Data-driven predictive control in a stochastic setting: a unified framework

TL;DR

This paper introduces a unified framework for data-driven predictive control in stochastic systems, proposing a new two-stage scheme called gamma-DDPC that simplifies controller tuning and enhances performance under noise.

Contribution

It develops a unified framework for regularized DDPC, introduces gamma-DDPC, and provides insights on regularization's role in stochastic data-driven control.

Findings

Gamma-DDPC improves control performance in noisy environments.

The framework reduces tuning complexity and computational effort.

Numerical case study validates the effectiveness of the proposed approach.

Abstract

Data-driven predictive control (DDPC) has been recently proposed as an effective alternative to traditional model-predictive control (MPC) for its unique features of being time-efficient and unbiased with respect to the oracle solution. Nonetheless, it has also been observed that noise may strongly jeopardize the final closed-loop performance since it affects both the data-based system representation and the control update computed from the online measurements. Recent studies have shown that regularization is potentially a successful tool to counteract the effect of noise. At the same time, regularization requires the tuning of a set of penalty terms, whose choice might be practically difficult without closed-loop experiments. In this paper, by means of subspace identification tools, we pursue a three-fold goal: $(i)$ we set up a unified framework for the existing regularized data-driven predictive control schemes for stochastic systems; $(ii)$ we introduce $\gamma$-DDPC, an efficient two-stage scheme that splits the optimization problem into two parts: fitting the initial conditions and optimizing the future performance, while guaranteeing constraint satisfaction; $(iii)$ we discuss the role of regularization for data-driven predictive control, providing new insight on $when$ and $how$ it should be applied. A benchmark numerical case study finally illustrates the performance of $\gamma$-DDPC, showing how controller design can be simplified in terms of tuning effort and computational complexity when benefiting from the insights coming from the subspace identification realm.