Contraction-Based Model Predictive Control using Bilinear Koopman Realizations with Proportional Error Bounds

Data-driven model predictive control based on Koopman operator theory is a promising approach for constrained control of nonlinear systems with unknown dynamics. This paper proposes a robust model predictive control framework for such systems using bilinear Koopman realizations with state- and input