Scaled consensus and reference tracking in multiagent networks with constraints

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

Transcale average consensus of directed multi-vehicle networks with fixed and switching topologies

<p>This paper contributes to solving a class of so-called ‘transcale average consensus’ problems for directed networks of vehicles with single-integrator dynamics. It generalises the idea of average consensus by taking into account the measurement scale of every vehicle's information state. Using the nearest neighbour-interaction rules, distributed algorithms are presented which are shown with the ability to achieve the transcale average consensus for directed networks associated with balanced graphs. Such consensus results are applicable to networks in the presence of both fixed topology and switching topologies, which are also illustrated via simulation tests. Moreover, improved algorithms and results of consensus are given for directed networks associated with unbalanced graphs.</p