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Adaptive Particle Swarm Optimization of PID Gain Tuning for Lower-Limb Human Exoskeleton in Virtual Environment

Author

Listed:
  • Mohammad Soleimani Amiri

    (Department of Mechanical and Manufacturing Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, Selangor 43600, Malaysia)

  • Rizauddin Ramli

    (Department of Mechanical and Manufacturing Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, Selangor 43600, Malaysia)

  • Mohd Faisal Ibrahim

    (Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, Selangor 43600, Malaysia)

  • Dzuraidah Abd Wahab

    (Department of Mechanical and Manufacturing Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, Selangor 43600, Malaysia)

  • Norazam Aliman

    (Mechanical Engineering Department, Politeknik Sultan Azlan Shah, Perak, Behrang 35950, Malaysia)

Abstract

Tuning of a proportional-integral-derivative (PID) controller for a complex multi-joint structure, such as an exoskeleton, using conventional methods is difficult and imprecise. In this paper, an optimal PID tuning method for a 3-dimensional model of a lower-limb human exoskeleton in gait training condition is presented. The dynamic equation of the human-exoskeleton is determined using a Lagrangian approach, and its transfer function is established in a closed-loop control system. PID controller gains, initialized by the Ziegler–Nichols (Z-N) method, are used as the input to an adaptive particle swarm optimization (APSO) algorithm for minimizing the multi-joint trajectory error. The optimized controller is tested in the Gazebo virtual environment and compared with the Z-N and conventional optimization methods. The numerical analysis shows that the PID controller tuned by a combination of Z-N and APSO improves the performance of a lower-limb human exoskeleton in gait training.

Suggested Citation

  • Mohammad Soleimani Amiri & Rizauddin Ramli & Mohd Faisal Ibrahim & Dzuraidah Abd Wahab & Norazam Aliman, 2020. "Adaptive Particle Swarm Optimization of PID Gain Tuning for Lower-Limb Human Exoskeleton in Virtual Environment," Mathematics, MDPI, vol. 8(11), pages 1-16, November.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:11:p:2040-:d:445696
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    References listed on IDEAS

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    2. Abdmouleh, Zeineb & Gastli, Adel & Ben-Brahim, Lazhar & Haouari, Mohamed & Al-Emadi, Nasser Ahmed, 2017. "Review of optimization techniques applied for the integration of distributed generation from renewable energy sources," Renewable Energy, Elsevier, vol. 113(C), pages 266-280.
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    Cited by:

    1. Qi Liu & Hong Lu & Heisei Yonezawa & Ansei Yonezawa & Itsuro Kajiwara & Ben Wang, 2023. "Grey-Wolf-Optimization-Algorithm-Based Tuned P-PI Cascade Controller for Dual-Ball-Screw Feed Drive Systems," Mathematics, MDPI, vol. 11(10), pages 1-29, May.
    2. Yingying Liao & Weiguo Zhao & Liying Wang, 2021. "Improved Manta Ray Foraging Optimization for Parameters Identification of Magnetorheological Dampers," Mathematics, MDPI, vol. 9(18), pages 1-38, September.
    3. Mohammad Soleimani Amiri & Rizauddin Ramli, 2022. "Utilisation of Initialised Observation Scheme for Multi-Joint Robotic Arm in Lyapunov-Based Adaptive Control Strategy," Mathematics, MDPI, vol. 10(17), pages 1-14, August.

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