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Design of Robust Adaptive Neural Switching Controller for Robotic Manipulators with Uncertainty and Disturbances

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Abstract

In this paper, we present the robust adaptive neural switching control problem for the application of robotic manipulators with uncertainty and disturbances. The control scheme relaxes the hypothesis that the bounds of external disturbance and approximation errors of neural networks are known. RBF Neural Networks (Radial Basis Function NNs) are adopted to approximate unknown functions of robotic manipulators and an H\(\infty \) controller is designed to enhance system robustness and stabilization due to the existence of the compound disturbance which consists of approximation errors of the neural networks and external disturbance. The adaptive updated laws and the admissible switching signals have been derived from switched multiple Lyapunov function method, so that both system tracking stability and error convergence can be guaranteed in the closed-loop system. Experimental results have demonstrated the improved performance of the proposed control scheme over PD (Proportional Differential) control strategy, which have shown good accuracy of position tracking.

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Authors and Affiliations

  1. School of Mechanical and Electric Engineering, Soochow University, Suzhou, 215021, China

    Lei Yu, Lining Sun & Jun Huang

  2. Henan Provincial Open Laboratory for Control Engineering Key Discipline, Jiaozuo, 454000, China

    Lei Yu

  3. Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Southeast University, Nanjing, 210096, China

    Shumin Fei

  4. Key Laboratory of Advanced Control and Optimization for Chemical Processes, Shanghai, 200237, China

    Jun Huang

  5. Digital Manufacture Technology Key Laboratory of JiangSu Province, Huaian, 223003, China

    Gang Yang

Authors
  1. Lei Yu
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  2. Shumin Fei
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  3. Lining Sun
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  4. Jun Huang
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  5. Gang Yang
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Correspondence to Lei Yu.

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Yu, L., Fei, S., Sun, L. et al. Design of Robust Adaptive Neural Switching Controller for Robotic Manipulators with Uncertainty and Disturbances. J Intell Robot Syst 77, 571–581 (2015). https://doi.org/10.1007/s10846-013-0008-3

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  • DOI: https://doi.org/10.1007/s10846-013-0008-3

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