Two-component controller design to safeguard data-driven predictive control

TL;DR

This paper introduces a two-component control scheme combining data-driven predictive control with a model-free high-gain feedback to ensure output constraints and safe data collection in systems with relative degree two.

Contribution

It proposes a novel integrated control architecture that safeguards output constraints while enabling data-driven predictive control, especially when data is limited or model accuracy is uncertain.

Findings

Effective combination of DeePC and EDMD-based MPC demonstrated

Safeguarding feedback ensures constraint satisfaction during set-point transitions

Numerical examples validate the approach in practical scenarios

Abstract

We design a two-component controller to achieve reference tracking with output constraints - exemplified on systems of relative degree two. One component is a data-driven or learning-based predictive controller, which uses data samples to learn a model and predict the future behavior of the system. We exemplify this component concisely by data-enabled predictive control (DeePC) and by model predictive control based on extended dynamic mode decomposition (EDMD). The second component is a model-free high-gain feedback controller, which ensures satisfaction of the output constraints if that cannot be guaranteed by the predictive controller. This may be the case, for example, if too little data has been collected for learning or no (sufficient) guarantees on the approximation accuracy derived. In particular, the reactive/adaptive feedback controller can be used to support the learning process by leading safely through the state space to collect suitable data, e.g., to ensure a sufficiently-small fill distance. Numerical examples are provided to illustrate the combination of EDMD-based model predictive control and a safeguarding feedback for the set-point transitions including the transition between the set points within prescribed bounds.