  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.
