Distributed short-term predictive control for AUV clusters in underwater cooperative hunting tasks

Abstract

A distributed short-term predictive control method is proposed to be applied to autonomous underwater vehicle (AUV) clusters for underwater cooperative hunting tasks. To support AUV clusters in unknown underwater dynamic environments, the constraints of information acquisition, AUV motion constraints, collision avoidance, and smoothness requirements of motion paths in the tasks are considered. First, a distributed multi-constraint optimization model including target attraction, interactions between AUVs, continuous obstacle avoidance, and control effort is proposed. Effective information between AUVs is extended under limited information conditions. Second, each AUV solves the distributed optimization problem and determines the desired trajectory in real time using information from the prediction horizon. In the simulation experiments, the proposed method is demonstrated to provide at least 12.19%, 11.08%, and 79.89% improvement in terms of task completion time, energy consumption, and path smoothing, respectively, when compared to other methods under the same information conditions. The robustness of the proposed method under different noise conditions is verified by positioning noise experiments, and the task can still be accomplished even if the noise level reaches 75% of the safe distance. The adaptability of the proposed method is demonstrated by simulations in different environments.