Linear Quadratic Gaussian (LQG) Lateral Controller for Vehicle Collision Avoidance Trajectory Tracking With the Presence of Additional Load Disturbance and Sensor Noise

This article presents the path tracking design for a vehicle collision avoidance system as the vehicle travels at <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$50 ~\text{km} / \mathrm{h}$</tex>. Considering the noise from IMU sensors, robust path-tracking strategies are necessary to ensure the vehicle can follow the reference path while avoiding obstacles. External disturbance: additional load distribution is also included while simulating the collision avoidance scenario. The path tracking system is designed to reduce the additional load disturbance as well as eliminate the measurement sensor noise while avoiding obstacles. The path tracking system encompasses the combination of a Kalman state estimator with a state feedback controller and operates within optimal linear control theory. The Kalman estimator is used in this research to reduce the external disturbance and sensor noise by estimating the state corresponding to the truth state value from the reference generator (yaw rate). The state feedback controller will produce a feasible steering wheel angle actuator for the vehicle plant corresponding to the estimated state from the Kalman estimator. The proposed path tracking system (LQG) managed to solve the issue by reducing the mean square error (MSE) between the reference trajectory and the actual trajectory to 0.004 while avoiding obstacles.