Intelligent cooperative control method for multi-rotor unmanned aerial vehicle swarm incorporating target allocation optimization strategies

The increasing complexity and dynamic nature of modern mission environments pose significant challenges to the coordinated control of multirotor unmanned aerial vehicle (UAV) swarms. Variations in environmental conditions-such as weather, terrain, and electromagnetic interference-combined with shifting mission requirements demand adaptive and intelligent control strategies to ensure efficient and robust swarm performance. Traditional methods often fall short in addressing real-time target allocation under resource constraints and maintaining stable formation control in the presence of dynamic disturbances. This study proposes a novel intelligent cooperative control method that integrates an optimized target allocation strategy with an elastic formation model to enhance the adaptability, precision, and robustness of multirotor UAV swarm operations.

The proposed method comprises two main components: target allocation optimization and cooperative formation control. First, a multiobjective target allocation function is formulated, considering path cost and average mission time, and the allocation problem is solved using a hybrid Hungarian-genetic algorithm, which combines the efficiency of the Hungarian method with the global search capability of genetic algorithms. This approach accommodates various UAV-to-target ratio scenarios and incorporates constraints such as maximum range, mission time limits, task priorities, and dynamic threat avoidance. Second, a dual-layer cooperative control architecture is designed. An elastic formation model is developed by integrating formation principles with a virtual elastic structure to maintain swarm cohesion. A sliding mode controller based on an improved exponential reaching law is designed for formation control, while a desired position controller translates formation commands into individual UAV actions. The stability of the controller is verified using Lyapunov theory. Simulations are conducted in a combined MATLAB/Simulink and Gazebo environment using quadrotor models based on industrial UAV specifications under various conditions, including static obstacles and dynamic threat zones.

Experimental results demonstrate that the proposed method achieves high-quality target allocation under dynamic conditions. Energy efficiency analysis shows that the average remaining battery power remains stable even after multiple task allocations, indicating effective load balancing. Compared to traditional methods, the proposed method reduces maximum power consumption and average flight distance, thereby improving overall energy efficiency. In terms of cooperative control performance, the proposed method achieves a 20.35% improvement in control accuracy and a 15.42% reduction in mission completion time relative to benchmark methods. Trajectory comparisons show that UAV swarms using the proposed method successfully avoid hazardous zones and reach targets more consistently. Robustness tests under simulated disturbances-such as localized turbulence and communication link interruptions-reveal that the proposed method maintains higher mission completion rates, greater average remaining power, and fewer UAV failures than existing approaches.

This study presents an intelligent cooperative control method for multirotor UAV swarms that integrates optimized target allocation with an elastic formation control framework. The hybrid Hungarian-genetic algorithm enables efficient and adaptive task assignment, while the dual-layer controller enhances formation stability and responsiveness under dynamic conditions. Experimental validation confirms that the proposed method significantly improves control precision, mission efficiency, and system robustness, even in the presence of environmental disturbances and communication disruptions. The proposed method offers a viable solution for enhancing the autonomous cooperative capabilities of UAV swarms in complex operational scenarios.