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Robust Sliding Mode Fuzzy Control of Industrial Robots Using an Extended Kalman Filter Inverse Kinematic Solver

Author

Listed:
  • Mojtaba Ahmadieh Khanesar

    (Faculty of Engineering, University of Nottingham, Nottingham NG7 2RD, UK)

  • David Branson

    (Faculty of Engineering, University of Nottingham, Nottingham NG7 2RD, UK)

Abstract

This paper presents a sliding mode fuzzy control approach for industrial robots at their static and near static speed (linear velocities less than 5 cm/s). The extended Kalman filter with its covariance resetting is used to translate the coordinates from Cartesian to joint angle space. The translated joint angles are then used as a reference signal to control the industrial robot dynamics using a sliding mode fuzzy controller. The stability and robustness of the proposed controller is proven using an appropriate Lyapunov function in the presence of parameter uncertainty and unknown dynamic friction. The proposed controller is simulated on a 6-DOF industrial robot, namely the Universal Robot-UR5, considering the maximum allowable joint torques. It is observed that the proposed controller can successfully control UR5 under uncertainties in terms of unknown dynamic friction and parameter uncertainties. The tracking performance of the proposed controller is compared with that of the sliding mode control approach. The simulation results demonstrate superior performance of the proposed approach over the sliding mode control method in the presence of uncertainties.

Suggested Citation

  • Mojtaba Ahmadieh Khanesar & David Branson, 2022. "Robust Sliding Mode Fuzzy Control of Industrial Robots Using an Extended Kalman Filter Inverse Kinematic Solver," Energies, MDPI, vol. 15(5), pages 1-17, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:5:p:1876-:d:763453
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    References listed on IDEAS

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    1. Mateusz Pietrala & Piotr Leśniewski & Andrzej Bartoszewicz, 2021. "Sliding Mode Control with Minimization of the Regulation Time in the Presence of Control Signal and Velocity Constraints," Energies, MDPI, vol. 14(10), pages 1-23, May.
    2. Jinhong Sun & Xiangdang Xue & Ka Wai Eric Cheng, 2019. "Fuzzy Sliding Mode Wheel Slip Ratio Control for Smart Vehicle Anti-Lock Braking System," Energies, MDPI, vol. 12(13), pages 1-22, June.
    3. Jiachun Lin & Yuteng Zhao & Pan Zhang & Junjie Wang & Hao Su, 2021. "Research on Compound Sliding Mode Control of a Permanent Magnet Synchronous Motor in Electromechanical Actuators," Energies, MDPI, vol. 14(21), pages 1-17, November.
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    Citations

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    Cited by:

    1. Yassine Bouteraa & Khalid A. Alattas & Obaid Alshammari & Sondess Ben Aoun & Mohamed Amin Regaieg & Saleh Mobayen, 2022. "Interval Fuzzy Type-2 Sliding Mode Control Design of Six-DOF Robotic Manipulator," Mathematics, MDPI, vol. 10(24), pages 1-52, December.
    2. Mai The Vu & Khalid A. Alattas & Yassine Bouteraa & Reza Rahmani & Afef Fekih & Saleh Mobayen & Wudhichai Assawinchaichote, 2022. "Optimized Fuzzy Enhanced Robust Control Design for a Stewart Parallel Robot," Mathematics, MDPI, vol. 10(11), pages 1-36, June.
    3. Yan Yang & Yeqin Wang & Weixing Zhang & Zhenghao Li & Rui Liang, 2022. "Design of Adaptive Fuzzy Sliding-Mode Control for High-Performance Islanded Inverter in Micro-Grid," Energies, MDPI, vol. 15(23), pages 1-25, December.
    4. Ayman A. Aly & Mai The Vu & Fayez F. M. El-Sousy & Ahmed Alotaibi & Ghassan Mousa & Dac-Nhuong Le & Saleh Mobayen, 2022. "Fuzzy-Based Fixed-Time Nonsingular Tracker of Exoskeleton Robots for Disabilities Using Sliding Mode State Observer," Mathematics, MDPI, vol. 10(17), pages 1-19, September.

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