文章类型Article
摘要

Reinforcement learning has proved to be a powerful tool to solve optimal control problems over the past few years. However, the data-based constrained optimal control problem of nonaffine nonlinear discrete-time systems has rarely been studied yet. To solve this problem, an adaptive optimal control approach is developed by using the value iteration-based Q-learning (VIQL) with the critic-only structure. Most of the existing constrained control methods require the use of a certain performance index and only suit for linear or affine nonlinear systems, which is unreasonable in practice. To overcome this problem, the system transformation is first introduced with the general performance index. Then, the constrained optimal control problem is converted to an unconstrained optimal control problem. By introducing the action-state value function, i.e., Q-function, the VIQL algorithm is proposed to learn the optimal Q-function of the data-based unconstrained optimal control problem. The convergence results of the VIQL algorithm are established with an easy-to-realize initial condition Q((0))(x, a) >= 0. To implement the VIQL algorithm, the critic-only structure is developed, where only one neural network is required to approximate the Q-function. The converged Q-function obtained from the critic-only VIQL method is employed to design the adaptive constrained optimal controller based on the gradient descent scheme. Finally, the effectiveness of the developed adaptive control method is tested on three examples with computer simulation.

关键词Adaptive Control Adaptive Dynamic Programming Constraints Critic-only Data-based Optimal Control Q-learning
WOS标题词Science & Technology ; Technology
DOI10.1109/TNNLS.2017.2751018
关键词[WOS]Optimal Tracking Control ; H-infinity Control ; Dynamic-programming Algorithm ; Linear-systems ; Unknown Dynamics ; Policy Iteration ; Neural-networks ; Control Scheme ; Equation ; Update
收录类别SCI
语种英语
项目资助者National Natural Science Foundation of China(61503377 ; 61533017 ; 61625302 ; 61473011 ; U1501251)
WOS研究方向Computer Science ; Engineering
WOS类目Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号WOS:000432398300005
引用统计
被引频次:3[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/22045
专题复杂系统管理与控制国家重点实验室_平行控制
作者单位1.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing 100190, Peoples R China
2.Guangdong Univ Technol, Sch Automat, Guangzhou 510006, Guangdong, Peoples R China
3.Beihang Univ, Sci & Technol Aircraft Control Lab, Beijing 100191, Peoples R China
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Luo, Biao,Liu, Derong,Wu, Huai-Ning. Adaptive Constrained Optimal Control Design for Data-Based Nonlinear Discrete-Time Systems With Critic-Only Structure[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2018,29(6):2099-2111.
APA Luo, Biao,Liu, Derong,&Wu, Huai-Ning.(2018).Adaptive Constrained Optimal Control Design for Data-Based Nonlinear Discrete-Time Systems With Critic-Only Structure.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,29(6),2099-2111.
MLA Luo, Biao,et al."Adaptive Constrained Optimal Control Design for Data-Based Nonlinear Discrete-Time Systems With Critic-Only Structure".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 29.6(2018):2099-2111.
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