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

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3 Author(s)
Tao Cheng ; Univ. of Texas at Arlington, Fort Worth ; Lewis, F.L. ; Abu-Khalaf, M.

In this paper, fixed-final time-constrained optimal control laws using neural networks (NNS) to solve Hamilton-Jacobi-Bellman (HJB) equations for general affine in the constrained nonlinear systems are proposed. An NN is used to approximate the time-varying cost function using the method of least squares on a predefined region. The result is an NN nearly -constrained feedback controller that has time-varying coefficients found by a priori offline tuning. Convergence results are shown. The results of this paper are demonstrated in two examples, including a nonholonomic system.

Published in:

Neural Networks, IEEE Transactions on  (Volume:18 ,  Issue: 6 )

Date of Publication:

Nov. 2007

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