Adaptive Control of Multi-Agent Systems for Finding Peaks of Unknown Fields

In this paper, we consider a deterministic adaptive control framework to design and analyze a class of multi-agent systems that locate peaks of unknown static fields in a distributed and scalable manner. Each agent is driven by swarming and gradient ascent efforts based on its own recursively estimated field via locally collected measurements by itself and its neighboring agents. The convergence properties of the proposed multi-agent systems are analyzed. We also provide a sampling scheme to facilitate the convergence. The simulation study confirms the convergence analysis of the proposed algorithms.