Adaptive Dual-Channel Event-Triggered Fuzzy Control for Autonomous Underwater Vehicles With Multiple Obstacles Environment
Shang Liu, Ronghui Zhang, Dezong Zhao, Houbing Song
Abstract
This article investigates the formation control of autonomous underwater vehicles (AUVs) suffering from unknown sea loads, unmoulded structure, limited communication and multiple static and moving obstacles. Given the challenge, a novel adaptive dual-channel event-triggered control scheme is proposed for formation tracking and obstacles avoidance. To economize the communication resources, the dual-channel event-triggered mechanism is designed in the sensor-to-controller and controller-to-actuator channels respectively. By adopting the approximation of fuzzy systems in the form of one-parameter integrated learning, the uncertainties consisted of the unmoulded structure and unknown sea loads are compressed together to be compensated online, which ensures a lower computational cost. To solve the multiple obstacles, the modified artificial potential field approach is employed, and the derived repulsive potential field can ensure that the multi-AUV formation can avoid obstacles smoothly regardless of static or moving obstacles. It is showed by the Lyapunov stability theorem that the tracking errors are guaranteed to be semi-globally uniformly ultimately bounded. Finally, three simulation examples illustrate the effectiveness and superiority of the proposed scheme.