Trajectory Tracking of Autonomous Vehicle Based on Model Predictive Control With PID Feedback
Duanfeng Chu, Haoran Li, Chenyang Zhao, Tuqiang Zhou
Abstract
The simplified vehicle model often results in inaccuracy with respect to the conventional model predictive control (MPC) as it causes steady error in tracking control, which has negative implications for vehicle cornering. This study presents a trajectory planning and tracking framework, which applies artificial potential to obtain target trajectory and MPC with PID feedback to effectively track planned trajectory. The experimental and simulation results are then presented to demonstrate the improved performance in tracking accuracy and steering smoothness compared to that of the conventional MPC control. Especially during negotiating a curve, its steady state error is close to 0.
Topics & Concepts
TrajectoryControl theory (sociology)Model predictive controlPID controllerTracking (education)Tracking errorSmoothnessVehicle dynamicsControl engineeringComputer scienceEngineeringControl (management)Artificial intelligenceMathematicsTemperature controlAutomotive engineeringAstronomyMathematical analysisPhysicsPedagogyPsychologyVehicle Dynamics and Control SystemsRobotic Path Planning AlgorithmsAutonomous Vehicle Technology and Safety