Sensitivity-Based Non-Linear Model Predictive Control for Aircraft Descent Operations Subject to Time Constraints
The ability to meet a controlled time of arrival while also flying a continuous descent operation will enable environmentally friendly and fuel efficient descent operations while simultaneously maintaining airport throughput. Previous work showed that model predictive control, a guidance strategy ba...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-12-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/8/12/377 |
| _version_ | 1797507098394755072 |
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| author | Ramon Dalmau Xavier Prats Brian Baxley |
| author_facet | Ramon Dalmau Xavier Prats Brian Baxley |
| author_sort | Ramon Dalmau |
| collection | DOAJ |
| description | The ability to meet a controlled time of arrival while also flying a continuous descent operation will enable environmentally friendly and fuel efficient descent operations while simultaneously maintaining airport throughput. Previous work showed that model predictive control, a guidance strategy based on a reiterated update of the optimal trajectory during the descent, provides excellent environmental impact mitigation figures while meeting operational constraints in the presence of modeling errors. Despite that, the computational delay associated with the solution of the trajectory optimization problem could lead to performance degradation and stability issues. This paper proposes two guidance strategies based on the theory of neighboring extremals that alleviate this problem. Parametric sensitivities are obtained by linearization of the necessary conditions of optimality along the active optimal trajectory plan to rapidly update it for small perturbations, effectively converting the complex and time consuming non-linear programming problem into a manageable quadratic programming problem. Promising results, derived from more than 4000 simulations, show that the performance of this method is comparable to that of instantaneously recalculating the optimal trajectory at each time sample. |
| first_indexed | 2024-03-10T04:41:51Z |
| format | Article |
| id | doaj.art-b5a4ee80ee8c423f864671e2168448f5 |
| institution | Directory Open Access Journal |
| issn | 2226-4310 |
| language | English |
| last_indexed | 2024-03-10T04:41:51Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj.art-b5a4ee80ee8c423f864671e2168448f52023-11-23T03:18:06ZengMDPI AGAerospace2226-43102021-12-0181237710.3390/aerospace8120377Sensitivity-Based Non-Linear Model Predictive Control for Aircraft Descent Operations Subject to Time ConstraintsRamon Dalmau0Xavier Prats1Brian Baxley2Department of Physics, Technical University of Catalonia (UPC), 08860 Castelldefels, SpainDepartment of Physics, Technical University of Catalonia (UPC), 08860 Castelldefels, SpainCrew Systems and Aviation Operations Branch, NASA Langley Research Center (LaRC), Hampton, VA 23666, USAThe ability to meet a controlled time of arrival while also flying a continuous descent operation will enable environmentally friendly and fuel efficient descent operations while simultaneously maintaining airport throughput. Previous work showed that model predictive control, a guidance strategy based on a reiterated update of the optimal trajectory during the descent, provides excellent environmental impact mitigation figures while meeting operational constraints in the presence of modeling errors. Despite that, the computational delay associated with the solution of the trajectory optimization problem could lead to performance degradation and stability issues. This paper proposes two guidance strategies based on the theory of neighboring extremals that alleviate this problem. Parametric sensitivities are obtained by linearization of the necessary conditions of optimality along the active optimal trajectory plan to rapidly update it for small perturbations, effectively converting the complex and time consuming non-linear programming problem into a manageable quadratic programming problem. Promising results, derived from more than 4000 simulations, show that the performance of this method is comparable to that of instantaneously recalculating the optimal trajectory at each time sample.https://www.mdpi.com/2226-4310/8/12/377trajectory optimizationmodel predictive controlcontinuous descent operations |
| spellingShingle | Ramon Dalmau Xavier Prats Brian Baxley Sensitivity-Based Non-Linear Model Predictive Control for Aircraft Descent Operations Subject to Time Constraints Aerospace trajectory optimization model predictive control continuous descent operations |
| title | Sensitivity-Based Non-Linear Model Predictive Control for Aircraft Descent Operations Subject to Time Constraints |
| title_full | Sensitivity-Based Non-Linear Model Predictive Control for Aircraft Descent Operations Subject to Time Constraints |
| title_fullStr | Sensitivity-Based Non-Linear Model Predictive Control for Aircraft Descent Operations Subject to Time Constraints |
| title_full_unstemmed | Sensitivity-Based Non-Linear Model Predictive Control for Aircraft Descent Operations Subject to Time Constraints |
| title_short | Sensitivity-Based Non-Linear Model Predictive Control for Aircraft Descent Operations Subject to Time Constraints |
| title_sort | sensitivity based non linear model predictive control for aircraft descent operations subject to time constraints |
| topic | trajectory optimization model predictive control continuous descent operations |
| url | https://www.mdpi.com/2226-4310/8/12/377 |
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