Distributed Composite Learning Adaptive Fault-Tolerant Control for Multiple Marine Vehicles With Event-Triggered Communication

The paper investigates the distributed cooperative control problem for multiple marine vehicles in the presence of sensor faults and limited communication networks. The composite learning adaptive fault-tolerant control algorithm is designed via the novel prediction error to tackle the perturbation incurred by possible sensor faults. To the best of the authors' knowledge, the application of composite adaptive control to deal with sensor faults is the first attempt for marine vehicle systems. Besides, composite neural networks (NNs) are constructed to reconstruct model uncertainties. Different from the existing schemes, a concise event-triggered communication mechanism is proposed to optimize inter-vehicle communication. In particular, the time-varying parameter is introduced to dynamically adjust the event-triggered virtual control law according to the feedback of real-time actual tracking error, thereby enhancing control accuracy. Only the attitude information between the multiple vehicle members is aperiodically exchanged at sampling instants, saving communication resources. Based on the Lyapunov criterion, the semi-global uniformly ultimately bounded (SGUUB) stability of the closed-loop system can be guaranteed covering both trigger instants and continuous intervals. Two experiment results are illustrated to verify the effectiveness of the proposed scheme.