Adaptive Dual-Channel Event-Triggered Fuzzy Control for Autonomous Underwater Vehicles With Multiple Obstacles Environment
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Liu, Shang, Ronghui Zhang, Dezong Zhao, and Houbing Song. “Adaptive Dual-Channel Event-Triggered Fuzzy Control for Autonomous Underwater Vehicles With Multiple Obstacles Environment.” IEEE Transactions on Intelligent Transportation Systems, 2024, 1–14. https://doi.org/10.1109/TITS.2024.3387999.
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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.