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
收藏
页码：588 / +
页数：3
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