Study on the control method about improving piezoelectric actuated photoelectric precision tracking system

Abstract

Fast steering mirrors (FSM) driven by Piezoelectric transducer (PZT) are widely used in various precision stable tracking systems. Aiming to counteract the hysteresis and non-linear interference in PZT, this work applies a radial basis function (RBF) neural network to approximate its nonlinearity. Adaptive backstepping sliding mode (ABSM) controller combinewith a sliding mode control method and backstepping control is designed. Combining the characteristics of PZT and voice coil motors (VCM), the FSM driven by VCM is designed as the power sub-system to ensure that the large-angle deflection of the FSM can match a wider field of view. The FSM driven by PZT is designed as a correction sub-system, which can adjust the system error within a small range. Finally, the power sub-system and the correction sub-system are combined into a two-level precision tracking system. The simulation results show that the maximum steady-state error of the system is about 15 μrad, and the root mean square error is about 10 μrad. Compared with the traditional PI controller, the error is reduced by about 75%, the response speed is up to 10 ms, and the output is smooth without some serious viberation.