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		Lijun ZHANG,Lixin YANG,Lining SUN,Xingwen ZHANG.[en_title][J].Control Theory and Technology,2012,10(4):504~510.[Copy]

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LijunZHANG,LixinYANG,LiningSUN,XingwenZHANG
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(Department of Automation, Harbin Engineering University; School of Marine Engineering, Northwestern Polytechnical University;College of Electrical & Information Engineering, Heilongjiang Institute of Science & Technology;Robotics Institute, Harbin Institute of Technology;College of Aeronautics Science and Technology, Beijing University of Aeronautics and Astronautics)

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Received:November 05, 2010Revised:May 12, 2011

基金项目:This work was partly supported by the National Natural Science Foundation of China (No. 61174047), the School Basic Foundation of Northwestern Polytechnical University (No. GCKY1006), and the Fundamental Research Funds for the Central Universities (No. HEUCFR1214).

Adaptive Kalman filter and dynamic recurrent neural networks-based control design of macro-micro manipulator

Lijun ZHANG,Lixin YANG,Lining SUN,Xingwen ZHANG

(Department of Automation, Harbin Engineering University; School of Marine Engineering, Northwestern Polytechnical University;College of Electrical & Information Engineering, Heilongjiang Institute of Science & Technology;Robotics Institute, Harbin Institute of Technology;College of Aeronautics Science and Technology, Beijing University of Aeronautics and Astronautics)

Abstract:

In this paper, a composite control scheme for macro-micro dual-drive positioning stage with high acceleration and high precision is proposed. The objective of control is to improve the precision by reducing the influence of system vibration and external noise. The positioning stage is composed of voice coil motor (VCM) as macro driver and piezoelectric actuator (PEA) as micro driver. The precision of the macro drive positioning stage is improved by the combined PID control with adaptive Kalman filter (AKF). AKF is used to compensate VCM vibration (as the virtual noise) and the external noise. The control scheme of the micro drive positioning stage is presented as the integrated one with PID and intelligent adaptive inverse control approach to compensate the positioning error caused by macro drive positioning stage. A dynamic recurrent neural networks (DRNN) based inverse control approach is proposed to offset the hysteresis nonlinearity of PEA. Simulations show the positioning precision of macro-micro dual-drive stage is clearly improved via the proposed control scheme.

Key words: Macro-micro dual-drive positioning stage Piezoelectric actuator Adaptive Kalman filter Dynamic recurrent neural networks