在线VRFT数据驱动控制方法及其仿真研究

摘要

This paper propesed an on-line virtual reference feedback tuning (VRFT)method. Introduced an effective filter to shift time-sequence in off-line algorithm in order to obtain real-time computable data. Secondly it presented an on-line VRFT algorithm based on recursive least square method (RLSM) with forgetting factor. This method only used real-time data to get optimal controller on-line with no system information and identification. Simulation results show that the proposed method is self-adaptive and superior to conventional VRFT when dealing with time-variant system.%针对离线算法的不足,提出了一种在线虚拟参考反馈整定(VRFT)数据驱动算法.首先利用滤波器改变了离线算法的时序,得到用于实时运算的有效数据;然后提出了基于带遗忘因子递推最小二乘法的VRFT控制器参数辨识方法,不依赖于对象模型,完全利用实时数据实现了在线控制器参数整定.仿真结果表明,在对象特性变化较大的情况下,在线VRFT方法优于传统的离线VRFT方法,具有很好的自适应性.