TY - JOUR
T1 - Cooperative Quantized Event-Based Fuzzy Tracking Control of Nonlinear Autonomous Surface Vehicles With Prescribed Performance
AU - Dong, Shanling
AU - Lai, Zhiyi
AU - Wu, Zheng Guang
AU - Liu, Meiqin
AU - Chen, Guanrong
N1 - Publisher Copyright:
© 2004-2012 IEEE.
PY - 2025
Y1 - 2025
N2 - This paper investigates the cooperative fuzzy tracking control of nonlinear autonomous surface vehicles with input quantization and event-triggered mechanism. The proposed cooperative control scheme consists of two parts: (i) the distributed observer and (ii) the dynamic event-based fuzzy tracking controller. The distributed observer is designed to obtain the nonlinear leader’s trajectory information on a directed communication topology. Under this framework, uncertain nonlinearity within the vehicle model is approximated through fuzzy logic systems, and, according to the state of the distributed observer, the dynamic event-based adaptive fuzzy tracking control law is developed with an input switching quantizer. Furthermore, a prescribed performance method is introduced to ensure the transient performance of tracking errors and obtain zero-tracking errors ultimately, which is proved through Lyapunov stability theory. Finally, the effectiveness of the proposed control strategy is verified by simulation experiments.
AB - This paper investigates the cooperative fuzzy tracking control of nonlinear autonomous surface vehicles with input quantization and event-triggered mechanism. The proposed cooperative control scheme consists of two parts: (i) the distributed observer and (ii) the dynamic event-based fuzzy tracking controller. The distributed observer is designed to obtain the nonlinear leader’s trajectory information on a directed communication topology. Under this framework, uncertain nonlinearity within the vehicle model is approximated through fuzzy logic systems, and, according to the state of the distributed observer, the dynamic event-based adaptive fuzzy tracking control law is developed with an input switching quantizer. Furthermore, a prescribed performance method is introduced to ensure the transient performance of tracking errors and obtain zero-tracking errors ultimately, which is proved through Lyapunov stability theory. Finally, the effectiveness of the proposed control strategy is verified by simulation experiments.
KW - Nonlinear system control
KW - cooperative fuzzy control
KW - dynamic event-triggered mechanism
KW - input quantization
KW - prescribed performance
UR - https://www.scopus.com/pages/publications/105008782030
U2 - 10.1109/TASE.2025.3581224
DO - 10.1109/TASE.2025.3581224
M3 - 文章
AN - SCOPUS:105008782030
SN - 1545-5955
VL - 22
SP - 17594
EP - 17605
JO - IEEE Transactions on Automation Science and Engineering
JF - IEEE Transactions on Automation Science and Engineering
ER -