A Robust Data-Driven Iterative Control Method for Linear Systems with Bounded Disturbances

TL;DR

This paper introduces a robust data-driven iterative control approach for linear systems with unknown disturbances, utilizing multiple datasets and iterative updates to enhance control robustness and reduce conservativeness.

Contribution

It develops a novel iterative control method that integrates multiple datasets both offline and online, improving robustness over existing single-dataset approaches.

Findings

Effective control of a batch reactor demonstrated

Reduced conservativeness compared to traditional methods

Online data integration enhances control performance

Abstract

This paper proposes a new robust data-driven control method for linear systems with bounded disturbances, where the system model and disturbances are unknown. Due to disturbances, accurately determining the true system becomes challenging using the collected dataset. Therefore, instead of designing controllers directly for the unknown true system, an available approach is to design controllers for all systems compatible with the dataset. To overcome the limitations of using a single dataset and benefit from collecting more data, multiple datasets are employed in this paper. Furthermore, a new iterative method is developed to address the challenges of using multiple datasets. Based on this method, this paper develops an offline and online robust data-driven iterative control method, respectively. Compared to the existing robust data-driven controller method, both proposed control methods iteratively utilize multiple datasets in the controller design process. This allows for the incorporation of numerous datasets, potentially reducing the conservativeness of the designed controller. Particularly, the online controller is iteratively designed by continuously incorporating online collected data into the historical data to construct new datasets. Lastly, the effectiveness of the proposed methods is demonstrated using a batch reactor.