Adaptive backstepping sliding mode control of fast steering mirror driven by piezoelectric actuator

Tian Fuqing; Li Keyu; Wang Jue; Wang Hongjian

doi:10.3788/HPLPB201426.011011

Volume 26 Issue 01

Jan. 2014

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Li Yunlong, Yang Haifeng, Yi Xuan, et al. Benchmark experiment and analysis of JMCT on nuclear critical safety[J]. High Power Laser and Particle Beams, 2017, 29: 016007. doi: 10.11884/HPLPB201729.160212

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Tian Fuqing, Li Keyu, Wang Jue, et al. Adaptive backstepping sliding mode control of fast steering mirror driven by piezoelectric actuator[J]. High Power Laser and Particle Beams, 2014, 26: 011011. doi: 10.3788/HPLPB201426.011011

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Adaptive backstepping sliding mode control of fast steering mirror driven by piezoelectric actuator

doi: 10.3788/HPLPB201426.011011

1.
Department of Weapon Engineering,Naval University of Engineering,Wuhan 430033,China;
2.
Military Representative Bureau of Navy in Chongqing,Chongqing 400042,China

Received Date: 2013-06-19
Rev Recd Date: 2013-09-26
Publish Date: 2014-01-15

Abstract

Abstract

Fast steering mirror (FSM) driven by piezoelectric actuator has been widely used in multifarious precision instruments for stabilization and tracking systems, whose tracking accuracy is decided by the control accuracy of FSM. However, there is a seriously nonlinear interference of hysteresis in the driving of piezoelectric actuator. In response to this defect, an adaptive radial bass function (RBF) neural network was used to approximate the nonlinear interference of hysteresis, and based on which, the sliding mode control and backstepping algorithm were combined to design adaptive backstepping sliding mode (ABSM) controller. The simulation results show that, compared with the control accuracy of the sliding mode controller, the maximum tracking error and mean-root-square error of ABSM controller declines by 57.26% and 52.53% respectively, which improves the control accuracy of FSM evidently.
- fast steering mirror,
- piezoelectric actuator,
- backstepping sliding mode control,
- RBF neural network

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