Model Predictive Control and Moving Horizon Estimation using Statistically Weighted Data-Based Ensemble Models
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866917462727983104 |
|---|---|
| author | de Giuli, Laura Boca Mallick, Samuel La Bella, Alessio Dabiri, Azita De Schutter, Bart Scattolini, Riccardo |
| author_facet | de Giuli, Laura Boca Mallick, Samuel La Bella, Alessio Dabiri, Azita De Schutter, Bart Scattolini, Riccardo |
| contents | This paper presents a model predictive control (MPC) framework leveraging an ensemble of data-based models to optimally control complex systems under multiple operating conditions. A novel combination rule for ensemble models is proposed, based on the statistical Mahalanobis distance, enabling the ensemble weights to suitably vary across the prediction window based on the system input. In addition, a novel state observer for ensemble models is developed using moving horizon estimation (MHE). The effectiveness of the proposed methodology is demonstrated on a benchmark energy system operating under multiple conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_21343 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Model Predictive Control and Moving Horizon Estimation using Statistically Weighted Data-Based Ensemble Models de Giuli, Laura Boca Mallick, Samuel La Bella, Alessio Dabiri, Azita De Schutter, Bart Scattolini, Riccardo Systems and Control This paper presents a model predictive control (MPC) framework leveraging an ensemble of data-based models to optimally control complex systems under multiple operating conditions. A novel combination rule for ensemble models is proposed, based on the statistical Mahalanobis distance, enabling the ensemble weights to suitably vary across the prediction window based on the system input. In addition, a novel state observer for ensemble models is developed using moving horizon estimation (MHE). The effectiveness of the proposed methodology is demonstrated on a benchmark energy system operating under multiple conditions. |
| title | Model Predictive Control and Moving Horizon Estimation using Statistically Weighted Data-Based Ensemble Models |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2511.21343 |