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Output Feedback Adaptive Control for Stochastic Non-strict-feedback System with Dead-zone

Abstract

This paper focuses on the problem of adaptive neural network (NN) control for a class of nonlinear stochastic non-strict feedback system with dead-zone input. A novel adaptive NN output feedback control approach is first proposed for stochastic non-strict feedback nonlinear systems. In order to solve the problem of dead-zone input, a linear decomposition method is proposed. On the basis of the state observer, an output feedback adaptive NN controller is designed by backstepping approach. It is shown that the proposed controller guarantees that all the signals of the closed-loop systems are semi-globally uniformly bounded in probability. Simulation results further illustrate the effectiveness of the proposed approach.

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Correspondence to Yumei Sun.

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Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Recommended by Associate Editor Guangdeng Zong under the direction of Editor Hamid Reza Karimi. This work is supported by the National Natural Science Foundation of China (Grant No. 61873137,61903228 and 41906165) and the Science and Technology Support Plan for Youth Innovation of Colleges and Universities of Shandong Province of China (2019KJI005).

Yumei Sun received her B.Sc. degree in mathematics from Shandong University, Jinan, China, in 2002, an M.Sc. degree in mathematics from Sun Yat-sen University, Guangzhou, China, in 2005, and a Ph.D. degree from Qingdao University Qingdao University, Qingdao, China in 2018. Since 2005, she has been at the Shandong University of Science and Technology, Qingdao, China. Her current research interests include stochastic nonlinear control systems, quantized control, backstepping control, and adaptive fuzzy control.

Bingwei Mao received her B.Sc. degree in mathematics from Shandong University, Jinan, China, in 2002 and her M.Sc. degree in probability and statistics from Beijing Institute of Technology, Beijing, China, in 2005. Then she graduated as a Ph.D. from the Science College of Yanshan University, Qinhuangdao, China in 2011. Her current research focuses in the application of queueing system.

Hongxia Liu received her B.Sc. degree in mathematics from Liaocheng University, Liaocheng, China, in 2002 and her M.Sc. degree in mathematics from Shandong University of Science and Technology, Qingdao, China, in 2008. Her current research interests include stochastic control of nonlinear systems and differential equation dynamic system.

Shaowei Zhou received her B.S. degree from Shandong Normal University, China, and her M.S. and Ph.D. degrees from Shandong University of Science and Technology, China, in 2000, 2006, and 2012, respectively. Since 2000, she has been at Shandong University of Science and Technology, Qingdao, China. Her current research interests include stochastic system control theory, and fuzzy adaptive control.

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Sun, Y., Mao, B., Liu, H. et al. Output Feedback Adaptive Control for Stochastic Non-strict-feedback System with Dead-zone. Int. J. Control Autom. Syst. 18, 2621–2629 (2020). https://doi.org/10.1007/s12555-019-0876-9

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Keywords

  • Adaptive neural control
  • backstepping
  • dead-zone
  • state observer
  • stochastic non-strict feedback nonlinear systems