DocumentCode :
1892688
Title :
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
Link To Document :
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