Title

Neural Network Solution For Suboptimal Control Of Non-Holonomic Chained Form System

Keywords

Constrained input systems; finite-horizon optimal control; Hamilton— Jacobi—Bellman; neural network control; non-holonomic systems

Abstract

In this paper, we develop fixed-final time nearly optimal control laws for a class of non-holonomic chained form systems by using neural networks to approximately solve a Hamilton—Jacobi—Bellman equation. A certain time-folding method is applied to recover uniform complete controllability for the chained form system. This method requires an innovative design of a certain dynamic control component. Using this time-folding method, the chained form system is mapped into a controllable linear system for which controllers can systematically be designed to ensure exponential or asymptotic stability as well as nearly optimal performance. The result is a neural network feedback controller that has time-varying coefficients found by a priori offline tuning. The results of this paper are demonstrated in an example. © 2009, The Institute of Measurement and Control. All rights reserved.

Publication Date

1-1-2009

Publication Title

Transactions of the Institute of Measurement & Control

Volume

31

Issue

6

Number of Pages

475-494

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1177/0142331208094043

Socpus ID

72149086016 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/72149086016

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