Scaled Consensus and Reference Tracking in Multiagent Networks With Constraints

Abstract

This paper considers the scaled consensus problem for networks of groups of agents having state constraints. We first address the problem by using a gradient projection approach and design distributed consensus protocols that implement state restriction and steer agents asymptotically to proportions in terms of pre-assigned scales. We then extend the method to multiagent systems with static reference values. We propose and analyze sufficient conditions on agent dynamics such that scaled reference tracking problem is solved. The results are applied to a ship steering system and numerical examples to verify the effectiveness of the proposed cooperative coordination system scheme.