On the design of terminal ingredients for data-driven MPC

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.

Final version, accepted for presentation at the 7th IFAC Conference on Nonlinear Model Predictive Control 2021

References in corpus (4)

• Linear tracking MPC for nonlinear systems Part II: The data-driven case
• Combining Prior Knowledge and Data for Robust Controller Design
• Robust Constraint Satisfaction in Data-Driven MPC
• Data-Driven Tracking MPC for Changing Setpoints

Cited by in corpus (6)

• Linear tracking MPC for nonlinear systems Part II: The data-driven case
• Combining Prior Knowledge and Data for Robust Controller Design
• Robust stability analysis of a simple data-driven model predictive control approach
• Data-driven model predictive control: closed-loop guarantees and experimental results
• Stability in data-driven MPC: an inherent robustness perspective
• Data-driven distributed MPC of dynamically coupled linear systems