2023/05/01 by Manuel Koch, Koch, Manuel, Colin N. Jones +1
Engineering · #Advanced Control Systems Optimization #Control Systems and Identification #FOS: Electrical engineering #Fault Detection and Control Systems #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2305.00807
openalex publication_date 2023/05/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Data-enabled predictive control (DeePC) is a recently established form of Model Predictive Control (MPC), based on behavioral systems theory. While eliminating the need to explicitly identify a model, it requires an additional regularization with a corresponding weight to function well with noisy data. The tuning of this weight is non-trivial and has a significant impact on performance. In this paper, we compare three reformulations of DeePC that either eliminate the regularization, or simplify the tuning to a trivial point. A building simulation study shows a comparable performance for all three reformulations of DeePC. However, a conventional MPC with a black-box model slightly outperforms them, while solving much faster, and yielding smoother optimal trajectories. Two of the DeePC variants also show sensitivity to an unobserved biased input noise, which is not present in the conventional MPC.