Title

Real-Time Nonlinear Optimal Control using Neural Networks

Abstract

In this paper, a neural network based controller which optimizes a finite horizon quadratic cost function is developed for a class of nonlinear systems. The controller converges to its optimal value real-time eliminating the need for a priori knowledge of the nonlinearity and the initial conditions. The method makes use of the optimality conditions obtained from the Hamiltonian directly. These conditions are realized by a series of neural networks which converge to the optimal control iteratively in real-time. A nonlinear system to demonstrate its applicability is also included.

Meeting Name

American Control Conference (1994: Jun. 29-Jul. 1, Baltimore, MD)

Department(s)

Electrical and Computer Engineering

Sponsor(s)

National Science Foundation (U.S.)
United States. Army Research Office
University of Missouri--Rolla. Intelligent Systems Center

Comments

This work has been partially supported by NSF Grant NSF ECS-9309486, by ARO Grant DAAHO4-93-G-0214, and by the Intelligent Systems Center of the University of Missouri-Rolla.

Keywords and Phrases

Computer simulation; Control nonlinearities; Control theory; Finite element method; Mathematical models; Nonlinear control systems; Optimal control systems; Optimization; Real time systems; Dynamical systems; Finite horizon quaratic cost function; Optimality conditions; Real time nonlinear optimal control; Neural networks

International Standard Book Number (ISBN)

0-780317831

International Standard Serial Number (ISSN)

0743-1619

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 1994 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

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