Adaptive Neural Fault-Tolerant Control for USV With the Output-Based Triggering Approach

This paper presents an adaptive neural fault-tolerant control algorithm for the path-following activity of the underactuated surface vehicle (USV) using the novel output-based triggering approach. In the algorithm, the event-triggered mechanism is designed utilizing the attitude states from the kine...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology 2022-07, Vol.71 (7), p.6948-6957
Main Authors: Zhang, Guoqing, Chu, Shengjia, Zhang, Weidong, Liu, Cheng
Format: Article
Language:English
Subjects:
Actuators
Adaptive control
Algorithms
Artificial neural networks
Control theory
Event-triggered control
Fault tolerance
Fault tolerant systems
fault-tolerant control
Feedback control
Kinematics
Neural networks
Observers
path-following
Surface vehicles
Surges
underactuated surface vehicle
Voltage control
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Summary:This paper presents an adaptive neural fault-tolerant control algorithm for the path-following activity of the underactuated surface vehicle (USV) using the novel output-based triggering approach. In the algorithm, the event-triggered mechanism is designed utilizing the attitude states from the kinematics aspect of USV. Both the control inputs and the related calculation thread are implemented only at the trigger instants. Furthermore, neural networks (NNs) are employed to remodel the model uncertainties, and the adaptive observer is developed to estimate and compensate for the effect of the unknown actuator faults. With the direct Lyapunov theorem, the semi-global uniformly ultimately bounded (SGUUB) stability can be guaranteed for the closed-loop system in aspects of both the trigger instant and the continuous interval. The comparison experiment has been illustrated to verify the effectiveness of the proposed algorithm, which can effectively improve the information transmission performance and the fault-tolerant capability.
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2022.3167038