Data-driven rate-based integral predictive control with estimated prediction matrices

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. Online, 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 (also called the velocity form model). The effectiveness of the developed data-driven predictive control algorithm is demonstrated in real-life control of a Buck DC-DC converter.