Data-Driven Control of Adaptive Cruise Control Systems Using Differential Flatness and Gaussian Processes

This paper develops a novel control methodology for the adaptive cruise control system under external, unknown dynamics to attain set point regulation. The approach employs differential flatness for controller design, along with Gaussian Processes to estimate the unknown components in the dynamics. In order to ensure robustness to this data-driven technique, a robust control law is also introduced, that further exploits the Gaussian Process framework. The error dynamics is shown to converge to zero with high probability. Numerical experiments to support the results are presented.