Neural network-based compensation control of mobile robots with partially known structure
Neural network-based compensation control of mobile robots with partially known structure
- Author(s): F.G. Rossomando ; C. Soria ; R. Carelli
- DOI: 10.1049/iet-cta.2011.0581
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- Author(s): F.G. Rossomando 1 ; C. Soria 1 ; R. Carelli 1
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View affiliations
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Affiliations:
1: Instituto de Automática (INAUT), Universidad Nacional de San Juan, San Juan, Argentina
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Affiliations:
1: Instituto de Automática (INAUT), Universidad Nacional de San Juan, San Juan, Argentina
- Source:
Volume 6, Issue 12,
16 August 2012,
p.
1851 – 1860
DOI: 10.1049/iet-cta.2011.0581 , Print ISSN 1751-8644, Online ISSN 1751-8652
This study proposes an inverse non-linear controller combined with an adaptive neural network proportional integral (PI) sliding mode using an on-line learning algorithm. The neural network acts as a compensator for a conventional inverse controller in order to improve the control performance when the system is affected by variations on their dynamics and kinematics. Also, the proposed controller can reduce the steady-state error of a non-linear inverse controller using the on-line adaptive technique based on Lyapunov's theory. Experimental results show that the proposed method is effective in controlling dynamic systems with unexpected large uncertainties.
Inspec keywords: Lyapunov methods; inverse problems; nonlinear control systems; mobile robots; learning systems; adaptive control; neurocontrollers; PI control; compensation
Other keywords:
Subjects: Self-adjusting control systems; Nonlinear control systems; Mathematical analysis; Mobile robots; Stability in control theory; Neurocontrol
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