Stochastic Data-Driven Predictive Control: Chance-Constraint Satisfaction with Identified Multi-step Predictors

TL;DR

This paper introduces a data-driven stochastic predictive control method that uses multi-step predictors and uncertainty quantification to satisfy chance constraints in uncertain linear systems, reducing conservatism.

Contribution

It develops a novel approach combining multi-step predictor identification with uncertainty quantification for chance-constrained control.

Findings

Reduced conservatism compared to existing methods

Effective uncertainty propagation via data-driven models

Successful numerical example demonstrating approach efficacy

Abstract

We propose a novel data-driven stochastic model predictive control framework for uncertain linear systems with noisy output measurements. Our approach leverages multi-step predictors to efficiently propagate uncertainty, ensuring chance constraint satisfaction. In particular, we present a strategy to identify multi-step predictors and quantify the associated uncertainty using a surrogate (data-driven) state space model. Then, we utilize the derived distribution to formulate a constraint tightening that ensures chance constraint satisfaction despite the parametric uncertainty. A numerical example highlights the reduced conservatism of handling parametric uncertainty in the proposed method compared to state-of-the-art solutions.