一种微操作平台的自适应运动跟踪控制

摘要

针对微操作平台的迟滞非线性和时变性,提出单神经元PID控制策略来对其进行运动跟踪控制,从而提高平台的运动精确性和响应快速性.采用RBF神经网络辨识器对微操作平台的梯度信息进行在线辨识,利用单神经元网络学习算法完成PID参数的在线自整定,实现微操作平台的自适应运动跟踪控制.为说明所提出控制方法的可行性,将其与普通PID控制方法进行了比较分析,实验结果表明,单神经元PID与普通PID控制的位移误差范围分别为-0.5~0.5μm、-2.5~2.5μm,调整时间分别为0.1 s、0.4 s,所提出控制方法具有更好的控制精度和响应快速性,并具有较强的自适应性.%Considering hysteresis nonlinearity and time variation of micro-manipulation stage,a single neuron PID control strategy was proposed for motion tracking control to improve motion accu-racy and response of the stage.Gradient informations of the micro-manipulation stage might be ob-tained online by RBF neural network identifier,and learning algorithm of single neuron network was applied to achieve online self-tuning of PID parameters to achieve the adaptive motion tracking control of the micro-manipulation stage.In order to illustrate the feasibility of the proposed control method, experimental comparative analyses with ordinary PID control were carried out.Experimental results show that,displacement error ranges of the single neuron PID are as -0.5~0.5μm,and the adjust-ment time of that is as 0.1 s,while displacement error ranges of ordinary PID control are as -2.5~2. 5μm,and the adjustment time of that is as 0.4 s.It shows that the proposed control method has better control accuracy,response speed,and stronger adaptability.