Blending global linearization with local learning: A Koopman neural framework for interpretable modeling and control of USV
- 15 January 2026
- journal article
- Published by Elsevier in Ocean Engineering
- Vol. 350
Abstract
No abstract availableFunding Information
- National Key Research and Development Program of China (2021YFC***1101)
- Fundamental Research Funds for the Central Universities (24CX02030A)
This publication has 32 references indexed in Scilit:
- Time series prediction of ship maneuvering motion at sea based on higher order dynamic mode decompositionOcean Engineering, 2025
- Enhanced Koopman operator-based robust data-driven control for 3 degree of freedom autonomous underwater vehicles: A novel approachOcean Engineering, 2024
- Adaptive neural synergetic heading control for USVs with unknown dynamics and disturbancesOcean Engineering, 2024
- Deep Koopman learning of nonlinear time-varying systemsAutomatica, 2023
- Towards global neural network abstractions with locally-exact reconstructionNeural Networks, 2023
- Koopman operator-based driver-vehicle dynamic model for shared control systemsApplied Mathematical Modelling, 2022
- Robust Control Theory Based Stability Certificates for Neural Network Approximated Nonlinear Model Predictive ControlIFAC-PapersOnLine, 2021
- Deep reinforcement learning-based controller for path following of an unmanned surface vehicleOcean Engineering, 2019
- Adaptive integral LOS path following for an unmanned airship with uncertainties based on robust RBFNN backsteppingISA Transactions, 2016
- Path following control for marine surface vessel with uncertainties and input saturationNeurocomputing, 2016