We published a new paper on a distributed data-driven predictive control (DDPC) based on the behavioral systems framework as a full paper: Yitao Yan, Jie Bao and Biao Huang, Distributed Data-driven Predictive Control via Dissipative Behavior Synthesis. IEEE Transactions on Automatic Control, July 2023, doi: 10.1109/TAC.2023.3298281. https://ieeexplore.ieee.org/document/10190991

By viewing dissipativity as a behavior and integrating it into the control design as a virtual dynamical system, the proposed approach carries out the entire design process in a unified framework with a set-theoretic viewpoint. This leads to an effective data-driven distributed control design (e.g., for process control) and decision making (e.g., operations of supply chains, where each participant only has the full information on its own operation and limited data of other participants), based on big data. This work shows that the behavioral systems theory is not only beautiful but also useful!