Cooperative Control and Intelligent Optimization for Air-Sea Heterogeneous Unmanned Systems

In order to cope with increasingly complex ocean missions, an air-sea heterogeneous unmanned system composed of unmanned aerial vehicles(UAVs), unmanned surface vessels(USVs), and unmanned undersea vehicles(UUVs) was constructed to study the cooperative control problem. For the information exchange problem of a heterogeneous unmanned system, each domain consisted of a leader and multiple followers, where cross-domain communication was required between the leaders of each domain. Meanwhile, for the trajectory planning issue of the leader in each domain, a cooperative trajectory planning algorithm based on the artificial potential field method was proposed for the leader in each domain to reach the target location while avoiding obstacles. For the limited communication resources, an impulsive hierarchical formation control protocol with intermittent communication was designed for followers in each domain, which reduces communication overhead while achieving formation control under obstacle avoidance. Besides, for the multi-objective optimization problem of convergence time and communication energy consumption in cooperative control algorithm, an improved multi-objective quantum-behavior particle swarm optimization algorithm was proposed by designing contraction-expansion coefficient and dynamic dense distance strategy, which was used to intelligently select the impulsive interval for each domain, achieving a good compromise between the convergence time and communication energy consumption of cooperative control algorithm. Simulation results demonstrate that the air-sea heterogeneous unmanned system can achieve formation control while avoiding obstacles and reducing communication overhead, and the proposed algorithm has better convergence and global search ability than the traditional multi-objective quantum-behavior particle swarm optimization algorithm.