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This paper deals with the control of dynamic systems preceded by an unknown hysteresis, where the hysteresis is modeled by a differential equation. By exploiting properties of the differential equation, a recurrent neural network is developed to construct a hysteresis inverse, which can compensate the affection of the input hysteresis. By using a traditional PD controller, the whole system tracks a desired trajectory within a specified precision. Simulation results verified the proposed schemes.
Neural Networks and Signal Processing, 2003. Proceedings of the 2003 International Conference on (Volume:1 )
Date of Conference: 14-17 Dec. 2003