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Data-Driven Adaptive Modelling and Control for a Class of Discrete-Time Robotic Systems Based on a Generalized Jacobian Matrix Initialization

Author

Listed:
  • América Berenice Morales-Díaz

    (Department of Robotics and Advanced Manufacturing, CINVESTAV-Saltillo, Ramos Arizpe 25900, Mexico)

  • Josué Gómez-Casas

    (Faculty of Engineering, Autonomous University of Coahuila, Arteaga 25350, Mexico)

  • Chidentree Treesatayapun

    (Department of Robotics and Advanced Manufacturing, CINVESTAV-Saltillo, Ramos Arizpe 25900, Mexico)

  • Carlos Rodrigo Muñiz-Valdez

    (Faculty of Engineering, Autonomous University of Coahuila, Arteaga 25350, Mexico)

  • Jesús Salvador Galindo-Valdés

    (Faculty of Engineering, Autonomous University of Coahuila, Arteaga 25350, Mexico)

  • Jesús Fernando Martínez-Villafañe

    (Faculty of Engineering, Autonomous University of Coahuila, Arteaga 25350, Mexico)

Abstract

Data technology advances have increased in recent years, especially for robotic systems, in order to apply data-driven modelling and control computations by only considering the input and output signals’ relationship. For a data-driven modelling and control approach, the system is considered unknown. Thus, the initialization values of the system play an important role to obtain a suitable estimation. This paper presents a methodology to initialize a data-driven model using the pseudo-Jacobian matrix algorithm to estimate the model of a mobile manipulator robot. Once the model is obtained, a control law is proposed for the robot end-effector position tasks. To this end, a novel neuro-fuzzy network is proposed as a control law, which only needs to update one parameter to minimize the control error and avoids the chattering phenomenon. In addition, a general stability analysis guarantees the convergence of the estimation and control errors and the tuning of the closed-loop control design parameters. The simulations results validate the performance of the data-driven model and control.

Suggested Citation

  • América Berenice Morales-Díaz & Josué Gómez-Casas & Chidentree Treesatayapun & Carlos Rodrigo Muñiz-Valdez & Jesús Salvador Galindo-Valdés & Jesús Fernando Martínez-Villafañe, 2023. "Data-Driven Adaptive Modelling and Control for a Class of Discrete-Time Robotic Systems Based on a Generalized Jacobian Matrix Initialization," Mathematics, MDPI, vol. 11(11), pages 1-19, June.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:11:p:2555-:d:1162744
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    References listed on IDEAS

    as
    1. Isiah Zaplana & Hugo Hadfield & Joan Lasenby, 2022. "Singularities of Serial Robots: Identification and Distance Computation Using Geometric Algebra," Mathematics, MDPI, vol. 10(12), pages 1-27, June.
    2. Peng Ji & Chenglong Li & Fengying Ma, 2022. "Sliding Mode Control of Manipulator Based on Improved Reaching Law and Sliding Surface," Mathematics, MDPI, vol. 10(11), pages 1-21, June.
    Full references (including those not matched with items on IDEAS)

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