论文部分内容阅读
针对一类非线性时变、动态特性突变显著,且无法进行准确机理建模的工业过程,提出一种基于机理分析与数据驱动方法相结合的子空间预测控制方法。该方法通过比较预测误差在线更新预测模型,并能根据能够根据反馈误差调整滚动窗口长度,增强了控制器对非线性时变特征的适应能力以及对不可测干扰的抑制能力。最后,通过对废杂铜冶炼过程的实际运行数据进行仿真研究,验证了方法的有效性。
Aiming at a kind of industrial process with nonlinear time-varying, significant abrupt change of dynamic characteristics and inability to accurately model the mechanism, a sub-space predictive control method based on mechanism analysis and data-driven method is proposed. The method updates the prediction model online by comparing the prediction error, and according to the fact that the length of the scroll window can be adjusted according to the feedback error, the adaptive capability of the controller to the nonlinear time-varying feature and the suppression of the unpredictable interference are enhanced. Finally, through the simulation study of the actual operation data of the scrap copper smelting process, the effectiveness of the method is verified.