Data-Driven Robust Subspace Predictive Control With Embedded Disturbance Observer Structure

Abstract

Subspace predictive control (SPC) is a data-driven control strategy that utilizes input-output measurements to predict future system behavior without requiring explicit model identification. Conventional SPC exhibits vulnerability to an unknown input disturbance, leading to degraded control performance and steady-state errors. To address these limitations, this article proposes a robust SPC method that inherently mitigates the effect of a constant input disturbance by augmenting the state-space representation through the internal model principle (IMP). This augmentation enables the controller to achieve integral action without requiring a separate disturbance observer (DOB) design. The proposed method is implemented in a data-driven framework, where an auxiliary disturbance is introduced into the data-driven algorithm to enhance disturbance rejection. A transfer function analysis verifies that the proposed Robust SPC eliminates a constant disturbance while maintaining the role of a DOB. Experimental validation on a two-inertia system confirms that the proposed method significantly improves reference tracking performance compared to conventional SPC, demonstrating its effectiveness in disturbance rejection without additional modeling complexity.