Learning exactly linearizable deep dynamics models
Research on control using models based on machine-learning methods has now shifted to the practical engineering stage. Achieving high performance and theoretically guaranteeing the safety of the system is critical for such applications. In this paper, we propose a learning method for exactly lineari...
Saved in:
Main Authors: | Ryuta Moriyasu, Masayuki Kusunoki, Kenji Kashima |
---|---|
Format: | Article |
Language: | English |
Published: |
Taylor & Francis Group
2025-12-01
|
Series: | SICE Journal of Control, Measurement, and System Integration |
Subjects: | |
Online Access: | http://dx.doi.org/10.1080/18824889.2025.2459429 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Learning to Boost the Performance of Stable Nonlinear Systems
by: Luca Furieri, et al.
Published: (2024-01-01) -
A plunger lifting optimization control method based on APSO-MPC for edge computing applications
by: Zhi Qiu, et al.
Published: (2025-02-01) -
Sustainable PV-hydrogen-storage microgrid energy management using a hierarchical economic model predictive control framework
by: Xinyu Guo, et al.
Published: (2025-02-01) -
Flatness-based control revisited: The HEOL setting
by: Join, Cédric, et al.
Published: (2024-11-01) -
Glowinski and numerical control problems
by: Fernández-Cara, Enrique
Published: (2023-04-01)