Modeling of hysteresis in piezoelectric actuators using neural networks

For the application of neural networks to the approximation of hysteresis which is characterized of multi-valued mapping and non-smooth nonlinearities, a novel modeling technique based on a transformation of one-to-one mapping is proposed in this paper. In this method, a special hysteretic operator is introduced to describe the change tendency of the hysteresis with regard to its input. Then an expanded input space is constructed for hysteresis with the introduction of such hysteretic operator, on which the multi-valued hysteresis is decomposed into a one-to-one mapping. Thus, neural networks model for hysteresis is derived, avoiding the calculation of the gradient of hysteresis. Subsequently, for approximation of rate-dependent hysteresis in piezoelectric actuators which is caused by the dynamic voltage excitations, a hybrid model, i.e. the dynamic extension of the proposed neural hysteresis submodel is developed. In the model, a linear dynamic block is introduced in series with the proposed neural model to allow for rate-dependent dynamics of the piezoelectric actuator simultaneously. Also the corresponding optimization algorithm by use of the modified Levenberg–Marquarqt (MLM) method is given. Finally, the experimental validation results of applying both the proposed neural hysteresis model and hybrid model to a piezoelectric actuator are presented.