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Hamilton–Jacobi Inequality Adaptive Robust Learning Tracking Controller of Wearable Robotic Knee System

Author

Listed:
  • Houssem Jerbi

    (Department of Industrial Engineering, College of Engineering, University of Ha’il, Ha’il 81451, Saudi Arabia)

  • Izzat Al-Darraji

    (Automated Manufacturing Department, Al-Khwarizmi College of Engineering, University of Baghdad, Baghdad 10081, Iraq)

  • Georgios Tsaramirsis

    (Abu Dhabi Women’s Campus, Higher Colleges of Technology, Abu Dhabi 25026, United Arab Emirates)

  • Lotfi Ladhar

    (Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdul Aziz University, Jeddah 21589, Saudi Arabia)

  • Mohamed Omri

    (Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah 21589, Saudi Arabia)

Abstract

A Wearable Robotic Knee (WRK) is a mobile device designed to assist disabled individuals in moving freely in undefined environments without external support. An advanced controller is required to track the output trajectory of a WRK device in order to resolve uncertainties that are caused by modeling errors and external disturbances. During the performance of a task, disturbances are caused by changes in the external load and dynamic work conditions, such as by holding weights while performing the task. The aim of this study is to address these issues and enhance the performance of the output trajectory tracking goal using an adaptive robust controller based on the Radial Basis Function (RBF) Neural Network (NN) system and Hamilton–Jacobi Inequality (HJI) approach. WRK dynamics are established using the Lagrange approach at the outset of the analysis. Afterwards, the L 2 gain technique is applied to enhance the control motion solutions and provide the main features of the designed WRK control systems. To prove the stability of the controlled system, the HJI approach is investigated next using optimization techniques. The synthesized RBF NN algorithm supports the easy implementation of the adaptive controller, as well as ensuring the stability of the WRK system. An analysis of the numerical simulation results is performed in order to demonstrate the robustness and effectiveness of the proposed tracking control algorithm. The results showed the ability of the suggested controller of this study to find a solution to uncertainties.

Suggested Citation

  • Houssem Jerbi & Izzat Al-Darraji & Georgios Tsaramirsis & Lotfi Ladhar & Mohamed Omri, 2023. "Hamilton–Jacobi Inequality Adaptive Robust Learning Tracking Controller of Wearable Robotic Knee System," Mathematics, MDPI, vol. 11(6), pages 1-32, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:6:p:1351-:d:1093477
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    References listed on IDEAS

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    1. Hanen Chaouch & Samia Charfeddine & Sondess Ben Aoun & Houssem Jerbi & Víctor Leiva, 2022. "Multiscale Monitoring Using Machine Learning Methods: New Methodology and an Industrial Application to a Photovoltaic System," Mathematics, MDPI, vol. 10(6), pages 1-16, March.
    2. Jun Yang & Jing Na & Guanbin Gao & Chao Zhang, 2018. "Adaptive Neural Tracking Control of Robotic Manipulators with Guaranteed NN Weight Convergence," Complexity, Hindawi, vol. 2018, pages 1-11, October.
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    Cited by:

    1. Rabeh Abbassi & Houssem Jerbi & Mourad Kchaou & Theodore E. Simos & Spyridon D. Mourtas & Vasilios N. Katsikis, 2023. "Towards Higher-Order Zeroing Neural Networks for Calculating Quaternion Matrix Inverse with Application to Robotic Motion Tracking," Mathematics, MDPI, vol. 11(12), pages 1-21, June.

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