【摘要】 在基于模型的控制技术中,例如模型预测控制(MPC),模型的质量对于控制器的设计和整定起到关键作用。所以,控制器的性能依赖于过程模型的精度,亦即受到模型失配程度的影响。针对线性系统的模型失配检测,已经有各种不同的方法见诸于相关文献,而对于非线性系统,则很少有相应的方法提出。考虑到广泛存在的系统非线性特性,采用互信息作为一种广义的相关性量化测度。利用摄动信号和模型残差的互信息量来表征过程模型失配程度而与扰动部分的模型变化无关。对于大规模的多变量系统,能够定位到子系统的模型失配对于故障诊断或者模型重新辨识起到至关重要的作用,利用一种递阶排除的分析方法来精确定位子系统的模型失配,提出利用互信息矩阵直观的量化表达多变量系统的模型失配。在两个仿真例子上的应用说明了该方法的有效性。更多还原

【Abstract】 For model based control systems,such as MPC,the model plays a key role in controller design and tuning.The performance of the controllers depend on the model’ s quality and hence on the model-plant mismatch(MPM).For linear systems,many different approaches have been well developed for MPM detection.Considering the widespread nonlinearities,mutual information(MI),as a general dependence measure,was proposed to detect model plant mismatch.MI between dithering signals and model residuals,was estimated to reveal process MPM regardless of change in disturbance dynamics.For large scale MIMO systems,re-identification of the model is a costly exercise as keeping a large number of inputs in a perturbed or excited state for a long time means loss of normal production time.Hence,it would be highly desirable to locate the sub-sets of mismatch so that only sub-systems have to be perturbed for model updating.An MI matrix,corresponding the model error was defined to reveal mismatched sub-models.This,in turn,provides important information to assist plant operators in narrowing down potential root causes.The good performance of the method for nonlinear systems was illustrated by two examples.更多还原