摘要
针对智能材料中存在的迟滞问题,对其开展了迟滞非线性特性分析,建立了迟滞系统。该迟滞系统由两个部分串联构成:一部分是滤去传递函数影响的Preisach模型;另一部分是不考虑迟滞影响的系统传递函数。将离线和在线辨识方法应用到辨识迟滞系统中,应用最小二乘法离线辨识得到了辨识传递函数参数,再用此辨识传递函数参数作为神经网络辨识的初始权值,得到了神经网络在线辨识的辨识模型;建立了辨识传递函数的逆模型控制系统和前馈逆模型PID控制系统,并对辨识系统进行了迟滞非线性补偿。研究结果表明,模型辨识方法的可行性和补偿控制的有效性在仿真中得到了验证。
Aiming at the hysteresis nonlinearity characteristics of smart materials, hysteretic system was established by analyzing the hystere- sis nonlinearity characteristics of smart materials. This system was consisted of two parts in series, including preisach model eliminated the influence of transfer function, and linear transfer function without considering the influence of hysteresis. Then, hysteretic system model was identified by offline and online identification method. Firstly, transfer function parameters were gotten by the least squares oftline identifica- tion. Then, hysteresis system model of online identification was obtained by using the identification transfer function parameters as initial weight value of the neural network. Finally, the inverse model control system of identification transfer function and the feed-forward controller with a PID controller were established to achieve compensating hysteresis nonlinearity of the identification systems. The results indicate that the feasibility of the identification method and the effectiveness of compensating controls are verified by simulation.
出处
《机电工程》
CAS
2014年第1期57-61,85,共6页
Journal of Mechanical & Electrical Engineering
基金
湖南省教育厅重点资助项目(13A081)
关键词
迟滞系统
离线辨识
神经网络辨识
逆模型
hysteretic systems
offline identification
neural network identification
inverse model controller
feed-forward controller withPID controller
