Event-Triggered Self-Organizing Swarm Control of Distributed Unmanned Surface Vehicles

Abstract

Aiming at autonomous massive transportation by sea, economically condition-based cooperative control solution remains unrevealed and is highly desirable for collective swarming of distributed unmanned surface vehicles (USVs) suffering from narrow-band communication and unstructured unknowns. In this paper, an event-triggered self-organizing swarm control (ESSC) scheme is innovated to flexibly helm a herd of USVs, and features main contributions as follows: 1) A suite of self-organizing swarm mechanism consisting of aggregation, collision avoidance and heading alignment is holistically established, such that emerging behaviors of swarm kinetics can be self-evolved for flexible morphology; 2) Within adaptive dynamic programming framework, an event-triggered optimal solution to USV swarm control is worked out by deriving optimization-oriented event-triggering mechanism from swarm kinetics tracking errors, thereby making a rational balance between channel occupation and tracking accuracy; and 3) Approximately optimal control actions are acquired by employing actor-critic reinforcement learning networks to solve Hamilton-Jacobi-Bellman equation, thereby assuring communication parsimony and control optimality, simultaneously. Performance validations with intensive comparisons to time-triggered methods demonstrate the effectiveness and superiority in terms of tracking accuracy, channel occupancy and control optimality, in addition that extensive application to roundup scenario showcases the proposed ESSC scheme performs feasible extension to wide-range tasks.