A comparative study of stanley, lqr and mpc controllers for path tracking application (adas/ad)

Abstract

This paper presents comparative study between Stanley, LQR (Linear Quadratic Regulator) and MPC (Model Predictive Controller) controllers for path tracking application, which is a level 4 automation feature under ADAS/AD (Advanced Driver Assistance System/Autonomous Driving). The accuracy associated with all the controllers are compared by making the vehicle model run in a prescribed environment. The initial designs are done in MATLAB environment and later they are interfaced with IPG CarMaker vehicle simulation tool for fine tuning. Stanley controller is more of an intuitive steering control law where as LQR and MPC are more advanced optimal controllers. The control actions are calculated by optimising the states of the model. Kinematic vehicle model is used with states as errors and a comparator design is made to find the deviation of the vehicle from the prescribed path. The paper gives a detailed idea about the controllers regarding its use, advantages and limitations in this application. © 2019 IEEE.