caa a22 4500 606159924 CHVBK 20210128100627.0 cr unu---uuuuu 210128e20151101xx s 000 0 eng 10.1007/s00521-015-1855-6 doi (NATIONALLICENCE)springer-10.1007/s00521-015-1855-6 Adaptive neural control with intercepted adaptation for time-delay saturated nonlinear systems [Elektronische Daten] [Shigen Gao, Bin Ning, Hairong Dong] In this paper, the adaptive neural control is proposed for a class of single-input-single-output nonlinear systems with state delay and input saturation. An intercepted adaptation approach is designed to attenuate the effect caused by the input saturation based on a constructed auxiliary system, and radial basis function neural networks are used in the online learning of unknown dynamics. Lyapunov-Krasovskii function is introduced to deal with the state delay. The proposed control scheme can guarantee semi-globally uniformly boundedness of the closed-loop system as rigorously proved by Lyapunov stability theorem. The ultimate and transient tracking errors will be confined in compact regions. The diameters of these regions can be adjusted to be arbitrarily small by tuning proper design parameters. Illustrative examples are used to demonstrate the effectiveness of the proposed control method. The Natural Computing Applications Forum, 2015 Adaptive neural control nationallicence Nonlinear system nationallicence State delay nationallicence Actuator saturation nationallicence Gao Shigen State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, 100044, Beijing, China aut Ning Bin State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, 100044, Beijing, China aut Dong Hairong State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, 100044, Beijing, China aut Neural Computing and Applications Springer London 26/8(2015-11-01), 1849-1857 0941-0643 26:8<1849 2015 26 521 https://doi.org/10.1007/s00521-015-1855-6 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00521-015-1855-6 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Gao Shigen State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, 100044, Beijing, China aut NATIONALLICENCE 700 1- Ning Bin State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, 100044, Beijing, China aut NATIONALLICENCE 700 1- Dong Hairong State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, 100044, Beijing, China aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/8(2015-11-01), 1849-1857 0941-0643 26:8<1849 2015 26 521