Modeling and Control of Deep Sign-Definite Dynamics with Application to Hybrid Powertrain Control
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866916967210811392 |
|---|---|
| author | Kato, Teruki Shima, Ryotaro Kashima, Kenji |
| author_facet | Kato, Teruki Shima, Ryotaro Kashima, Kenji |
| contents | Deep learning is increasingly used for complex, large-scale systems where first-principles modeling is difficult. However, standard deep learning models often fail to enforce physical structure or preserve convexity in downstream control, leading to physically inconsistent predictions and discontinuous inputs owing to nonconvexity. We introduce sign constraints--sign restrictions on Jacobian entries--that unify monotonicity, positivity, and sign-definiteness; additionally, we develop model-construction methods that enforce them, together with a control-synthesis procedure. In particular, we design exactly linearizable deep models satisfying these constraints and formulate model predictive control as a convex quadratic program, which yields a unique optimizer and a Lipschitz continuous control law. On a two-tank system and a hybrid powertrain, the proposed approach improves prediction accuracy and produces smoother control inputs than existing methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_19869 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Modeling and Control of Deep Sign-Definite Dynamics with Application to Hybrid Powertrain Control Kato, Teruki Shima, Ryotaro Kashima, Kenji Systems and Control Machine Learning Optimization and Control Deep learning is increasingly used for complex, large-scale systems where first-principles modeling is difficult. However, standard deep learning models often fail to enforce physical structure or preserve convexity in downstream control, leading to physically inconsistent predictions and discontinuous inputs owing to nonconvexity. We introduce sign constraints--sign restrictions on Jacobian entries--that unify monotonicity, positivity, and sign-definiteness; additionally, we develop model-construction methods that enforce them, together with a control-synthesis procedure. In particular, we design exactly linearizable deep models satisfying these constraints and formulate model predictive control as a convex quadratic program, which yields a unique optimizer and a Lipschitz continuous control law. On a two-tank system and a hybrid powertrain, the proposed approach improves prediction accuracy and produces smoother control inputs than existing methods. |
| title | Modeling and Control of Deep Sign-Definite Dynamics with Application to Hybrid Powertrain Control |
| topic | Systems and Control Machine Learning Optimization and Control |
| url | https://arxiv.org/abs/2509.19869 |