Data-driven predictive control with estimated prediction matrices and integral action

TL;DR

This paper introduces a data-driven predictive control method that estimates prediction matrices directly from measured data, enabling effective control without explicit system models, and extends it to include integral action for improved robustness.

Contribution

It proposes a novel data-driven approach to estimate prediction matrices for predictive control, including integral action, bypassing the need for explicit system models.

Findings

Effective position control of a linear motor demonstrated

Prediction matrices estimated accurately from data

Controller solves a quadratic program similar to MPC

Abstract

This paper presents a data-driven approach to the design of predictive controllers. The prediction matrices utilized in standard model predictive control (MPC) algorithms are typically constructed using knowledge of a system model such as, state-space or input-output models. Instead, we directly estimate the prediction matrices relating future outputs with current and future inputs from measured data, off-line. On-line, the developed data--driven predictive controller reduces to solving a quadratic program with a similar structure and complexity as linear MPC. Additionally, we develop a new procedure for estimating prediction matrices from data for predictive controllers with integral action, corresponding to the rate-based formulation of linear MPC. The effectiveness of the developed data-driven predictive controller is illustrated on position control of a linear motor model.