Natural heuristic dynamic programming for dynamic systems control

被引:0
|
作者
Tang, KW [1 ]
Rastegar, J [1 ]
机构
[1] SUNY Stony Brook, Dept Elect & Comp Engn, Stony Brook, NY 11794 USA
来源
ARTIFICIAL INTELLIGENCE IN REAL-TIME CONTROL 1998 | 1999年
关键词
neural control; dynamic systems; learning control; neural networks; backpropagation;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Heuristic Dynamic Programming (HDP) is the simplest kind of Adaptive Critic (Werbos, 1992). It can be used to maximize or minimize any utility function, such as total energy or trajectory error, of a system over time in a noisy environment. This article proposes a new version of HDP, called NHDP (Natural Heuristic Dynamic Programming). This new version incorporates basic HDP algorithm with the following features:(i) use of Trajectory Pattern Method to guarantee smoothness of trajectory and control signals; (ii) use multiple critic networks to localize effect of each parameter mimicking the natural biological model of human brain; and (iii) allow the controller to learn from slow to fast motion, analogous to the natural learning behavior of humans. A simple dynamic system is used to illustrate NHDP. Copyright (C) 1998 IFAC.
引用
收藏
页码:17 / 22
页数:6
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