Application of a method to diagnose the source of performance degradation in MPC systems

Model Predictive Control systems may suffer from performance degradation mainly for two reasons: (i) external unmeasured disturbances are not estimated correctly, (ii) the (linear) dynamic model used by the MPC does not match (any longer) the actual process response. In this work we present the application of a method to detect when performance is not optimal, to diagnose the source of performance degradation and to propose appropriate corrections. In the simplest situation (i), optimal performance can be restored by recomputing the estimator parameters; in the other case (ii), re-identification becomes necessary. The method is based on analysis of the prediction error, i.e. the difference between the actual measured output and the corresponding model prediction, and uses three main tools: a statistical (whiteness) test on the prediction error sequence, a subspace identification method to detect the order of the input-to-prediction error system, and a nonlinear optimization algorithm to recompute optimal estimator parameters. We illustrate the effectiveness of the method on a large-scale rigorously simulated industrial process. Copyright © 2013, AIDIC Servizi S.r.l

Performance degradation diagnosis and remedies in offset-free MPC

Linear offset-free MPC algorithms augment the internal model with integrating “disturbances”, which are estimated from output measurements along with the model states. We develop in this paper a performance monitoring strategy for general offset-free MPC algorithms, in which we use the prediction error sequence to detect whether the internal model is correct and/or the augmented state estimator is appropriate. When the prediction error is a white noise signal, revealed by the Ljung-Box test, optimal performance is detected. Otherwise, we use a closed-loop subspace identification approach to reveal the order of a minimal realization of the system from the deterministic input to the prediction error. We prove that, if such order is zero, the model is correct and the source of suboptimal performance is an incorrect estimator. In such cases, we propose an optimization method to recalculate the correct augmented state estimator. If, instead, such order is greater than zero we prove that the model is incorrect, and re-identification is suggested. Two illustrative examples are presented