A model based compensator for rate-dependent hysteresis in piezoelectric actuators

A feedforward compensator for rate-dependent hysteresis in piezoelectric actuators (PEA) is proposed. In this method, a model with parallel structure is proposed to approximate both hysteretic behavior and rate-dependent feature of the PEA. In the model, the rate-independent hysteretic performance is approximated by a neural network based on the so called expanded input space method, while the rate-dependent behavior of the hysteresis is approximated by a network with the sum of weighted first-order difference operators. To transform the multi-valued mapping of the hysteresis into a one-to-one mapping, the expanded input space with a hysteretic operator is constructed. Then, the corresponding neural inverse sub-model is obtained. After that, a compensator is constructed to compensate for the effect of the rate-dependent hysteresis in the PEA. Finally, the experimental results of applying the proposed method to a piezoelectric actuator are presented.