TY - GEN
T1 - RBFNN-based event-triggered MFAC for discrete-time nonlinear systems with disturbances and data quantization
AU - Wang, Xianming
AU - Zhang, Tu
AU - Wu, Xingzheng
AU - Qin, Wen
AU - Li, Liwei
AU - Shen, Mouquan
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - This parper concentrates on neural network-based event-triggered MFAC for discrete-time nonlinear systems with disturbances and data quantization. First, the logarithmic quantizer is adopted to quantify the input signal. By uti-lizing the radial basis function neural network, an estimation method is presented to estimate the unknown pseudopartial derivative and disturbances. To reduce the influence of disturbances, an improved control law is built on the estimated disturbances. By using the tracking error and the estimated disturbance, an event-triggered mechanism is designed to save the communication resources. Conditions are presented to guarantee that the tracking error is uniformly ultimately bounded. The validity of the proposed method is verified by simulation example.
AB - This parper concentrates on neural network-based event-triggered MFAC for discrete-time nonlinear systems with disturbances and data quantization. First, the logarithmic quantizer is adopted to quantify the input signal. By uti-lizing the radial basis function neural network, an estimation method is presented to estimate the unknown pseudopartial derivative and disturbances. To reduce the influence of disturbances, an improved control law is built on the estimated disturbances. By using the tracking error and the estimated disturbance, an event-triggered mechanism is designed to save the communication resources. Conditions are presented to guarantee that the tracking error is uniformly ultimately bounded. The validity of the proposed method is verified by simulation example.
KW - Event-triggered control
KW - RBF neural network
KW - model-free adaptive control
UR - http://www.scopus.com/inward/record.url?scp=85149505859&partnerID=8YFLogxK
U2 - 10.1109/CCDC55256.2022.10034390
DO - 10.1109/CCDC55256.2022.10034390
M3 - 会议稿件
AN - SCOPUS:85149505859
T3 - Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022
SP - 4042
EP - 4047
BT - Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 34th Chinese Control and Decision Conference, CCDC 2022
Y2 - 15 August 2022 through 17 August 2022
ER -