Kernel-based Consensus Control of Multi-agent Systems with Unknown System Dynamics

A novel method for optimal consensus control of multi-agent systems (MASs) based on adaptive dynamic programming (ADP) is developed in this paper. Unlike neural networks (NNs) that require manually designed features in value function approximation and may effect the approximation quality. Kernel-based methods are adopted to approximate value functions without predefining the model structure. Moreover, to handle the challenge of unknown or complex system dynamics, a local action-value function is defined and kernel-based methods are used to approximate the local action-value function. Thus, an action dependent heuristic dynamic programming (ADHDP) approach that uses kernel-based local action-value function approximation to achieve the model-free optimal consensus control is developed in this paper. The developed approach learns the system dynamics from historical data and avoids the need for system identification. The effectiveness of the developed approach is demonstrated with two simulation examples.