Data-Based Optimal Tracking Control of Nonaffine Nonlinear Discrete-Time Systems

被引：0

作者：

Luo, Biao ^{[1
]}

Liu, Derong ^{[2
]}

Huang, Tingwen ^{[3
]}

Li, Chao ^{[1
]}

机构：

[1] Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China

[2] Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China

[3] Texas A&M Univ Qatar, POB 23874, Doha, Qatar

来源：

NEURAL INFORMATION PROCESSING, ICONIP 2016, PT IV | 2016年 / 9950卷

关键词：

Optimal tracking control; Data-based; Q-learning; Critic-only; H-INFINITY CONTROL; LINEAR-SYSTEMS; CONTROL SCHEME; APPROXIMATION; ITERATION;

D O I：

10.1007/978-3-319-46681-1_68

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The optimal tracking control problem of nonaffine nonlinear discrete-time systems is considered in this paper. The problem relies on the solution of the so-called tracking Hamilton-Jacobi-Bellman equation, which is extremely difficult to be solved even for simple systems. To overcome this difficulty, the data-based Q-learning algorithm is proposed by learning the optimal tracking control policy from data of the practical system. For its implementation purpose, the critic-only neural network structure is developed, where only critic neural network is required to estimate the Q-function and the least-square scheme is employed to update the weight of neural network.

引用

页码：573 / 581

页数：9

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