Modeling and control methodology for an XYZ micro manipulator

Abstract

This paper presents the modeling and control methodology for a piezoactuated compliant XYZ manipulator toward precision positioning. The manipulator was fabricated using a wire electrical discharge machining technique, and the system identification was conducted to obtain the dynamic model based on the frequency response. To reduce the effects of hysteresis, creep, and external disturbances, a feedforward/feedback hybrid controller is proposed, which contains a dynamic dependent Prandtl-Ishlinskii (DDPI) hysteresis model and a novel sliding mode controller. The DDPI hysteresis model has excellent modeling accuracy at high operating frequencies with consideration of the dynamic characteristics of the micromanipulator. The novel sliding mode controller integrated with uncertainty and disturbance estimation (SMCUDE) technique is developed, which has the advantages of fast response, strong robustness, and resistance to chattering. The performance of the DDPI hysteresis model and the novel sliding mode controller is validated and compared using experimental tests. The experimental results indicate that the DDPI model provides better positioning accuracy than the traditional Prandtl-Ishlinskii (P-I) model and the rate-dependent P-I model, and furthermore, the SMCUDE controller can improve the response speed without loss of stability, which demonstrates that precision positioning operations can be implemented by the developed manipulator using the proposed control strategy.

Fractional order zero phase error tracking control of a novel decoupled 2-DOF compliant micro-positioning stage

“…Meanwhile, the feedforward controller is adopted to realize a zero-phase error tracking control (ZPETC) [8]. Tian et al presented a hybrid feedforward/feedback controller, which incorporates a dynamic-dependent proportionalintegral hysteresis model and a novel sliding mode controller with uncertainty and disturbance estimation technique [9]. Jian et al concentrated on exploiting a practical iterative learning control algorithm to further lower tracking error [10].…”