Design Neural Network-Pid Controller for Trajectory Tracking of Differential Drive Mobile Robot

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