Nearly optimal state feedback control of constrained nonlinear systems using a neural networks HJB approach

被引：33
|
作者：
Abu-Khalaf, M [1 ]
Lewis, FL [1 ]
机构：
[1] Univ Texas, Automat Robot Res Inst, Arlington, TX 76118 USA
来源：
ANNUAL REVIEWS IN CONTROL | 2004年 / 28卷 / 02期
基金：
美国国家科学基金会;
关键词：
actuator saturatiom; state constraints; minimum-time control; neural network; optimal control;
D O I：
10.1016/j.arcontrol.2004.07.002
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, we treat constrained optimization of nonlinear systems. A rigorous solution method to obtain nearly optimal state feedback control that takes into consideration. actuator saturation, state space constraints. and minimum-Lime control requirement is preserved. The constraints are encoded into the optimization formulation through special nonquadriatic functionals. The associated Hamilton-Jacobi-Bellman (HJB) equation is then solved successively. Nonlinear approximating networks are used to obtain an approximate closed form solution of the value function of the HJB equation, which is then used to obtain a state feedback controller. The solution is carried over a compact set of the asymptotic stability region of an initial stabilizing control. (C) 2004 Elsevier Ltd. All rights reserved.
引用
收藏
页码：239 / 251
页数：13
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