Koopman operator theory has proven to be a promising approach to nonlinear system
identification and global linearization. For nearly a century, there had been no efficient …

This paper proposes the Koopman operator-based model identification and control method
for a lane-keeping system. The Koopman operator is a linear mapping that can capture …

Koopman operator theory offers a rigorous treatment of dynamics and has been emerging
as a powerful modeling and learning-based control method enabling significant …

This paper provides a comprehensive tutorial on a family of Model Predictive Control (MPC)
formulations, known as MPC for tracking, which are characterized by including an artificial …

This paper proposes Koopman operator-based Stochastic Model Predictive Control (K-
SMPC) for enhanced lateral control of autonomous vehicles. The Koopman operator is a …

This dissertation presents the development and applications of Koopman operator theory for
solving system identification and prediction problems in the domain of vehicular systems …

The complex mathematical model of autonomous vehicles makes it difficult for system
identification due to a combination of non-linearity and uncertainty. Various strategies have …

This letter presents a new robust data-driven predictive control scheme for linear time-
varying (LTV) systems with unknown nominal system models. To tackle the challenges …

Nonlinear dynamics bring difficulties to controller design for control-affine systems such as
tractor-trailer vehicles, especially when the parameters in the dynamics are unknown. To …

Poincaré's geometric representation has long been fundamental in dynamical system
analysis. However, its limitations in handling high-dimensional and uncertain systems have …