A Generalized Policy Iteration Adaptive Dynamic Programming Algorithm for Optimal Control of Discrete-Time Nonlinear Systems with Actuator Saturation

被引：1

作者：

Lin, Qiao ^{[1
]}

Wei, Qinglai ^{[1
]}

Zhao, Bo ^{[1
]}

机构：

[1] Univ Chinese Acad Sci, Beijing 100190, Peoples R China

来源：

ADVANCES IN NEURAL NETWORKS, PT II | 2017年 / 10262卷

基金：

中国国家自然科学基金;

关键词：

Adaptive dynamic programming; Neural network; Optimal control; Saturating actuators; TRACKING CONTROL SCHEME; LINEAR-SYSTEMS;

D O I：

10.1007/978-3-319-59081-3_8

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this study, a nonquadratic performance function is introduced to overcome the saturation nonlinearity in actuators. Then a novel solution, generalized policy iteration adaptive dynamic programming algorithm, is applied to deal with the problem of optimal control. To achieve this goal, we use two neural networks to approximate control vectors and performance index function. Finally, this paper focuses on an example simulated on Matlab, which verifies the excellent convergence of the mentioned algorithm and feasibility of this scheme.

引用

页码：60 / 65

页数：6

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