Adaptive neural output feedback control for stochastic nonlinear time-delay systems with unknown control directions

This paper first focuses on the problem of adaptive output feedback stabilization for a more general class of stochastic nonlinear time-delay systems with unknown control directions. By using a linear state transformation, the original system is transformed to a new system for which control design b...

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Published inNeural computing & applications Vol. 25; no. 7-8; pp. 1979 - 1992
Main Authors Yu, Zhaoxu, Li, Shugang, Du, Hongbin
Format Journal Article
LanguageEnglish
Published London Springer London 01.12.2014
Springer
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Abstract This paper first focuses on the problem of adaptive output feedback stabilization for a more general class of stochastic nonlinear time-delay systems with unknown control directions. By using a linear state transformation, the original system is transformed to a new system for which control design becomes feasible. Then a novel adaptive neural network (NN) output feedback control strategy, which only contains one adaptive parameter, is developed for such systems by combining the input-driven filter design, the backstepping technique, the NN’s parameterization, the Nussbaum gain function method and the Lyapunov–Krasovskii approach. The proposed control design guarantees that all signals in the closed-loop systems are 4-moment (or 2-moment) semi-globally uniformly bounded. Finally, two simulation examples are given to demonstrate the effectiveness and the applicability of the proposed control design.
AbstractList This paper first focuses on the problem of adaptive output feedback stabilization for a more general class of stochastic nonlinear time-delay systems with unknown control directions. By using a linear state transformation, the original system is transformed to a new system for which control design becomes feasible. Then a novel adaptive neural network (NN) output feedback control strategy, which only contains one adaptive parameter, is developed for such systems by combining the input-driven filter design, the backstepping technique, the NN’s parameterization, the Nussbaum gain function method and the Lyapunov–Krasovskii approach. The proposed control design guarantees that all signals in the closed-loop systems are 4-moment (or 2-moment) semi-globally uniformly bounded. Finally, two simulation examples are given to demonstrate the effectiveness and the applicability of the proposed control design.
Author Yu, Zhaoxu
Li, Shugang
Du, Hongbin
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  givenname: Shugang
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  fullname: Du, Hongbin
  organization: Key Laboratory of Advanced Control and Optimization for Chemical Process of Ministry of Education, East China University of Science and Technology
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Issue 7-8
Keywords Unknown control direction
Neural network
Time-varying delay
Stochastic nonlinear systems
Output feedback
Non linear control
Delay control
Feedback regulation
Control synthesis
Parameterization
Adaptive control
Adaptive method
Stochastic system
Time varying system
Uncertain system
Delay time
Closed loop
Delay system
Non linear system
Recursive algorithm
Backstepping control
Closed feedback
Neurocontrollers
Linear system
Linear transformation
Lyapunov function
Payoff function
Language English
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Snippet This paper first focuses on the problem of adaptive output feedback stabilization for a more general class of stochastic nonlinear time-delay systems with...
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SubjectTerms Adaptative systems
Applied sciences
Artificial Intelligence
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Computer science; control theory; systems
Control system analysis
Control system synthesis
Control theory. Systems
Data Mining and Knowledge Discovery
Exact sciences and technology
Image Processing and Computer Vision
Modelling and identification
Original Article
Probability and Statistics in Computer Science
Title Adaptive neural output feedback control for stochastic nonlinear time-delay systems with unknown control directions
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