A novel approach to integrate potential field and interval type-2 fuzzy learning for the formation control of multiple autonomous underwater vehicles

Underwater vehicles coordination and formation have attracted increasingly attentions since their great potential on the real-world applications. However, usually such vehicles are underactuated and with very different environmental difficulties, which are different from those vehicles (robots) on the land. This study proposes a novel approach to integrate potential field and interval type-2 fuzzy learning algorithm for autonomous underwater vehicles formation control based on formation system framework. For the system nonlinearity and complicated environment, support vector machine has been applied to generate optimal rules for the type-2 fuzzy systems. This approach can generate optimal and reasonable formation rules on the face of different situations through classification. Furthermore, reinforcement learning has been combined with fuzzy systems to deal with limited communication state during formation. Therefore, autonomous underwater vehicles can not only execute actions through the evaluation, but also can avoid coupling character between communication state and potential field. Finally, simulations and experiments results have been extensively performed to validate the proposed methods.