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Neural Network-Based Self-tuning Kinematic Control and Dynamic Compensation for Mobile Robots

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Proceedings of 10th International Conference on Mechatronics and Control Engineering (ICMCE 2021)

Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

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Abstract

This paper proposes a neural network-based self-tuning kinematic controller and dynamic compensation for tracking trajectories, which applied, e.g., in cases where mobile robots are subject to: continuous parametric changes; different trajectories and external disturbances where online gain tuning is a desirable choice. For this kind of controller, the kinematic and dynamic model was developed considering that the mobile robot is confirmed by differential platform, and the operating point is not located at the center of wheel’s axes. The artificial neural network estimates the states of the mobile robot while the gradient descent optimization algorithm adjusts the controller gains that attain the smaller position tracking error, the dynamic model is used to compensate the velocity errors in the robot. Moreover, the stability of the proposed controller is demonstrated analytically. Finally, simulation results are given considering a Turtlebot3 mobile robot, real-time experiments are implemented in the same mobile robot, where the tests are carried out to show the effectiveness of the controller in a real environment.

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Acknowledgements

The authors would like to thank Proyecto de Investigacion: Análisis, diseño e implementación de algoritmos de control inteligente en controladores con una red de sensores IoT en vehículos para mejorar la seguridad vial; Escuela Superior Politécnica de Chimborazo ESPOCH and the Research Group GITEA, for the support for the development of this work.

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Authors and Affiliations

  1. Universidad De Las Fuerzas Armadas ESPE, Sangolqui, Ecuador

    Luis F. Recalde, Bryan S. Guevara & Víctor H. Andaluz

  2. Escuela Superior Politécnica de Chimborazo ESPOCH, Riobamba, Ecuador

    Danny J. Zea

Authors
  1. Luis F. Recalde
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  2. Bryan S. Guevara
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  3. Danny J. Zea
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  4. Víctor H. Andaluz
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Corresponding author

Correspondence to Luis F. Recalde .

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Editors and Affiliations

  1. Department of Information Engineering, Marche Polytechnic University, Ancona, Italy

    Giuseppe Conte

  2. GIPSA-lab, University of Grenoble Alpes, Grenoble, France

    Olivier Sename

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Recalde, L.F., Guevara, B.S., Zea, D.J., Andaluz, V.H. (2023). Neural Network-Based Self-tuning Kinematic Control and Dynamic Compensation for Mobile Robots. In: Conte, G., Sename, O. (eds) Proceedings of 10th International Conference on Mechatronics and Control Engineering . ICMCE 2021. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-19-1540-6_11

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  • DOI: https://doi.org/10.1007/978-981-19-1540-6_11

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-1539-0

  • Online ISBN: 978-981-19-1540-6

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