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Fixed-Final Time Constrained Optimal Control of Nonlinear Systems Using Neural Network HJB Approach

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2 Author(s)
Tao Cheng ; Automation and Robotics Research Institute, The University of Texas, Arlington, TX 76118, USA. Tel./fax: +1 817 272 5938. E-mail: chhengtao@arri.uta.edu ; Frank L. Lewis

Fixed-final time constrained input optimal control laws using neural networks to solve Hamilton-Jacobi-Bellman (HJB) equations for general affine in the input nonlinear systems are proposed. A neural network is used to approximate the time-varying cost function using the method of least-squares on a pre-defined region and hence solve the HJB. The result is a neural network nearly optimal constrained feedback controller that has time-varying coefficients found by a priori offline tuning. The results of this paper are demonstrated on an example

Published in:

Proceedings of the 45th IEEE Conference on Decision and Control

Date of Conference:

13-15 Dec. 2006