Simulation Performance Evaluation of Pure Pursuit, Stanley, LQR, MPC Controller for Autonomous Vehicles
Jia Liu, Zhiheng Yang, Zhejun Huang, Wenfei Li, Shaobo Dang, Huiyun Li
Abstract
Autonomous vehicles have been gaining increasing attentions, one key research interesting is stable path tracking for an advanced driver assistance system. This paper investigates Pure Pursuit, Stanley, Linear Quadratic Regulator (LQR) and Linear Model Predictive Control (MPC) with Ackerman steering model and these methods are tested on different shape paths in simulation experiments. It is demonstrated that the performances of LQR and MPC controllers are better than those using Pure Pursuit and Stanley controllers. In future work, we will apply them to our autonomous vehicle and we will employ dynamic models to design the controller in high-speed scenarios.
Topics & Concepts
Linear-quadratic regulatorControl theory (sociology)Model predictive controlComputer scienceController (irrigation)Work (physics)Control engineeringVehicle dynamicsKey (lock)Tracking (education)Path (computing)Control (management)EngineeringArtificial intelligenceAutomotive engineeringProgramming languagePedagogyMechanical engineeringBiologyComputer securityAgronomyPsychologyVehicle Dynamics and Control SystemsReal-time simulation and control systemsControl and Dynamics of Mobile Robots