Image de couverture de livre

Data-driven parallel Koopman subsystem modeling and distributed moving horizon state estimation for large-scale nonlinear processes

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Li, Xiaojie, Bo, Song, Zhang, Xuewen, Qin, Yan, Yin, Xunyuan
Format:	Preprint
Publié:	2024
Sujets:	Systems and Control
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Iterative distributed moving horizon estimation of linear systems with penalties on both system disturbances and noise
par: Li, Xiaojie, et autres
Publié: (2024)

Approximating arrival costs in distributed moving horizon estimation: A recursive method
par: Li, Xiaojie, et autres
Publié: (2024)

Learning-based data-enabled moving horizon estimation with application to membrane-based biological wastewater treatment process
par: Li, Xiaojie, et autres
Publié: (2026)

Reduced-order Koopman modeling and predictive control of nonlinear processes
par: Zhang, Xuewen, et autres
Publié: (2024)

Self-tuning moving horizon estimation of nonlinear systems via physics-informed machine learning Koopman modeling
par: Yan, Mingxue, et autres
Publié: (2024)

Partition-based distributed extended Kalman filter for large-scale nonlinear processes with application to chemical and wastewater treatment processes
par: Li, Xiaojie, et autres
Publié: (2024)

Machine learning-based input-augmented Koopman modeling and predictive control of nonlinear processes
par: Li, Zhaoyang, et autres
Publié: (2024)

Learning-based data-enabled economic predictive control with convex optimization for nonlinear systems
par: Yan, Mingxue, et autres
Publié: (2025)

Event-triggered moving horizon estimation for nonlinear systems
par: Krauss, Isabelle, et autres
Publié: (2024)

Deep DeePC: Data-enabled predictive control with low or no online optimization using deep learning
par: Zhang, Xuewen, et autres
Publié: (2024)

Robust stability of event-triggered nonlinear moving horizon estimation
par: Krauss, Isabelle, et autres
Publié: (2025)

Strong nonlinear detectability and moving horizon estimation for nonlinear systems with unknown inputs
par: Guo, Yang, et autres
Publié: (2025)

Deep Neural Koopman Operator-based Economic Model Predictive Control of Shipboard Carbon Capture System
par: Han, Minghao, et autres
Publié: (2025)

Nonlinear moving horizon estimation for robust state and parameter estimation -- extended version
par: Schiller, Julian D., et autres
Publié: (2023)

Economic data-enabled predictive control using machine learning
par: Yan, Mingxue, et autres
Publié: (2025)

Moving horizon partition-based state estimation of large-scale systems -- Revised version
par: Farina, Marcello, et autres
Publié: (2024)

Machine learning-based hybrid dynamic modeling and economic predictive control of carbon capture process for ship decarbonization
par: Zhang, Xuewen, et autres
Publié: (2025)

Sample-based detectability and moving horizon state estimation of continuous-time systems
par: Krauss, Isabelle, et autres
Publié: (2026)

Self‐tuning moving horizon estimation of nonlinear systems via physics‐informed machine learning Koopman modeling
par: Mingxue Yan, et autres
Publié: (2024)

Efficient Economic Model Predictive Control of Water Treatment Process with Learning-based Koopman Operator
par: Han, Minghao, et autres
Publié: (2024)

Lyapunov-based reinforcement learning for distributed control with stability guarantee
par: Yao, Jingshi, et autres
Publié: (2024)

Robust stability of moving horizon estimation for continuous-time systems
par: Schiller, Julian D., et autres
Publié: (2023)

A moving horizon estimator for aquifer thermal energy storages
par: van Randenborgh, Johannes, et autres
Publié: (2025)

Economic zone data-enabled predictive control for connected open water systems
par: Chen, Xiaoqiao, et autres
Publié: (2025)

Distributed Observer and Controller Design for Linear Systems: A Separation-Based Approach
par: Cao, Ganghui, et autres
Publié: (2025)

Distributed Omniscient Observers for Multi-Agent Systems: Design and Applications
par: Cao, Ganghui, et autres
Publié: (2026)

Robust stability of moving horizon estimation for nonlinear systems with bounded disturbances using adaptive arrival cost
par: Deniz, Nestor N., et autres
Publié: (2019)

Performance triggered adaptive model reduction for soil moisture estimation in precision irrigation
par: Debnath, Sarupa, et autres
Publié: (2024)

MAKO: Meta-Adaptive Koopman Operators for Learning-based Model Predictive Control of Parametrically Uncertain Nonlinear Systems
par: Han, Minghao, et autres
Publié: (2025)

On boundedness of solutions of three-state Moore-Greitzer compressor model with nonlinear proportional-integral controller for the surge subsystem
par: Shiriaev, Anton S., et autres
Publié: (2026)

Data-driven optimal control of unknown nonlinear dynamical systems using the Koopman operator
par: Zeng, Zhexuan, et autres
Publié: (2024)

Cooperative nonlinear distributed model predictive control with dissimilar control horizons
par: Chanfreut, Paula, et autres
Publié: (2024)

Kernel-based error bounds of bilinear Koopman surrogate models for nonlinear data-driven control
par: Strässer, Robin, et autres
Publié: (2025)

Data-driven functional state estimation of complex networks
par: Zhang, Yuan, et autres
Publié: (2025)

Smart energy management: process structure-based hybrid neural networks for optimal scheduling and economic predictive control in integrated systems
par: Wu, Long, et autres
Publié: (2024)

Data-driven MPC with terminal conditions in the Koopman framework
par: Worthmann, Karl, et autres
Publié: (2024)

Moving horizon estimation for nonlinear systems with time-varying parameters
par: Schiller, Julian D., et autres
Publié: (2024)

Online Reduced-Order Data-Enabled Predictive Control
par: Vahidi-Moghaddam, Amin, et autres
Publié: (2024)

Data-driven Koopman MPC using Mixed Stochastic-Deterministic Tubes
par: Zhong, Zhengang, et autres
Publié: (2025)

Koopman operator based identification of nonlinear networks
par: Anantharaman, Ramachandran, et autres
Publié: (2024)