Enhancing the Robustness of Model-Predictive Control using GMMs as Outlier Detection

This article proposes a flexible control strategy to improve the reliability of data-driven model-predictive control (MPC). Prediction models are usually trained on logged data, which may be incomplete, leading to model failure within the optimization algorithm of the MPC. To counter this, we propose a Gaussian Mixture Model (GMM) for detecting outliers during live operation. These outliers are found in the observed system states as well as the calculated control signals regarding the logged training data of the ai-model. If an outlier is detected, we assume that the data-driven prediction model will be inaccurate, and an appropriate fallback strategy is used instead, which includes but is not limited to an alternative controller or a complete system-shutdown. In addition, outliers are stored for subsequent improvement of the prediction model. This outlier-protected control (OPC) architecture is evaluated in terms of control quality using the integrated squared error (ISE) on three nonlinear dynamical systems. The outcomes indicate a significant enhancement in control quality in comparison to an unprotected but otherwise equivalent architecture. These findings suggest that a very simple and efficient outlier detection can greatly benefit data-driven MPC in general whenever feasible fallback strategies exist.