Distributed Adaptive Formation Control with Collision Avoidance and Connectivity Maintenance of Multiple Autonomous Surface Vehicles

In this paper, a distributed adaptive formation control with collision avoidance and connectivity maintenance strategy is proposed for multiple autonomous surface vehicles (ASVs) subject to unknown uncertainties and disturbances. Four control objectives of formation generation, formation maintenance, collision avoidance, and connectivity maintenance can be achieved simultaneously by integrating the artificial potential field (APF) methods into the leader-follower strategies. While the two stages of distributed formation control, namely formation generation and formation maintenance, can be achieved through information exchange among inter-vehicles. The APF method provides auxiliary repulsive and attractive force to assist the ASVs in achieving collision avoidance and connectivity maintenance among inter-vehicles. Furthermore, each vehicle faces unknown dynamics due to model uncertainty and environmental disturbances, which increases the complexity of the system and hampers the achievement of control objectives. To solve these problems, neural network (NN) technologies are employed and their learning parameters are designed in scalar form. Only one scalar learning parameter instead of the tremendous weight matrix of NN needs to be adaptively updated for each vehicle. In this way, the computational burden can be greatly reduced. According to Lyapunov stability theory and graph theory, the proposed controller can be proved to accomplish the four control objectives. Several sets of comparative simulations verify the effectiveness of the proposed controller.