Decentralized adaptive flocking control algorithm with avoiding collision and preserving connectivity for crowded UUV swarm with uncertainties and input saturation

Abstract

This paper investigates a decentralized adaptive flocking control for crowded unmanned underwater vehicle (UUV) swarm in the presence of uncertainties and input saturation. Consider a realistic model of small-size and low-cost UUV with limited communication, it is assumed that only one UUV can access global information (desired path) and each UUV only communicates with its neighbors. To keep all UUVs connected in a crowded swarm, the center of flocking (COF) of each UUV is identified using bio-inspired consensus, thereby a leader-follower flocking controller is proposed to guarantee the collision avoidance and connectivity preservation with bounded fuzzy potential fields at the kinematic level. An adaptive neural networks (NNs) controller is further developed to track the desired path, where an additional control term is incorporated to handle input saturation at the kinetic level. Stability analysis demonstrated that all closed-loop signals are uniformly ultimately bounded. The main contributions of this paper are three-folded. (i) A decentralized algorithm without a prior connectivity assumption on a directed spanning tree is proposed. (ii) Collision avoidance and connectivity preservation can be achieved simultaneously. (iii) An adaptive controller which is robust against model uncertainties and disturbances is developed. Simulation results illustrate the effectiveness of the proposed approach.