Robust Longitudinal Control of Self-Driving Racecar Models

This paper focuses on the control of longitudinal self-driving racecar models with model uncertainty and pro- poses a robust solution that comprises an online disturbance estimator and a nonlinear compensation control feedback law. By modeling all uncertainty with respect to a nominal model as suitably disturbance signals and afterward exploiting unknown- input state observer theory, a lean and fast estimator is derived for the racecar model. The estimator does not require a priori knowledge of the uncertainty. Closed-loop stability of the proposed controller ensuring the asymptotic reconstruction of the system state and disturbance inputs as well as asymptotic tracking of desired longitudinal speed is proved. Simulations are presented to exemplify the functioning of the proposed solution and show its validity.