Distributed Synthesis of Gray-Box Distributed H2 Controllers
It addresses the need for scalable and privacy-preserving controller synthesis in large-scale systems where model knowledge is incomplete, offering a practical alternative to centralized or white-box approaches.
This paper proposes a distributed gray-box method for synthesizing H2 controllers that combines partial model knowledge with input-state data, enabling scalable and privacy-preserving control without a central server. The method is validated on the IEEE 39-bus power system test case.
Distributed controller synthesis offers scalable and privacy-preserving control design, but typical state-of-the-art approaches either assume white-box models or resort to centralized synthesis. In this paper, we combine partially known model knowledge and an input-state dataset within a distributed gray-box scheme to design \(\mathcal{H}_2\) controllers. Our method can handle unknown dynamics and offers scalable synthesis. Each agent communicates with a set of neighbors determined by the physical coupling topology of the system such that we can apply the Alternating Direction Method of Multipliers (ADMM) to solve the problem iteratively in a fully distributed fashion (i.e., without a central server). The effectiveness and flexibility of the proposed approach is demonstrated in simulations of the IEEE 39-bus power system test case.