A Neural-Network-Based Controller for Piezoelectric-Actuated Stick–Slip Devices

Piezoelectric-actuated stick-slip device (PASSD) is a highly promising equipment that is composed of one end-effector, one piezoelectric actuator (PEA) and one driving object adhered to the PEA. Since the end-effector can slip on the surface of the driving object, the PASSD is capable of realizing the macrolevel motion with the microlevel precision. Due to the following two reasons: the complicated relative motion between the end-effector and the driving object, and the inherent hysteresis nonlinearity in the PEA, the ultraprecision displacement control of the end-effector of PASSDs raises a real challenge, which is rarely reported in the literature. Toward solving this challenge, a neural-network-based controller is proposed in this paper. First, a neural-network-based model is proposed to capture the relative motion between the end-effector and the driving object. Second, a neural-network-based inversion model is developed to online calculate the desired position of the PEA under the predesigned reference of the end-effector. Third, a dynamic linearized neural-network-based model predictive control method, which can effectively handle the hysteresis nonlinearity, is employed to implement the displacement control of the PEA, which finally results in an overall high-precision controller of the end-effector. Finally, a PASSD prototype has been implemented and tested through experimental studies to demonstrate the effectiveness of the proposed approach.