提出了设计自校正内模控制的新方法。该方法把输出预测误差划分为有规律和无规律误差两部分。通过在线辨识建立有规律误差的预报模型,由此模型对误差值进行自适应预报,达到对内模控制器的在线校正,而对无规律的误差只作闭环反馈修正,最后达到消除系统的稳态余差。本方法比通常自校正内模控制算法的运算量大大减少,而在对象参数在一定范围内漂移时,同样能取到自校正效果。 A new method of designing self-tuning internal model control is proposed. The method divides the output prediction error into two parts, regular and irregular errors. Through online identification to establish a forecasting model with regular error, the model adaptively predicts the error value to achieve on-line calibration of the internal model controller, while only correcting closed-loop feedback to the erroneous error, finally to eliminate the system stability The remaining balance. Compared with the self-tuning internal model control algorithm, the computational cost of this method is greatly reduced. When the object parameters drift within a certain range, the self-correction effect can also be obtained.