摘要
由压电驱动器驱动的快速反射镜(FSM)广泛应用于各种精密稳定跟踪系统,FSM的控制精度决定了系统的跟踪精度。但压电驱动器具有严重的迟滞非线性干扰,针对这一缺点,应用自适应径向基RBF神经网络对迟滞干扰进行非线性逼近,并在此基础上结合滑模控制和反演法,设计了自适应反演滑模(ABSM)控制器。仿真实验表明,相对于滑模控制器,ABSM控制器的最大跟踪误差和均方根误差为分别降低了57.26%和52.53%,提高了FSM的控制精度。
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.
出处
《强激光与粒子束》
EI
CAS
CSCD
北大核心
2014年第1期59-63,共5页
High Power Laser and Particle Beams
基金
省级十二五预研项目(4010802010103)
关键词
快速反射镜
压电驱动器
反演滑模控制
RBF神经网络
fast steering mirror
piezoelectric actuator
backstepping sliding mode control
RBF neural network
