A Neural Network Controller Design for the Mecanum Wheel Mobile Robot

Authors

  • Trinh Thi Khanh Ly Faculty of Control and Automation, Electric Power University (EPU), Vietnam
  • Nguyen Thi Thanh Hanoi University of Science and Technology (HUST), Vietnam | University of Economics - Technology for Industries (UNETI), Vietnam
  • Hoang Thien Faculty of Mechatronics, Hanoi University of Science and Technology (HUST), Vietnam
  • Thai Nguyen Hanoi University of Science and Technology
Volume: 13 | Issue: 2 | Pages: 10541-10547 | April 2023 | https://doi.org/10.48084/etasr.5761

Abstract

Advanced controllers are an excellent choice for the trajectory tracking problem of Wheeled Mobile Robots (WMRs). However, these controllers pose a challenge to the hardware structure of WMRs due to the controller's complex structure and the large number of calculations needed. In that context, designing a controller with a simple structure and a small number of computations but good real-time performance is necessary in order to improve the tracking accuracy for the WMRs without requiring high hardware architecture. In this work, a neural network controller with a simple structure for the trajectory-tracking of a Mecanum-Wheel Mobile robot (MWMR) based on a reference controller is proposed. A two-layer feedforward neural network is designed as a tracking controller for the robot. The neural network is trained with a sample input-output data set so that the error between the neural network output and the reference control signal of the supervisory controller is minimal. The neural network parameters are trained to update over time. The simulation results verified the effectiveness of the neural network controller, whose parameters are tuned adaptively to ensure a fast convergence to the desired Bézier trajectory.

Keywords:

tracking control, neural networks, Bézier trajectory, mecanum wheel mobile robot

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How to Cite

[1]
T. T. K. Ly, N. T. Thanh, H. Thien, and T. Nguyen, “A Neural Network Controller Design for the Mecanum Wheel Mobile Robot”, Eng. Technol. Appl. Sci. Res., vol. 13, no. 2, pp. 10541–10547, Apr. 2023.

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