Xingru Qu

In this article, a deep reinforcement learning based three-dimensional path following control approach is proposed for an underactuated autonomous underwater vehicle (AUV). To be specific, kinematic control laws are employed by using the three-dimensional line-of-sight guidance and dynamic control laws are employed by using the twin delayed deep deterministic policy gradient algorithm (TD3), contributing to the surge velocity, pitch angle and heading angle control of an underactuated AUV. In order to solve the chattering of controllers, the action filter and the punishment function are built respectively, which can make control signals stable. Simulations are carried out to evaluate the performance of the proposed control approach. And results show that the AUV can complete the control mission successfully.

This paper presents a novel swarm control framework for path following of multiple underactuated unmanned marine vehicles (UMVs) with uncertain dynamics and unmeasured velocities. Main contributions are as follows: (1) unlike previous master-slave formation control, a swarm system function with distributed and self-organized capability is designed; (2) a center-of-swarm (COS) guidance scheme without vehicle number constraints is proposed for swarm path following, where an improved artificial potential field (APF) using ring-shaped repulsion is further employed for collision avoidance and obstacle avoidance; (3) a nonlinear velocity observer is incorporated into the proposed swarm control framework to estimate the unmeasured velocities, thereby contributing to robust controllers based on fuzzy sliding mode against uncertain dynamics and time-varying disturbances. Simulations are carried out to illustrate the universal applicability and effectiveness of the proposed swarm control framework.