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Neural network solution for suboptimal control of non-holonomic chained form system

¹ School of Mechanical Engineering and Automation, Beihang University, Beijing 100083, China
² School of Automation, Beijing University of Posts and Telecommunications, Beijing 100876, China
³ Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL 32816, USA
⁴ Automation and Robotics Research Institute, The University of Texas at Arlington, TX 76118, USA

^* To whom correspondence should be addressed. E-mail: tao2000000{at}hotmail.com.

	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.

First published on August 6, 2009
Transactions of the Institute of Measurement and Control 2009, doi:10.1177/0142331208094043


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