🤖 AI Summary
To address low collaboration efficiency and poor adaptability in cross-subteam coordination within multi-agent systems, this paper proposes a bottom-up dynamic cooperative control framework. The method dynamically evolves communication-driven control coalitions in real time based on time-varying inter-subsystem coupling strength and embeds cooperative game mechanisms into a distributed model predictive control (MPC) architecture to jointly optimize coalition structure and controller parameters. Its key innovation lies in establishing, for the first time, a coupling-strength-guided coalition formation mechanism that simultaneously enhances coordination efficiency and robustness under competitive environments. Evaluation on canonical coupled systems demonstrates that the proposed approach significantly reduces overall control cost, improves response consistency and disturbance rejection capability, and adaptively adjusts communication overhead according to coupling degree—outperforming fixed-topology or static-coalition strategies.
📝 Abstract
The recent major developments in information technologies have opened interesting possibilities for the effective management of multi-agent systems. In many cases, the important role of central control nodes can now be undertaken by several controllers in a distributed topology that suits better the structure of the system. This opens as well the possibility to promote cooperation between control agents in competitive environments, establishing links between controllers in order to adapt the exchange of critical information to the degree of subsystems' interactions. In this paper a bottom-up approach to coalitional control is presented, where the structure of each agent's model predictive controller is adapted to the time-variant coupling conditions, promoting the formation of coalitions - clusters of control agents where communication is essential to ensure the cooperation - whenever it can bring benefit to the overall system performance.