Self-tuning moving horizon estimation of nonlinear systems via physics-informed machine learning Koopman modeling

Documenti analoghi

• Self‐tuning moving horizon estimation of nonlinear systems via physics‐informed machine learning Koopman modeling
  di: Mingxue Yan, et al.
  Pubblicazione: (2024)
• Efficient Economic Model Predictive Control of Water Treatment Process with Learning-based Koopman Operator
  di: Han, Minghao, et al.
  Pubblicazione: (2024)
• MAKO: Meta-Adaptive Koopman Operators for Learning-based Model Predictive Control of Parametrically Uncertain Nonlinear Systems
  di: Han, Minghao, et al.
  Pubblicazione: (2025)
• Partition-based distributed extended Kalman filter for large-scale nonlinear processes with application to chemical and wastewater treatment processes
  di: Li, Xiaojie, et al.
  Pubblicazione: (2024)
• Data-driven parallel Koopman subsystem modeling and distributed moving horizon state estimation for large-scale nonlinear processes
  di: Li, Xiaojie, et al.
  Pubblicazione: (2024)