Method for managing the continuous climb of an aircraft or drone

Patent metadata

Field	Value
Publication number	US-11934205-B2
Application number	US-202017775865-A
Country	US
Kind code	B2
Filing date	Nov 13, 2020
Priority date	Nov 14, 2019
Publication date	Mar 19, 2024
Grant date	Mar 19, 2024

Abstract

Methods and devices for optimizing the climb of an aircraft or drone are provided. After an optimal continuous climb strategy has been determined, a lateral path is determined, in particular in terms of speeds and turn radii, based on vertical predictions computed in the previous step. Subsequently, computation results are displayed on one or more human-machine interfaces and the climb strategy is actually flown. Embodiments describe the use of altitude and speed constraints and/or settings in respect of speed and/or thrust and/or level-flight avoidance and/or gradient-variation minimization, and iteratively fitting parameters in order to make the profile of the current path coincide with the constrained profile in real time depending on the selected flight dynamics (e.g. energy sharing, constraint on climb gradient, constraint on the vertical climb rate). System (e.g. FMS) and software aspects are described.

First claim

The invention claimed is:

1. A method for optimizing the climb phase of an aircraft or a drone, the method being computer-implemented in an on-board or ground-based flight management system, avionics navigation system, or non-avionics navigation system, and the method comprising the steps of:
determining an optimal continuous climb strategy for the climb phase, the optimal continuous climb strategy being determined depending on flight-profile parameters, said flight-profile parameters comprising altitude and/or speed constraints, and/or settings in respect of speed and/or thrust and/or level-flight avoidance and/or gradient-variation minimization, and allowing vertical climb predictions for the climb phase to be computed;
determining a lateral path, depending on lateral-path parameters comprising settings in respect of turn radius as a function of roll-angle settings, and depending on the vertical predictions for the climb phase computed for the flight-profile parameters; and
iterating the two previous steps on each constrained climb segment of the aircraft or drone climb path, a constrained climb segment being a segment of the climb path containing a binding constraint; then iterating the two previous steps over the entire climb path, while fitting in each iteration said flight-profile parameters and said lateral-path parameters, until all the binding constraints have been met one by one according to a predefined strategy,
wherein the iterating step consists of an incremental method for solving a binding constraint (Cn) for a constrained segment from a save point issued from the previous constrained segment (Cn−1), which save point is re-estimated in order to solve the binding constraint (Cn), the segment-by-segment fit leading to reconsideration of the i previous segments impacted by the solution of constraint n, the setting parameters selected being applicable to all of the constrained climb segments between Cn−i and Cn.

2. The method as claimed in claim 1 , wherein the iterating step is carried out using an iterative method for fitting parameters, chosen from:
a dichotomous fitting method;
a brute-force fitting method;
a so-called secant fitting method;
an estimator-based fitting method;
the choice of the method possibly being dictated by a criterion of better performance in respect of response time.

3. The method as claimed in claim 1 , wherein the predefined strategy is adjustable by a user and comprises one or more parameters selected from:
a setting in respect of the energy-sharing ratio between potential energy and kinetic energy;
a setting in respect of the climb gradient;
a setting in respect of the vertical climb rate;
a setting in respect of the longitudinal speed during the climb;
a setting in respect of the climb thrust; and/or
a setting in respect of the roll angle.

4. The method as claimed in claim 1 , wherein one or more target settings are converted into pseudo-constraints taken into account in the computation of the vertical predictions.

5. The method as claimed in claim 1 , wherein one or more intermediate computation results, pieces of information relating to the root causes and/or the computation context of the steps of the method are displayed on a human-machine interface offering options in respect of modifications by a user.

6. The method as claimed in claim 1 , further comprising a step of applying the optimal continuous climb strategy, by transmitting all or some of the computed information with a view to it being actually exploited by avionic systems in order to apply the obtained optimal climb strategy.

7. A system comprising one or more flight management system (FMS) computers, or on-board tablet-based or ground-based non-avionics navigation computers, for implementing the steps of:
determining an optimal continuous climb strategy for the climb phase, the optimal continuous climb strategy being determined depending on flight-profile parameters, said flight-profile parameters comprising altitude and/or speed constraints, and/or settings in respect of speed and/or thrust and/or level-flight avoidance and/or gradient-variation minimization, and allowing vertical climb predictions for the climb phase to be computed;
determining a lateral path, depending on lateral-path parameters comprising settings in respect of turn radius as a function of roll-angle settings, and depending on the vertical predictions for the climb phase computed for the flight-profile parameters; and
iterating the two previous stepson each constrained climb segment of the aircraft or drone climb path, a constrained climb segment being a segment of the climb path containing a binding constraint; then iterating the two previous steps over the entire climb path, while fitting in each iteration said flight-profile parameters and said lateral-path parameters, until all the binding constraints have been met one by one according to a predefined strategy,
wherein the iterating step consists of an incremental method for solving a binding constraint (Cn) for a constrained segment from a save point issued from the previous constrained segment (Cn−1), which save point is re-estimated in order to solve the binding constraint (Cn), the segment-by-segment fit leading to reconsideration of the i previous segments impacted by the solution of constraint n, the setting parameters selected being applicable to all of the constrained climb segments between (Cn−i) and (Cn).

8. A non-transitory computer readable medium comprising code instructions that allow a process comprising the steps of:
determining an optimal continuous climb strategy for the climb phase, the optimal continuous climb strategy being determined depending on flight-profile parameters, said flight-profile parameters comprising altitude and/or speed constraints, and/or settings in respect of speed and/or thrust and/or level-flight avoidance and/or gradient-variation minimization, and allowing vertical climb predictions for the climb phase to be computed;
determining a lateral path, depending on lateral-path parameters comprising settings in respect of turn radius as a function of roll-angle settings, and depending on the vertical predictions for the climb phase computed for the flight-profile parameters; and
iterating the two previous stepson each constrained climb segment of the aircraft or drone climb path, a constrained climb segment being a segment of the climb path containing a binding constraint; then iterating the two previous steps over the entire climb path, while fitting in each iteration said flight-profile parameters and said lateral-path parameters, until all the binding constraints have been met one by one according to a predefined strategy;
the process to be performed, when said program is executed on a computer,
wherein the iterating step consists of an incremental method for solving a binding constraint (Cn) for a constrained segment from a save point issued from the previous constrained segment (Cn−1), which save point is re-estimated in order to solve the binding constraint (Cn), the segment-by-segment fit leading to reconsideration of the i previous segments impacted by the solution of constraint n, the setting parameters selected being applicable to all of the constrained climb segments between (Cn−i) and (Cn).

Assignees

• Thales Sa

Inventors

• Boyer Johan
• Durand Geoffrey
• Sacle Jérôme

Classifications

• B64U50/10

  Propulsion (rotors specially adapted for rotorcraft or VTOL B64U30/20) · CPC title

• G05D1/0607 Primary

  specially adapted for aircraft · CPC title

• B64C39/024

  of the remote controlled vehicle type, i.e. RPV · CPC title

• G05D1/48 Primary

  Control of altitude or depth · CPC title

• G05D1/652

  Take-off (delivering or retrieving payloads G05D1/667) · CPC title