Robust Constraint Satisfaction in Data-Driven MPC

Abstract

We propose a purely data-driven model predictive control (MPC) scheme to control unknown linear time-invariant systems with guarantees on stability and constraint satisfaction in the presence of noisy data. The scheme predicts future trajectories based on data-dependent Hankel matrices, which span the full system behavior if the input is persistently exciting. This paper extends previous work on data-driven MPC by including a suitable constraint tightening which ensures that the closed-loop trajectory satisfies desired pointwise-in-time output constraints. Furthermore, we provide estimation procedures to compute system constants related to controllability and observability, which are required to implement the constraint tightening. The practicality of the proposed approach is illustrated via a numerical example.

arXiv commentThis version fixes a minor typo in the published version (definition of equilibrium on page 2, change marked in blue color)

References in corpus (3)

• Data-Driven Model Predictive Control with Stability and Robustness Guarantees
• A trajectory-based framework for data-driven system analysis and control
• Data-Driven Tracking MPC for Changing Setpoints

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• Data-driven model predictive control: closed-loop guarantees and experimental results
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• On the design of terminal ingredients for data-driven MPC
• Constrained nonlinear output regulation using model predictive control -- extended version
• Adaptive Robust Data-driven Building Control via Bi-level Reformulation: an Experimental Result
• Stability in data-driven MPC: an inherent robustness perspective
• Data-Driven Prediction with Stochastic Data: Confidence Regions and Minimum Mean-Squared Error Estimates
• A Linear Parameter-Varying Approach to Data Predictive Control
• Stochastic Data-driven Predictive Control of Linear Systems with Sub-Gaussian Disturbances using Causal Predictors