A neural-network-based hysteresis model for piezoelectric actuators

In this paper, a new neural network based hysteresis model is presented. First of all, a variable-order hysteretic operator (VOHO) is proposed via the characteristics of the motion point trajectory. Based on the VOHO, a basic hysteresis model (BHM) is constructed. Next, the input space is expanded from one-dimension to two-dimension based on the BHM so that the method of neural networks can be used to approximate the mapping between the expanded input space and the output space. Finally, three experiments involved with a piezoelectric actuator were implemented to validate the neural hysteresis model. The results of the experiments suggest that the proposed approach is effective.In this paper, a new neural network based hysteresis model is presented. First of all, a variable-order hysteretic operator (VOHO) is proposed via the characteristics of the motion point trajectory. Based on the VOHO, a basic hysteresis model (BHM) is constructed. Next, the input space is expanded from one-dimension to two-dimension based on the BHM so that the method of neural networks can be used to approximate the mapping between the expanded input space and the output space. Finally, three experiments involved with a piezoelectric actuator were implemented to validate the neural hysteresis model. The results of the experiments suggest that the proposed approach is effective.