Abstract

In this paper, the nearly optimal solution for discrete-time (DT) affine nonlinear control systems in the presence of partially unknown internal system dynamics and disturbances is considered. the approach is based on successive approximate solution of the Hamilton-Jacobi-Isaacs (HJI) equation, which appears in optimal control. Successive approximation approach for updating control input and disturbance for DT nonlinear affine systems are proposed. Moreover, sufficient conditions for the convergence of the approximate HJI solution to the saddle-point are derived, and an iterative approach to approximate the HJI equation using a neural network (NN) is presented. Then, the requirement of full knowledge of the internal dynamics of the nonlinear DT system is relaxed by using a second NN online approximator. the result is a closed-loop optimal NN controller via offline learning. Numerical example is provided illustrating the effectiveness of the approach. © 2010 IEEE.

Department(s)

Electrical and Computer Engineering

Second Department

Computer Science

Keywords and Phrases

Generalized Hamilton-Jacobi-Isaacs; Neural networks; Nonlinear Discrete-time systems; Optimal control

International Standard Book Number (ISBN)

978-142446917-8

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.

Publication Date

01 Jan 2010

Share

 
COinS