Model-Free Disturbance Observer with Online Modification: Listening to MFDOOM
For control of unknown systems with disturbances, this method offers improved performance over existing online DeePC variants.
The paper proposes MFDOOM, an augmentation of DeePC that corrects output predictions using previous errors via a continuously updated Hankel matrix, reducing tracking error and online-update burden for disturbances modeled as outputs of autonomous LTI systems.
Data-Enabled Predictive Control (DeePC) has recently emerged as a framework for controlling unknown systems from data. However, its performance relies on the relevance of the collected data, and as such, disturbances lead to inevitable errors. This paper addresses this problem by proposing an augmentation of DeePC using Model-Free Disturbance Observer with Online Modification (MFDOOM). The method corrects output predictions based on previous prediction errors using a dedicated continuously updated Hankel matrix. We compare our method, both theoretically and through simulation, to other recent algorithms designed for time-varying systems in the DeePC framework. It is shown that for disturbances that can be modeled as the output of an autonomous linear time-invariant system, this approach can reduce tracking error and online-update burden compared with existing online DeePC variants.