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.

Factors like growing data availability and increasing system complexity have sparked interest in data-driven predictive control (DDPC) methods like Data-enabled Predictive Control (DeePC). However, closed-loop identification bias arises in the presence of noise, which reduces the effectiveness of obtained control policies. In this paper we propose Closed-loop Data-enabled Predictive Control (CL-DeePC), a framework that unifies different approaches to address this challenge. To this end, CL-DeePC incorporates instrumental variables (IVs) to synthesize and sequentially apply consistent single or multi-step-ahead predictors. Furthermore, a computationally efficient CL-DeePC implementation is developed that reveals an equivalence with Closed-loop Subspace Predictive Control (CL-SPC). Time marching simulations of DeePC and CL-DeePC are conducted using Hankel matrices of past data that are updated at every time step to induce potentially troublesome closed-loop correlations between inputs and noise. Compared to DeePC, CL-DeePC simulations demonstrate superior reference tracking, with a sensitivity study finding a 48% lower susceptibility to noise-induced reference tracking performance degradation.

In recent years, the amount of data available from systems has drastically increased, motivating the use of direct data-driven control techniques that avoid the need of parametric modeling. The aim of this paper is to analyze closed-loop aspects of these approaches in the presence of noise. To analyze this, a unified formulation of several approaches, including Data-enabled Predictive Control (DeePC) and Subspace Predictive Control (SPC) is obtained and the influence of noise on closed-loop predictors is analyzed. The analysis reveals potential closed-loop correlation problems, which are closely related to well-known results in closed-loop system identification, and consequent control issues. A case study reveals the hazards of noise in data-driven control.

Light Detection and Ranging (LIDAR)-assisted Model Predictive Control (MPC) for wind turbine control has received much attention for its ability to incorporate future wind speed disturbance information in a receding horizon optimal control problem. However, the growth of wind turbine sizes results in increasing system complexity and system interactions, and complicates the design of model-based controllers like MPC. Together with increasing data availability, this obstacle motivates the use of direct data-driven predictive control approaches like Subspace Predictive Control (SPC). An SPC implementation is developed that both does not suffer from traditional, potentially detrimental closed-loop identification bias and incorporates past and future (not necessarily periodic) disturbance information. Simulations of the presented method for above-rated wind turbine rotor speed regulation using pitch control demonstrate the capabilities of the data-driven SPC algorithm for increasing degrees of wind speed disturbance information in the developed framework.