Assessment of Model-Plant Mismatch by the Nominal Sensitivity Function for Unconstrained MPC

Abstract

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.

Perspectives and challenges in performance assessment of model predictive control

“…The complete mathematical description as well as the theorem proof can be found in Botelho et al Equation shows that it is possible to estimate the nominal closed‐loop output from the controller model as well as plant input and output data. Since y 0 is an estimate of the output process in the absence of a model‐plant mismatch or unmeasured disturbance, it could be considered a benchmark for controller‐model output response.…”