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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. | Vietnam Journal of Science and Technology 62 2 2024 374-386 doi 10.15625 2525-2518 18066 Design of neural network-PID controller for trajectory tracking of differential drive mobile robot Trinh Thi Khanh Ly1 Nguyen Hong Thai2 Luu Thanh Phong2 1 Faculty of Automation Technology Electric Power University EPU 235 Hoang Quoc Viet Bac Tu Liem Ha Noi Viet Nam 2 Department of Mechatronics Hanoi University of Science and Technology HUST No. 1 Dai Co Viet Hai Ba Trung Ha Noi Viet Nam Emails thai.nguyenhong@hust.edu.vn Received 7 February 2023 Accepted for publication 24 February 2024 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. Keywords differential drive mobile robot trajectory tracking control neural network train the neural network NURBS trajectory. Classification numbers 5.2.1 5.3.3 .
