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		Tao CHENG,Frank L. LEWIS.[en_title][J].Control Theory and Technology,2007,5(1):1~11.[Copy]

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TaoCHENG,FrankL.LEWIS
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Received:March 23, 2006Revised:October 12, 2006

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Neural network solution for finite-horizon H-infinity constrained optimal control of nonlinear systems

Tao CHENG, Frank L. LEWIS

(Automation and Robotics Research Institute, University of Texas, Arlington TX 76118, USA)

Abstract:

In this paper, neural networks are used to approximately solve the finite-horizon constrained input H-infinity state feedback control problem. The method is based on solving a related Hamilton-Jacobi-Isaacs equation of the corresponding finite-horizon zero-sum game. The game value function is approximated by a neural network with time-varying weights. It is shown that the neural network approximation converges uniformly to the game-value function and the resulting almost optimal constrained feedback controller provides closed-loop stability and bounded L2 gain. The result is an almost optimal H-infinity feedback controller with time-varying coefficients that is solved a priori off-line. The effectiveness of the method is shown on the Rotational. Translational Actuator benchmark nonlinear control problem.

Key words: Constrained input system Hamilton-Jacobi-Isaacs H-infinity control Finite-horizon zero-sum games Neural network control