Autonomous Racing using Learning Model Predictive Control
This work addresses the problem of autonomous racing for robotics and control systems, presenting an incremental improvement by integrating learning with model predictive control.
The paper tackles autonomous racing by applying a novel learning Model Predictive Control technique to minimize lap time, using data from previous laps to improve performance while meeting safety requirements, with simulation results in CarSim demonstrating its effectiveness.
A novel learning Model Predictive Control technique is applied to the autonomous racing problem. The goal of the controller is to minimize the time to complete a lap. The proposed control strategy uses the data from previous laps to improve its performance while satisfying safety requirements. Moreover, a system identification technique is proposed to estimate the vehicle dynamics. Simulation results with the high fidelity simulator software CarSim show the effectiveness of the proposed control scheme.