低频线振动台加速度失真非线性补偿控制方法  

Nonlinear compensation control scheme for acceleration distortion of low-frequency linear vibration table system

作　　者：赵富[1] 于志伟[1] 曾鸣[1] 苏宝库[1] 

机构地区：[1]哈尔滨工业大学空间控制与惯性技术研究中心,黑龙江哈尔滨150001

出　　处：《电机与控制学报》2010年第7期91-98,共8页Electric Machines and Control

基　　金：国家安全重大基础研究项目(973-61334)

摘　　要：为了抑制低频线振动台中存在的模型不确定性及外部扰动,基于模糊基函数网络(FBFN)提出了一种自适应重复学习控制方法。利用FBFN逼近低频线振动台的模型不确定性及外部扰动,将对模型不确定性和扰动的辨识问题转化为对FBFN权系数的辨识问题。所提出的控制律由自适应控制和重复学习控制组成。自适应律用来估计FBFN权系数;为了有效地减弱抖振,使用自适应PI控制结构逼近非连续控制。由于非连续控制的界是未知的,利用自适应律估计这个未知的界。重复学习控制用来提高系统对周期性输入信号的跟踪性能。采用Lyapunov理论设计的自适应重复学习控制律保证了低频线振动台的渐近稳定性和位置跟踪性能。仿真结果表明,自适应重复学习控制律改善了系统的跟踪性能和加速度失真度。An adaptive repetitive learning control scheme based on the fuzzy basis function network（FBFN） is presented to restrain model uncertainties and external disturbances in low-frequency linear vi-bration table system.FBFN is used to estimate model uncertainties and external disturbances,which change the identification problem from identifying model uncertainties and disturbances to identifying the coefficients of FBFN.The control algorithm consists of an adaptive component and a repetitive learning component.The adaptive component is used to estimate weight coefficients of FBFN.To attenuate chatte-ring effectively,the discontinuous control is approximated by an adaptive PI control structure.Due to the bound of the discontinuous control term being assumed to be unknown,an adaptive mechanism is used to estimate this bound.The repetitive learning component is used to improve the tracking performance of pe-riodic input signals.The adaptive repetitive learning control law designed by using Lyapunov theory guar-antees the system stability and the position tracking performance.The simulation results demonstrate the adaptive repetitive learning control scheme can improve the tracking performance and acceleration distor-tion for low-frequency linear vibration table.

关 键 词：直线电机 低频线振动台 重复学习控制 自适应控制 模糊系统 跟踪性能 加速度失真度 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]