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

被引：34
作者：
Zhang, Guoqing [1 ]
Chu, Shengjia [1 ]
Zhang, Weidong [2 ,3 ]
Liu, Cheng [1 ]
机构：
[1] Dalian Maritime Univ DMU, Nav Coll, Dalian 116026, Liaoning, Peoples R China
[2] Hainan Univ, Sch Informat Engn, Haikou 570228, Hainan, Peoples R China
[3] Shanghai Jiao Tong Univ, Dept Automat, Shanghai 200240, Peoples R China
来源：
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY | 2022年 / 71卷 / 07期
基金：
中国国家自然科学基金;
关键词：
Fault tolerant systems; Fault tolerance; Actuators; Observers; Surges; Artificial neural networks; Voltage control; Event-triggered control; fault-tolerant control; path-following; underactuated surface vehicle; PATH-FOLLOWING CONTROL; UNDERACTUATED SHIPS; TRAJECTORY TRACKING; SYSTEMS; PERFORMANCE;
D O I：
10.1109/TVT.2022.3167038
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
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.
引用
收藏
页码：6948 / 6957
页数：10
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