Robust adaptive neural control for a class of uncertain non-linear time-delay systems with unknown dead-zone non-linearity
Robust adaptive neural control for a class of uncertain non-linear time-delay systems with unknown dead-zone non-linearity
- Author(s): J. Wang and J. Hu
- DOI: 10.1049/iet-cta.2010.0633
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- Author(s): J. Wang 1 and J. Hu 1
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View affiliations
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Affiliations:
1: Engineering College, Air Force Engineering University, Xi'an, People's Republic of China
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Affiliations:
1: Engineering College, Air Force Engineering University, Xi'an, People's Republic of China
- Source:
Volume 5, Issue 15,
13 October 2011,
p.
1782 – 1795
DOI: 10.1049/iet-cta.2010.0633 , Print ISSN 1751-8644, Online ISSN 1751-8652
A robust adaptive neural network controller is proposed for a class of uncertain non-linear time-delay systems in strict feedback form with both completely unknown control gains and unknown non-symmetric dead-zone non-linearity based on backstepping design. The proposed design approach does not require a priori knowledge of the signs of the unknown control gains. The unknown time delays are compensated for constructing appropriate Lyapunov–Krasovskii functionals. By utilising integral Lyapunov design and sliding-mode control strategy, the controller singularity problem and the effect of dead-zone input non-linearity are avoided perfectly. From Lyapunov stability theorem, it is proved that the proposed design approach is able to guarantee semi-globally uniformly ultimately boundedness of all the signals in the closed-loop system, and the tracking error of the system is proven to be converged to a small neighbourhood of the origin. The simulation results demonstrate the effectiveness of the proposed approach.
Inspec keywords: variable structure systems; nonlinear control systems; closed loop systems; neurocontrollers; uncertain systems; robust control; control system synthesis; Lyapunov methods; delays; adaptive control; feedback
Other keywords:
Subjects: Stability in control theory; Neurocontrol; Nonlinear control systems; Distributed parameter control systems; Multivariable control systems; Self-adjusting control systems
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