Decentralized adaptive control for a class of semi-parametric uncertain multi-agent systems

Decentralized adaptive control for a discrete-time multi-agent semi-parametric uncertain dynamical system, where each agent is coupled with other agents in both parametric and non-parametric ways, is studied in this paper. Each agent can only use its history information and local information on its neighborhood agents to design its control law aimed at achieving its own local goal, i.e. tracking a local signal sequence. To deal with the co-existing parametric and non-parametric uncertainties occurred in the local couplings and the internal dynamics, an idea of information concentration is adopted with the key idea of nearest-neighbor-estimation, which makes it possible to compensate well the challenging non-parametric uncertainties. With such ideas, decentralized adaptive control laws are designed based on the “certainty equivalence” principle, and the simulations as well as preliminary theoretical discussions show that the closed-loop system for the whole multi-agent system is stable under some mild conditions on the a priori knowledge on the uncertain couplings.