Receding horizon control for multiple UAV formation flight based on modified brain storm optimization

Formation flight for unmanned aerial vehicles (UAVs) is a rather complicated global optimum problem. In the global optimum problem, the complex relationship between the controller parameters and the performance index, and the different kinds of constrains under complex combat field environment are taken into account. Brain storm optimization (BSO) is a brand-new swarm intelligence optimization algorithm inspired by a human being’s behavior of brainstorming. In this paper, in allusion to the drawbacks that the basic BSO algorithm traps into local optimum easily and has a slow convergent speed, some novel designs are proposed to enhance the performance of the optimization algorithm. The modified BSO is applied to solve the optimization problem based on the nonlinear Receding horizon control (RHC) mode of UAVs to seek the RHC control parameters for UAV formation flight. Series of comparative experimental results are presented to show the feasibility, validity, and superiority of our proposed method.