2 papers
math.OC2026
Contraction-Based Model Predictive Control using Bilinear Koopman Realizations with Proportional Error Bounds
Yuki Higuchi, Kazuhiro Sato
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 propose…
math.OC2026
Bilinear Koopman-Based Robust Model Predictive Control for Unknown Nonlinear Systems via Contraction Metrics
Yuki Higuchi, Kazuhiro Sato
Data-driven model predictive control (MPC) using Koopman operator theory is a promising approach for constrained control of unknown nonlinear systems. While linear Koopman realizat…