Assessment of Model-Plant Mismatch by the Nominal Sensitivity Function for Unconstrained MPC
نویسندگان
چکیده
Model Predictive Control (MPC) is a class of control systems which use a dynamic process model to predict the best future control actions based on past information. Thus, a representative process model is a key factor for its correct performance. Therefore, the investigation of model-plant-mismatch effect is very important issue for MPC performance assessment, monitoring, and diagnosis. This paper presents a method for model quality evaluation based on the investigation of closed-loop data and the nominal complementary sensitivity function. The proposed approach ensures that the MPC tuning is taken into account in the evaluation of the model quality. A SISO case study is analyzed and the results show the effectiveness of the method.
منابع مشابه
Pii: S0959-1524(98)00045-6
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