Jul 2026· International Conference on Control, Decision and Information Technologies· pp. 2722-2777· 0 citations· 29 references
Abstract
This paper presents a distributed Koopman-based nonlinear model predictive control (NMPC) for virtually coupled train system. Considering nonlinear train dynamics and operational constraints on both states and control inputs, a nonlinear continuous-time tracking control problem is formulated for each train. An analytical observable generation procedure is employed to lift the nonlinear dynamics into the Koopman space, yielding a bilinear lifted system representation. Through bilinearity relaxation and discretization of the lifted dynamics, a linear discrete-time system is obtained, enabling nonlinear programs to be closely approximated by quadratic programs within the MPC framework. Based on a train-to-train communication topology, a distributed implementation is adopted in which each train solves a local optimization problem using updated information received from its preceding train. Simulation results demonstrate that the proposed approach achieves tracking performance comparable to that of a distributed baseline NMPC while significantly reducing computation time, thereby supporting its applicability to real-time virtually coupled train systems.
This paper presents a data-efficient Koopman-based tracking model predictive control (MPC) scheme for nonlinear systems. Linear surrogate models are constructed online via a kernel extended dynamic mode decomposition (EDMD) framework in a reproducing kernel Hilbert space (RKHS), for which a proportional approximation e...
Hai-Ning Yan, Huai-Cheng Yan, Kai-Tian Chen et al.· IEEE Transactions on Automat...· 0 citations
The modeling and control of soft pneumatic manipulators present significant challenges due to their inherent compliance and history-dependent hysteresis. While the Koopman operator theory offers a promising solution by embedding these nonlinear dynamics into a linear framework, conventional Koopman approaches are limit...
Yuxuan Chen, Yun-Peng Zhu, Jian-Da Han et al.· IEEE Robotics and Automation...· 0 citations
Nonlinear model predictive control (NMPC) is widely used in robotics due to its ability to handle nonlinear dynamics and explicitly enforce state and input constraints by solving an optimal control problem online. However, standard NMPC implementations typically rely on direct time discretization and evaluate costs and...
Fumiya Matsuzaki, Akinori Sakaguchi, Kaoru Yamamoto· Conference on Control Techno...· 0 citations
State estimation for nonlinear dynamical systems remains a fundamental challenge, particularly when measurements are sparse and internal states are inaccessible. This work presents a KOOPMAN-based Linear State Observer (KOOPMAN-LSO) design framework that enables linear observer synthesis for nonlinear systems through K...
Mustapha Habib, Dario Aguiar, Esther Kieseritzky et al.· 0 citations
This paper proposes a physics-based adaptive Koopman Model Predictive Control (MPC) strategy for combined spacecraft attitude stabilization under inertia uncertainties and active target maneuverability. A novel, quaternion-based Koopman model is constructed from a set of analytical lifting functions derived from the qu...
Yi-Cheng Sun, Yueyong Lyu, Yu-Han Liu et al.· IEEE Transactions on Automat...· 0 citations
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