Input‐mapping based data‐driven model predictive control for unknown linear systems via online learning

Abstract

Data-driven model predictive control (MPC) is an effective control method in controlling unknown constrained systems. The existing data-driven MPC methods either estimate the system online (adaptive) with extra computation efforts, or use the initially measured trajectory from offline trials to design controller. The offline trials are economically expensive for many practical systems. To overcome these limitations, we propose a multistep input-mapping data-driven scheme. It maps the current and future inputs to the past online measured input-state trajectories and expresses the future state as a linear combination of the past states. A moving data window is used to update the data online. Based on this scheme, two computationally efficient multistep input-mapping data-driven robust model predictive controllers with guarantee of feasibility and stability are proposed. The simulation results verify the improved performance and better computation efficiency of the resulting methods.