Identification of inverse generalized asymmetric prandtl-ishlinskii model for compensation of hysteresis nonlinearities

Classic Preisach model can precisely describe the hysteresis of piezoelectric stack actuators, but its model identification is relatively complicated. Neural network is easy to be identified with available training algorithm, but it cannot directly describe the multi-valued mapping of hysteresis. A neural-Preisach model was proposed for modeling and control of piezoelectric stack actuators. The neural-Preisach model inherits the advantages of Preisach model and neural network, which can describe the hysteresis and update parameters by training algorithm. A feedforward controller was designed with the inverse neural-Preisach model, and then experiments of tracking control were performed to validate the effectiveness of the neural-Preisach model. The maximal error, in case of feedforward and PID controller, is reduced by 83.97%, comparing with the case without control. This indicates that control accuracy with hysteresis compensation is greatly improved compared to that without hysteresis compensation.

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Identification of inverse generalized asymmetric prandtl-ishlinskii model for compensation of hysteresis nonlinearities

Cited by 2 publications

References 13 publications

A Model Inversion Procedure for Control of Nonlinear Series Elastic Actuators

A Model Inversion Procedure for Control of Nonlinear Series Elastic Actuators

Hysteresis modeling and tracking control for piezoelectric stack actuators using neural-Preisach model

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