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...

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Bibliographic Details
Published in:Aerospace Vol. 8; no. 12; p. 377
Main Authors: Dalmau, Ramon, Prats, Xavier, Baxley, Brian
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.12.2021
Subjects:
Aircraft
Aircraft control
Airports
Aviation
Constraint modelling
continuous descent operations
Descent
Environmental impact
Expected values
Guidance (motion)
Linear programming
model predictive control
Nonlinear control
Nonlinear programming
Optimization
Ordinary differential equations
Parameter sensitivity
Performance degradation
Perturbation
Planning
Predictive control
Quadratic programming
Trajectory optimization
Trajectory planning
Variables
ISSN:2226-4310, 2226-4310
Online Access:Get full text
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Summary: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.
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ISSN:2226-4310
2226-4310
DOI:10.3390/aerospace8120377