Planar positioning systems are widely utilized in micro and nano applications.The challenges in modeling and control of XYΘflexure-based mechanisms include hysteresis of the piezoelectric actuators,couplings among th...Planar positioning systems are widely utilized in micro and nano applications.The challenges in modeling and control of XYΘflexure-based mechanisms include hysteresis of the piezoelectric actuators,couplings among the input axes,and coupled linear and angular motions of the end effector.This paper presents an inverse hysteresis-coupling hybrid model to account for such hysteresis and couplings.First,a specially designed kinematic chain is adopted to transfer the pose of the end effector into the linear motions at three prismatic joints.Second,an inverse hysteresis-coupling hybrid model is developed to linearize and decouple the system via a multilayer feedforward neural network.A fractional-order PID controller is also integrated to improve the motion accuracy of the overall system.Experimental results demonstrate that the proposed method can accurately control the motion of the end effector with improved accuracy and robustness.展开更多
基金supported in part by the Open Fund of State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment,Guangdong University of Technology(Grant No.JMDZ2021007)in part by the Guangdong International Cooperation Program of Science and Technology(Grant No.2022A0505050078).
文摘Planar positioning systems are widely utilized in micro and nano applications.The challenges in modeling and control of XYΘflexure-based mechanisms include hysteresis of the piezoelectric actuators,couplings among the input axes,and coupled linear and angular motions of the end effector.This paper presents an inverse hysteresis-coupling hybrid model to account for such hysteresis and couplings.First,a specially designed kinematic chain is adopted to transfer the pose of the end effector into the linear motions at three prismatic joints.Second,an inverse hysteresis-coupling hybrid model is developed to linearize and decouple the system via a multilayer feedforward neural network.A fractional-order PID controller is also integrated to improve the motion accuracy of the overall system.Experimental results demonstrate that the proposed method can accurately control the motion of the end effector with improved accuracy and robustness.
