Design of a Feedforward-Feedback Controller for a Piezoelectric-Driven Mechanism to Achieve High-Frequency Nonperiodic Motion Tracking

Abstract

Piezoelectric-driven mechanisms have several advantages like high stiffness, rapid response, and good resolution. Therefore, they are widely used for many micro/nano trajectory-tracking applications. However, the existence of the hysteretic nonlinearity behavior makes it challenging to use in practice. In addition, the hysteresis changes with frequency and is dependent on environmental parameters like temperature and load. Finding a method that can track both continuous periodic and nonperiodic motion under wide frequency range with high precision is nontrivial. In this study, a feedforward-feedback control strategy is proposed to bridge this gap, where a direct inverse rate-dependent Prandtl–Ishlinskii model based on radial basis function neural network to compensate rate-dependent hysteresis and a proportional-integral controller with an inner-loop disturbance observer to further attenuate tracking error (caused by the imperfect modeling, unknown lumped disturbance). The proposed method can perform a wide-bandwidth tracking control of periodic and nonperiodic motion of a piezoelectric-driven mechanism. Experiments are then conducted to demonstrate the capability of the proposed controller.