Quantum optimization for Nonlinear Model Predictive Control
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866917828583489536 |
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| author | Novara, Carlo Boggio, Mattia Volpe, Deborah |
| author_facet | Novara, Carlo Boggio, Mattia Volpe, Deborah |
| contents | Nonlinear Model Predictive Control (NMPC) is a general and flexible control approach, used in many industrial contexts, and is based on the online solution of a nonlinear optimization problem. This operation requires in general a high computational cost, which may compromise the NMPC implementation in ``fast'' applications, especially if a large number variables is involved. To overcome this issue, we propose a quantum computing approach for the solution of the NMPC optimization problem. Assuming the availability of an efficient quantum computer, the approach has the potential to considerably decrease the computational time and/or enhance the solution quality compared to classical algorithms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_19467 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Quantum optimization for Nonlinear Model Predictive Control Novara, Carlo Boggio, Mattia Volpe, Deborah Systems and Control Quantum Physics Nonlinear Model Predictive Control (NMPC) is a general and flexible control approach, used in many industrial contexts, and is based on the online solution of a nonlinear optimization problem. This operation requires in general a high computational cost, which may compromise the NMPC implementation in ``fast'' applications, especially if a large number variables is involved. To overcome this issue, we propose a quantum computing approach for the solution of the NMPC optimization problem. Assuming the availability of an efficient quantum computer, the approach has the potential to considerably decrease the computational time and/or enhance the solution quality compared to classical algorithms. |
| title | Quantum optimization for Nonlinear Model Predictive Control |
| topic | Systems and Control Quantum Physics |
| url | https://arxiv.org/abs/2410.19467 |