自动化学报2005，Vol.31Issue(1)：13-18,6.
Approximate Dynamic Programming for Self-Learning Control
Approximate Dynamic Programming for Self-Learning Control
摘要
Abstract
This paper introduces a self-learning control approach based on approximate dynamic programming. Dynamic programming was introduced by Bellman in the 1950's for solving optimal control problems of nonlinear dynamical systems. Due to its high computational complexity, the applications of dynamic programming have been limited to simple and small problems. The key step in finding approximate solutions to dynamic programming is to estimate the performance index in dynamic programming. The optimal control signal can then be determined by minimizing (or maximizing) the performance index. Artificial neural networks are very efficient tools in representing the performance index in dynamic programming. This paper assumes the use of neural networks for estimating the performance index in dynamic programming and for generating optimal control signals, thus to achieve optimal control through self-learning.关键词
Approximate dynamic programming/learning control/neural networksKey words
Approximate dynamic programming/learning control/neural networks分类
信息技术与安全科学引用本文复制引用
Derong Liu..Approximate Dynamic Programming for Self-Learning Control[J].自动化学报,2005,31(1):13-18,6.基金项目
Supported by the National Science Foundation (U.S.A.) under Grant ECS-0355364 （U.S.A.）
