Data-driven plant-model mismatch detection for dynamic matrix control systems using sum-of-norms regularization

Abstract

This article addresses the plant-model mismatch detection problem for linear multiple-input and multiple-output systems operating under the constrained dynamic matrix control (DMC) with the assumption of unknown noise models. An autocovariance-based mismatch detection method that uses sum-of-norms regularization is proposed, aiming to detect parameter jumps and estimate the noise model separately. The intention of introducing regularization is not only to be able to segment the mismatch so that the mismatch is piece-wise constant in time, but also to make the method robust to colored noise. Moreover, a method to alleviate mis-detection caused by unknown operating conditions is proposed. We show that the method can detect significant jumps in parameters and thus provide a priori knowledge for system re-identification and timing of updating the model. Finally, the feasibility of the proposed method under closed-loop conditions is analyzed from a stochastic perspective and demonstrated with illustrative examples.