Design of neural network-PID controller for trajectory tracking of differential drive mobile robot

Trinh Thi Khanh Ly; Nguyen Hong Thai; Luu Thanh Phong

doi:10.15625/2525-2518/18066

Author affiliations

Authors

Trinh Thi Khanh Ly Faculty of Automation Technology, Electric Power University (EPU), 235 Hoang Quoc Viet, Bac Tu Liem, Ha Noi, Viet Nam
Nguyen Hong Thai Department of Mechatronics, Hanoi University of Science and Technology (HUST), No. 1 Dai Co Viet, Hai Ba Trung, Ha Noi, Viet Nam
Luu Thanh Phong Department of Mechatronics, Hanoi University of Science and Technology (HUST), No. 1 Dai Co Viet, Hai Ba Trung, Ha Noi, Viet Nam https://orcid.org/0009-0000-0672-1130

DOI:

https://doi.org/10.15625/2525-2518/18066

Keywords:

differential drive mobile robot, trajectory tracking control, neural network, train the neural network, NURBS trajectory

Abstract

This paper proposes the design of a neural network controller based on a sample controller for controlling the trajectory-tracking motion of a differential drive mobile robot (DDMR). Firstly, the trajectory tracking model for DDMR is established based on position error. Next, a perceptron neural network is designed with three hidden layers to control the trajectory tracking of DDMR. The backpropagation algorithm is used to train the neural network with training data obtained from the PID controller with time-varying parameters. The authors have developed this approach and experimentally verified it with minor tracking errors. The neural network's weight matrix (W) and bias vector (b) are updated in real-time, providing an advantage over other methods. The effectiveness of the proposed controller is demonstrated by the DDMR's NURBS trajectory tracking error, which does not exceed 2.17 cm, and the DDMR's motion error, with linear and angular velocities not exceeding 0.004 m/s and 0.0007 rad/s, respectively. The proposed controller can supplement traditional controllers in controlling the trajectory of autonomous mobile robots, thereby improving the ability to generate local trajectories to avoid dynamic obstacles by the neural network

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