An Adaptive PID Controller with Neural Network Self-Tuning for Vehicle Lane Keeping System

Vehicle lane keeping system is becoming a new research focus of drive assistant system except adaptive cruise control system. As we all known, vehicle lateral dynamics show strong nonlinear and time-varying with the variety of longitudinal velocity, especially tire’s mechanics characteristic will change from linear characteristic under low speed to strong nonlinear under high speed. For this reason, the traditional PID controller and even self-tuning PID controller, which need to know a precise vehicle lateral dynamics model to adjust the control parameter, are too difficult to get enough accuracy and the ideal control quality. Based on neural network’s ability of self-learning, adaptive and approximate to any nonlinear function, an adaptive PID control algorithm with BP neural network self-tuning online was proposed for vehicle lane keeping. The results of the simulation running in different lane curvature under different velocities show this algorithm can effectively control vehicle to keep the target trajectory and has good robustness and adaptability for vehicle lateral nonlinear dynamics during the changing of velocity and path curvature.