Accession Number:

ADA455430

Title:

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

Descriptive Note:

Conference paper

Corporate Author:

TEXAS UNIV AT ARLINGTON AUTOMATION AND ROBOTICS INST

Personal Author(s):

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

Report Date:

2006-06-01

Pagination or Media Count:

7.0

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.

Descriptors:

• *NEURAL NETS
• *NONLINEAR SYSTEMS
• *CONTROL THEORY
• SYMPOSIA
• DYNAMIC PROGRAMMING
• DIFFERENTIAL EQUATIONS
• CONVERGENCE
• OPTIMIZATION

Subject Categories:

• Operations Research
• Cybernetics
• Theoretical Mathematics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE