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Adaptive Neural Network Finite-Time Control for Uncertain Robotic Manipulators

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Abstract

An adaptive neural network finite-time controller (NNFTC) for a class of uncertain nonlinear systems is proposed by using the backstepping method, which employs an adaptive neural network (NN) system to approximate the structure uncertainties and uses a variable structure term to compensate the approximation errors, thus improving the robustness of the system to external disturbances. The controller is then applied to uncertain robotic manipulators, with a control objective of driving the system state to the original equilibrium point. It is proved that the closed-loop system is finite-time stable. Moreover, simulated and experimental results indicate that the proposed NNFTC is effective and robust.

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Authors and Affiliations

  1. Guangdong Ocean University, Zhanjiang, 524088, Guangdong, China

    Haitao Liu

  2. South China University of Technology, Guangzhou, 510640, Guangdong, China

    Tie Zhang

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  1. Haitao Liu
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  2. Tie Zhang
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Correspondence to Haitao Liu.

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Liu, H., Zhang, T. Adaptive Neural Network Finite-Time Control for Uncertain Robotic Manipulators. J Intell Robot Syst 75, 363–377 (2014). https://doi.org/10.1007/s10846-013-9888-5

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  • DOI: https://doi.org/10.1007/s10846-013-9888-5

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