Quasi-steady flight to quasi-steady flight transition for abort landing in a windshear: Trajectory optimization and guidance

Miele, A.; Wang, T.; Melvin, W. W.

doi:10.1007/bf00939681

Cited by 13 publications

(7 citation statements)

References 18 publications

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“…In this section, we consider an approximation u h of u. This approximation could be a numerical solution obtained by solving a discretized scheme of the Hamilton Jacobi equation (20) verified by u.…”

Section: 2mentioning

confidence: 99%

“…Indeed, the best strategy to avoid a failed landing, that can occurs because of quick changes of the wind velocity, is to steer the aircraft to the maximal altitude that can be reached, during an interval of time, in order to prevent a crash on the ground. In [20,21], a Chebyshev-type optimal control problem was proposed and an approximate solution is provided. The Hamilton-Jacobi-Bellman approach was applied in [2] to solve this problem after supposing the knowledge of the wind velocity fields.…”

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confidence: 99%

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A differential game control problem with state constraints

Gammoudi

Zidani

2023

MCRF

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<p style='text-indent:20px;'>We study the Hamilton-Jacobi (HJ) approach for a two-person zero-sum differential game with state constraints and where controls of the two players are coupled within the dynamics, the state constraints and the cost functions. It is known for such problems that the value function may be discontinuous and its characterization by means of an HJ equation requires some controllability assumptions involving the dynamics and the set of state constraints. In this work, we characterize this value function through an auxiliary differential game free of state constraints. Furthermore, we establish a link between the optimal strategies of the constrained problem and those of the auxiliary problem and we present a general approach allowing to construct approximated optimal feedbacks to the constrained differential game for both players. Finally, an aircraft landing problem in the presence of wind disturbances is given as an illustrative numerical example.</p>

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Section: 2mentioning

confidence: 99%

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confidence: 99%

A differential game control problem with state constraints

Gammoudi

Zidani

2023

MCRF

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“…Further work noted the consisting differences between minimum-fuel and minimum-time trajectories in terms of climb and descent angles, cruise altitudes and airspeed [42]. Miele adopted vertical trajectory optimisation methods to study the guidance problem of flight recovery in the case of an aborted landing associated to windshear, both as horizontal shear and including a vertical downdraft component [50,51]. The work highlighted the opportunity of developing advanced windshear control systems entailing take-off, aborted and penetration landings.…”

Section: Early Trajectory Optimisation Research In Aviationmentioning

confidence: 99%

Multi-objective optimisation of aircraft flight trajectories in the ATM and avionics context

Gardi

Sabatini

Ramasamy

2016

Progress in Aerospace Sciences

116

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The continuous increase of air transport demand worldwide and the push for a more economically viable and environmentally sustainable aviation are driving significant evolutions of aircraft, airspace and airport systems design and operations. Although extensive research has been performed on the optimisation of aircraft trajectories and very efficient algorithms were widely adopted for the optimisation of vertical flight profiles, it is only in the last few years that higher levels of integration were proposed for automated flight planning and rerouting functionalities of innovative Communication Navigation and Surveillance/Air Traffic Management (CNS/ATM) and Avionics (CNS+A) systems. In this context, the implementation of additional environmental targets and of multiple operational constraints introduces the need to efficiently deal with multiple objectives as part of the trajectory optimisation algorithm. This article provides a comprehensive review of Multi-Objective Trajectory Optimisation (MOTO) techniques for transport aircraft flight operations, with a special focus on the recent advances introduced in the CNS+A research context. In the first section, a brief introduction is given, together with an overview of the main international research initiatives where this topic has been studied, and the problem statement is given. The second section introduces the mathematical formulation and the third section reviews the numerical solution techniques, including discretisation and optimisation methods for the specific problem formulated. The fourth section summarises the strategies to articulate the preferences and to select optimal trajectories when multiple conflicting objectives are introduced. The fifth section introduces a number of models defining the optimality criteria and constraints typically adopted in MOTO studies, including fuel consumption, air pollutant and noise emissions, operational costs, condensation trails, current airspace and airport operations. A brief overview of atmospheric and weather modelling is also included. Key equations describing the optimality criteria are presented, with a focus on the latest advancements in the respective application areas. In the sixth section, a number of MOTO implementations in the CNS+A systems context are mentioned with relevant simulation case studies addressing different operational tasks. The final section draws some conclusions and outlines guidelines for future research on MOTO and associated CNS+A system implementations.

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“…This procedure consists in steering the aircraft to the maximum altitude that can reach in order to prevent a crash on the ground. In the references [22], [21], the authors propose a Chebyshev-type optimal control for which an approximate solution for the problem is derived along with the associated feedback control. This solution was improved in [11] and [12] by considering the switching structure of the problem that has bang-bang subarcs and singular arcs.…”

Section: Introductionmentioning

confidence: 99%

“…Here, we consider the same problem formulation as in [22,21,11,12]. The Hamilton Jacobi approach is used in order to characterize the value function and compute its numerical approximations.…”

Section: Introductionmentioning

confidence: 99%

Value function and optimal trajectories for a maximum running cost control problem with state constraints. Application to an abort landing problem

et al. 2018

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The aim of this article is to study the Hamilton Jacobi Bellman (HJB) approach for state-constrained control problems with maximum cost. In particular, we are interested in the characterization of the value functions of such problems and the analysis of the associated optimal trajectories, without assuming any controllability assumption. The rigorous theoretical results lead to several trajectory reconstruction procedures for which convergence results are also investigated. An application to a five-state aircraft abort landing problem is then considered, for which several numerical simulations are performed to analyse the relevance of the theoretical approach.

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Quasi-steady flight to quasi-steady flight transition for abort landing in a windshear: Trajectory optimization and guidance

Cited by 13 publications

References 18 publications

A differential game control problem with state constraints

A differential game control problem with state constraints

Multi-objective optimisation of aircraft flight trajectories in the ATM and avionics context

Value function and optimal trajectories for a maximum running cost control problem with state constraints. Application to an abort landing problem

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