Hybrid ISMC-PIO and Receding Horizon Control for UAVs Formation

Abstract

Unmanned aerial vehicles (UAVs) formation can achieve considerable missions. Control strategy plays an important role in UAVs formation. In this paper, a receding horizon control (RHC) for UAVs formation based on independent search and multi-area convergence pigeon-inspired optimization (ISMC-PIO) is proposed. To minimize the cost value for measuring UAVs formation process, the modified pigeon-inspired optimization (PIO) is utilized by converting the RHC parameters and performance index for UAVs formation problem to a global optimization problem. PIO is a novel bioinspired algorithm. However, basic PIO has the disadvantages of slower convergence speed and falling into local optimum easily. The modified PIO has faster convergence rate and global search ability by importing independent search factor and multi-area convergence strategy. Numerous experiments are implemented to prove that the ISMC-PIO can converge quickly and obtain a better cost value.