We are focusing our research on developing a general robust adaptive MPC scheme for trajectory following of autonomous vehicles. The general control problem formulation allows us to operate several experimental vehicle types, e.g. passenger vehicles, trucks, or race cars, and further projects with different use cases that require an autonomous trajectory following.

The MPC problem formulation for the combined control of lateral- and longitudinal vehicle dynamics is high-dimensional and complex. We aim to automate the MPC Design process and reduce the manual effort needed by a control expert/engineer. Besides saving enormous time of manual design, automating the process gives us the potential for improving the performance of the closed-loop system w.r.t. our control objectives, e.g. tracking or comfort.

Furthermore, our main research goal is MPC self-adaptivity. When changes to the environment occur, where the MPC is designed, we want to eliminate the manual interventions of the MPC expert to update its configuration. Instead, the MPC should adapt its structure and parametrization automatically according to the new conditions. Additionally, the MPC should be robust against uncertain parameters and external disturbances.

With online continuous learning, we strive for the self-knowledge expansion of the MPC to automatically improve its performance with useful available collected data.