Distributed Experiment Design and Control for Multi-agent Systems with Gaussian Processes

This paper focuses on distributed learning-based control of decentralized multi-agent systems where the agents' dynamics are modeled by Gaussian Processes (GPs). Two fundamental problems are considered: the optimal design of experiment for learning of the agents' GP models concurrently, and the distributed coordination given the learned models. Using a Distributed Model Predictive Control (DMPC) approach, the two problems are formulated as distributed optimization problems, where each agent's sub-problem includes both local and shared objectives and constraints. To solve the resulting complex and non-convex DMPC problems efficiently, we develop an algorithm called Alternating Direction Method of Multipliers with Convexification (ADMM-C) that combines a distributed ADMM algorithm and a Sequential Convex Programming method. We prove that, under some technical assumptions, the ADMM-C algorithm converges to a stationary point of the penalized optimization problem. The effectiveness of our approach is demonstrated in numerical simulations of a multi-vehicle formation control example.