On the design of terminal ingredients for data-driven MPC

TL;DR

This paper introduces a data-driven MPC scheme for linear systems that uses measured input-output data to design terminal ingredients, ensuring exponential stability without requiring a system model.

Contribution

It provides an explicit data-driven design procedure for terminal costs and sets in MPC, guaranteeing stability solely from input-output measurements.

Findings

Exponential stabilization of the setpoint achieved.

Explicit data-driven design procedure provided.

Advantages over existing approaches demonstrated.

Abstract

We present a model predictive control (MPC) scheme to control linear time-invariant systems using only measured input-output data and no model knowledge. The scheme includes a terminal cost and a terminal set constraint on an extended state containing past input-output values. We provide an explicit design procedure for the corresponding terminal ingredients that only uses measured input-output data. Further, we prove that the MPC scheme based on these terminal ingredients exponentially stabilizes the desired setpoint in closed loop. Finally, we illustrate the advantages over existing data-driven MPC approaches with a numerical example.