Speed Control Based on an Improved PID Controller with BP Neural Network for Two Wheel Differential AGV System

The motor of Automatic Guided Vehicle (AGV) has characteristics of nonlinearity, time-varying uncertainty, strong interference, traditional PID control algorithms are difficult to achieve ideal control effects. To address this issue, this paper proposes an improved PID control method based on backpropagation (BP) neural network and incorporating inertia factors. The error between the expected speed and actual speed of the AGV left and right wheels is used as input to the BP neural network to adjust the parameters of the PID controller and improve the speed control accuracy of the AGV. The simulation results validate the effectiveness of this method.