A Neural Network Solution for Fixed-Final Time Optimal Control of Nonlinear Systems

Author

Cheng, Tao ; Lewis, Frank L. ; Abu-Khalaf, Murad

Author_Institution

Autom. & Robotics Res. Inst., Texas Univ., Arlington, TX

fYear

2006

fDate

28-30 June 2006

Firstpage

1

Lastpage

5

Abstract

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

Keywords

Jacobian matrices; neurocontrollers; nonlinear control systems; time optimal control; time-varying systems; Kronecker matrix methods; fixed-final time optimal control; neural network feedback controller; nonlinear systems; offline tuning; time-varying Hamilton-Jacobi-Bellman equations; Adaptive control; Control systems; Differential equations; Neural networks; Nonlinear control systems; Nonlinear dynamical systems; Nonlinear equations; Nonlinear systems; Optimal control; Robotics and automation; Finite-horizon optimal control; Hamilton-Jacobi-Bellman; Neural Network control;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Automation, 2006. MED '06. 14th Mediterranean Conference on

Conference_Location

Ancona

Print_ISBN

0-9786720-1-1

Electronic_ISBN

0-9786720-0-3

Type

conf

DOI

10.1109/MED.2006.328821

Filename

4124940