Distributed Parameter Estimation in Adaptive Coverage Control

In distributed adaptive coverage control, agents are equipped with sensors to measure the sensory information at their respective positions. The sensory function is assumed to be linear in parameters, comprising a known basis vector and an unknown constant parameter vector. We propose a distributed adaptation law containing memory and consensus terms, which guarantees convergence of each agent’s parameter estimate to its true value and agreement among agents. The proposed adaptation law relies on locally available sensory data and basis vectors, along with communication among neighboring agents. The origin of the parameter estimation error of each agent is proved to be asymptotically stable. Numerical simulations validate the theoretical results and demonstrate the effectiveness of the proposed approach.