Experimental Validation of Finite-Time Robust Control for Autonomous Vehicles using QCar

The performance of autonomous vehicles in tasks where lateral maneuvers are required can be significantly compromised by uncertainties, parameter variations, and external disturbances. To address these challenges, this work proposes an adaptive super-Twisting controller aimed at improving path tracking performance under uncertain conditions. The proposed technique effectively reduces the chattering effect, ensures high tracking accuracy, and offers robustness against disturbances, while guaranteeing finite-Time convergence. To validate the effectiveness of the developed technique, initial simulations are conducted using a digital twin model, followed by experimental validation on a 1/10 scale QCar in a real-world driving scenario.