Evolutionary Pinning Control and Its Application in UAV Coordination

Abstract

Maximizing the controllability of complex networks by selecting appropriate nodes and designing suitable control gains is an effective way to control distributed complex networks. In this paper, some novel particle swarm optimization (PSO) approaches are developed to enhance the controllability of distributed networks. The proposed PSO algorithm is combined with a global search scheme and a modified simulated binary crossover (MSBX). In addition, the node importance-based method is introduced to study the controllability of distributed complex networks. A set of experiments show that the PSO with the global search and the MSBX (PSO-GSBX) can outperform some well-known evolutionary algorithms and pinning schemes. Following the PSO-GSBX approach, some interesting findings about pinned nodes, coupling strengths and the eigenvalues for enhancing the controllability of distributed networks are revealed. The obtained results and methods are applied in unmanned aerial vehicle (UAV) coordination to show their effectiveness. These findings will help to understand controllability of complex networks and can be applied in control science and industrial system.