Enclosing Control of UAV Swarm Based on the Improved Pigeon Inspired Optimization with Variable Weights

In the process of tracking and enclosing the moving targets by UAV (unmanned aerial vehicle) swarms, the fixed size and altitude of the formation often lead to the target escaping the encirclement. To address this problem, a moving target enclosing control framework based on an improved PIO (pigeon inspired optimization) algorithm is proposed. By observing and analyzing the cooperative hunting behaviors of natural community, a variable formation control strategy is introduced to track and enclose the moving targets with uncertain speeds. Additionally, an improved PIO algorithm is proposed to optimize the distributed control parameters based on bearing information, and overcome the drawback of the traditional PIO algorithm easily falling into local optima. Simulation test results demonstrate that under the proposed enclosing control framework, the UAV swarm can successfully enclose the moving targets while achieving adaptive changes in formation size and altitude. Compared with traditional bio-inspired optimization algorithms, the improved PIO algorithm can quickly converge and find better solutions, thereby enhancing the control quality of UAV swarm in enclosing, and validating the effectiveness of the proposed method.