TY - JOUR
T1 - Decentralized Adaptive Neural Approximated Inverse Control for a Class of Large-Scale Nonlinear Hysteretic Systems with Time Delays
AU - Zhang, Xiuyu
AU - Wang, Yue
AU - Chen, Xinkai
AU - Su, Chun Yi
AU - Li, Zhi
AU - Wang, Chenliang
AU - Peng, Yaxuan
N1 - Funding Information:
Manuscript received December 13, 2017; revised March 4, 2018; accepted April 2, 2018. Date of publication May 4, 2018; date of current version November 19, 2019. This work was supported in part by the National Natural Science Foundation of China under Grant 61673101, in part by the Science and Technology Project of Jilin Province under Grant 20180201009SF, Grant 20170414011GH, Grant 20180201004SF, and Grant 20180101069JC, and in part by the JSPS under Grant C-15K06152 and Grant 14032011-000073. This paper was recommended by Associate Editor S. Tong. (Corresponding authors: Yaxuan Peng; Xinkai Chen.) X. Zhang, Y. Wang, and Y. Peng are with the Department of Automation Engineering, Northeast Electric Power University, Jilin City 132012, China (e-mail: zhangxiuyu80@163.com; wangyue930708@163.com; pengyaxuan_64@163.com).
Publisher Copyright:
© 2013 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - This paper proposes a decentralized neural adaptive dynamic surface approximated inverse control (DNADSAIC) scheme for a class of large-scale time-delay systems with hysteresis nonlinearities as input. The decentralized control problem under the case only the outputs are measurable is solved by utilizing the radial basis function neural networks approximator and the hysteresis approximated inverse compensator. Also, with the help of finite covering lemma, the traditional Krasovskii functionals are dropped when coping with the delays, leading to the removal of the assumptions on the functions with time-delay states and the acquisition of the arbitrarily small ${L}_{{\infty }}$ tracking performance of each hysteretic subsystem with time delays. The analysis of stabilities guarantees all the signals of the closed-loop systems are semiglobally uniformly ultimately bounded. Simulation results illustrate the efficiency of the proposed DNADSAIC scheme.
AB - This paper proposes a decentralized neural adaptive dynamic surface approximated inverse control (DNADSAIC) scheme for a class of large-scale time-delay systems with hysteresis nonlinearities as input. The decentralized control problem under the case only the outputs are measurable is solved by utilizing the radial basis function neural networks approximator and the hysteresis approximated inverse compensator. Also, with the help of finite covering lemma, the traditional Krasovskii functionals are dropped when coping with the delays, leading to the removal of the assumptions on the functions with time-delay states and the acquisition of the arbitrarily small ${L}_{{\infty }}$ tracking performance of each hysteretic subsystem with time delays. The analysis of stabilities guarantees all the signals of the closed-loop systems are semiglobally uniformly ultimately bounded. Simulation results illustrate the efficiency of the proposed DNADSAIC scheme.
KW - Decentralized adaptive control
KW - dynamic surface approximated inverse control
KW - hysteresis
KW - large-scale system
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U2 - 10.1109/TSMC.2018.2827101
DO - 10.1109/TSMC.2018.2827101
M3 - Article
AN - SCOPUS:85046483639
SN - 2168-2216
VL - 49
SP - 2424
EP - 2437
JO - IEEE Transactions on Systems, Man, and Cybernetics: Systems
JF - IEEE Transactions on Systems, Man, and Cybernetics: Systems
IS - 12
M1 - 8354937
ER -