A neural network solution for fixed-final time optimal control of nonlinear systems

被引：0

作者：

Cheng, Tao ^{[1
]}

Lewis, Frank L. ^{[1
]}

Abu-Khalaf, Murad ^{[1
]}

机构：

[1] Univ Texas, Automat & Robot Res Inst, Arlington, TX 76118 USA

来源：

PROCEEDINGS OF 2006 MEDITERRANEAN CONFERENCE ON CONTROL AND AUTOMATION, VOLS 1 AND 2 | 2006年

关键词：

finite-horizon optimal control; Hamilton-Jacobi-Bellman; neural network control;

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：

We consider the use of neural networks and Hamilton-Jacobi-Bellman equations towards obtaining fixed-final time optimal control laws in the input nonlinear systems. The method is based on Kronecker matrix methods along with neural network approximation over a compact set to solve a time-varying Hamilton-Jacobi-Bellman equation. The result is a neural network feedback controller that has time-varying coefficients found by a priori offline tuning. Convergence results are shown. The results of this paper are demonstrated on two examples.

引用

页码：588 / +

页数：3

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