非线性预测控制在塞棒伺服系统中的应用

由于连铸机塞棒位置伺服系统中存在着伺服阀流量非线性、液压缸阀芯摩擦非线性等因素，造成了伺服精度的降低。为改善控制质量，首先建立了含非线性因素的塞棒位置伺服系统模型，然后引入非线性预测控制方法进行伺服控制器设计。该控制器采用RBF神经网络对系统在线辨识并作为预测模型和实现在线校正功能；采用黄金分割法实现控制量滚动优化。最后，在Matlab6．5环境下进行了仿真试验。结果表明，采用该方法能有效提高塞棒位置控制质量，验证了设计方法的有效性。

Application of Nonlinear Predictive Control to the Stopper Servo Systems

In the stopper position servo system,there exist the nonlinear flow property of the servo valve and the nonlinear fiction of hydraulic cylinder needle,which reduce the servo accuracy.To improve the quality of control,the system model containing nonlinear friction is established and the nonlinear predictive control is applied to designing servo controller.The predictive modle and its on-line adjustment are realized by adopting the RBF neural network,which is used to identify system online.The roll optimization of control quantity is realized by exploiting the fibonacci arithmetic.The control plan is tested in Matlab6.5 and the simulink result shows that the method can improve the quality of control effectively.