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    • 专利摘要:
    Systems (400) and methods for refining trajectories for aircraft include a trajectory prediction module (412) for predicting a series of four-dimensional trajectories for aircraft, and a constraint selection module (416) for determining a series of constraints according to the series of four-dimensional trajectories. The trajectory can be refined by mapping goal values associated with the four-dimensional trajectory series based on the determined set of constraints and estimating additional values for the goal based on the mapped values.
    公开号:FR3038999A1
    申请号:FR1656449
    申请日:2016-07-06
    公开日:2017-01-20
    发明作者:Szabolcs Andras Borgyos
    申请人:GE Aviation Systems LLC；
    IPC主号:
    专利说明:

    System and method for refining trajectories for aircraft
    A typical aircraft flight process begins with the declaration of a flight plan by an airline or pilot to a civil aviation authority (eg the Federal Aviation Authority in the United States). The flight plan broadly outlines the route followed during a flight and includes departure and arrival locations and times, as well as information on intermediate waypoints that define a route or flight path. . The airlines, despite their lack of physical existence, are similar to three-dimensional highways and can be defined with a series of intermediate waypoints. Waypoints are landmarks in physical space that are used for navigation purposes and usually include latitude, longitude, and altitude. While navigating according to a flight plan, the aircraft follows a trajectory that borrows in chronological order the series of waypoints. The flight trajectory actually followed by the aircraft is therefore called a four-dimensional trajectory, since the trajectory includes three spatial coordinates and a temporal coordinate. From the place of departure, the arrival location and the intermediate waypoints, a flight management system or trajectory predictor predicts the four-dimensional trajectory to be tracked by the aircraft. It is envisaged that modifying a four-dimensional trajectory on the basis of factors concerning the aircraft (ie speed, fuel, altitude, turbulence, wind, weather, etc.) and the availability of common resources (to track, airspace, traffic management services, etc.) can improve the efficiency of an aircraft or fleet of aircraft with respect to one or more commercial indicators (ie fuel savings , passenger flows, cost, etc.). However, predicting a four-dimensional path is a costly problem in computing resources. Thus, although the flight management system or trajectory predictor accurately predicts a four-dimensional trajectory, the prediction is a relatively time-consuming operation. Therefore, searching for an optimal four-dimensional trajectory directly in a continuum of possible four-dimensional trajectories for a flight is unlikely to be computer-readable in a real-time or near-real-time environment. The problem is exacerbated if we look for optimal trajectories for a fleet of aircraft.
    According to a first aspect, a method for refining a series of four-dimensional trajectories for aircraft comprises the steps of obtaining data relating to the series of four-dimensional trajectories; determining, by a constraint selection module, a series of constraints for the series of four-dimensional trajectories; converting by mapping values in a goal processor associated with the series of four-dimensional trajectories according to the set of constraints determined and estimating, in the processor, additional values for the purpose according to the mapped values. The steps of obtaining, determining, mapping, and estimating are repeated until a value mapped to the goal associated with a determined final set of constraints exceeds a predetermined threshold. An aircraft trajectory can be predicted based on the predetermined final set of constraints.
    In another aspect, a trajectory refining system includes a trajectory predictor for predicting a series of four dimensional trajectories for aircraft, a constraint selection module, and a refresh module. The constraint selection module determines a series of constraints for the four-dimensional trajectory series; converts by mapping values in a goal processor associated with the series of four-dimensional trajectories based on the predetermined set of constraints; and estimates additional values for the purpose based on the mapped values and repeats the determining, mapping, and estimating steps until a value mapped to goal associated with a determined final set of constraints exceeds one predetermined threshold. The update module is coupled to the constraint selection module and the trajectory predictor and is adapted to obtain data relating to a four-dimensional trajectory computed by the predictor of trajectories at the end of each repetition by the constraint selection module. Aircraft trajectory that can be predicted from the determined final set of constraints.
    In another aspect, a method for refining a series of four-dimensional trajectories for aircraft comprises the steps of: obtaining, from a predictor of trajectories, data relating to a four-dimensional trajectory for aircraft; determining, by a constraint selection module, a series of constraints for the series of four-dimensional trajectories; conversion by mapping values in a goal processor associated with the four-dimensional trajectory series based on the determined set of constraints; setting in the processor estimates of additional values for the objective based on the mapped values; and adjusting the estimates until a value converted by goal mapping associated with a determined final set of constraints exceeds a predetermined threshold. The trajectory of an aircraft can be predicted according to the final set of constraints determined. The invention will be better understood from the detailed study of some embodiments taken by way of nonlimiting examples and illustrated by the appended drawings in which: FIG. 1 is an example of a schematic illustration of aircraft with trajectories and a ground system according to various aspects described herein; FIG. 2 is an example of a schematic illustration of aircraft with trajectories and a series of constraints and a ground system according to various aspects described herein; FIG. 3 is an example of a schematic illustration of aircraft with trajectories and a refined series of constraints for an efficient cruising flight and a ground system according to various aspects described herein; FIG. 4 is an exemplary schematic illustration of aircraft with trajectories and a refined series of constraints for course extension and a ground system according to various aspects described herein; FIG. 5 is an example of a schematic diagram of a trajectory predictor system according to various aspects described herein; FIG. 6 is an exemplary block diagram of a trajectory predictor according to various aspects described herein; FIG. 7 is a flowchart illustrating a method for refining trajectories according to various aspects described herein; and FIG. 8 is a graph illustrating an iterative process for converging to a series of constraints that refines aircraft trajectories according to various aspects described herein.
    The following terms and expressions are used throughout the detailed description:
    Permissible Constraint: The appearance of a potential trajectory to be executed by an object, which includes inherent operating or performance limits of that object or the environment in which the object moves.
    Four-dimensional trajectory: a chronological sequence of points that describe a path followed by an object between a starting point and an end point, or as a vector in a spatiotemporal space that describes, among other things, the position of the object.
    Minor disturbance modification of one or more aspects of a subset of a set of interactive or interdependent elements. Series any number of elements, including a single element.
    Spatiotemporal: having both spatial and temporal properties.
    At least some embodiments of the invention provide tracking systems, methods and devices for determining a set of constraints such that four-dimensional trajectories tracked by aircraft are more efficient in a series of indicators. predefined. A "series" can include any number of predefined flags, including a single predefined flag. Although it is classically described by a series of three spatial coordinates and a temporal coordinate, "a four-dimensional trajectory" in the sense of this presentation can be defined as a chronological sequence of points that describe a path followed by an object between a starting point and end point or as a vector in a space-time space including the position of the object. Currently, aircraft four-dimensional trajectories are calculated based on factors including, but not limited to, origin, destination, intermediate waypoints, aircraft performance, weather conditions and spacing constraints. In response to various constraints or objectives or combinations thereof, a trajectory prediction system determines a four-dimensional trajectory and, given the unavoidable response times, does not fully exploit the potentially relevant information available to achieve objectives. strategies concerning the optimization of the fleet or the optimization of the on-board flight management system. More specifically, in most online applications, the trajectory prediction system does not have the computation time required to determine a four-dimensional trajectory that takes into account spacing constraints, weather conditions and optimal performance maneuvers. including cruising altitude with minimum fuel consumption / costs and an optimal fuel consumption / cost growth.
    Figure 1 shows a processor 36 in communication with a land station 32 communicating with an aircraft 10, 11 according to various aspects described herein. The aircraft 10, 11 may follow a route from one place to another and may include elements common in the aircraft, such as one or more propulsion engines 12 mounted on a fuselage 14. D other common elements in the aircraft include a cockpit 16 disposed in a fuselage 14 and half-wings 18 extending outwardly from the fuselage 14. Furthermore, a series of edge systems 20 which allow a smooth operation of the aircraft 10, 11 may also be included, in particular an automaton or a computer 22, and a communication system provided with a communication link 24. These edge systems 20 may comprise, by way of no limitation, an electrical system, an oxygen circuit, a hydraulic or pneumatic circuit, a fuel system, a propulsion system, a flight management system, flight controls, audio / video systems and an IVHM integrated state of the art system. unit, and systems associated with the mechanical structure of the aircraft 10, 11. Although a commercial aircraft has been shown, it is contemplated that embodiments of the invention may be used in any type of aircraft, for example, by no means limiting, fixed wing, rotary wing, rocket, personal aircraft, autonomous unmanned aircraft and military aircraft. The computer 22 can cooperate with the series of onboard systems 20 and it is envisaged that the computer 22 can contribute to the operation of the series of on-board systems 20 and can receive information from the series of onboard systems 20. L computer 22 can, among other things, automate the tasks of piloting and tracking the flight plan of the aircraft 10, 11. The computer 22 can also be connected to other automata or computers of the aircraft 10, 11 The computer 22 may comprise a memory 26, the memory 26 may comprise a random access memory (RAM), a read only memory (ROM), a flash memory or one or more different types of portable electronic memory such as disks. , DVDs, CD-ROMs, etc., or any suitable combination of these types of memory. The computer 22 may include one or more processors, which may execute any suitable programs. The computer 22 may comprise or be associated with any adequate number of individual components such as microprocessors, power sources, storage devices, interface boards, autopilot systems, management computers, and the like. and other conventional components, and the computer 22 can understand or cooperate with any number of software (eg flight management programs) or instructions for implementing the various methods, process tasks, calculations and control / display functions necessary for the operation of the aircraft 10, 11.
    The communication link 24 may establish communications with the computer 22 or other processors of the aircraft to transfer information to and from the aircraft 10, 11. It is envisaged that the communication link 24 may be a radio communication link and may be any communication mechanism allowing radio links with other systems and peripherals and may include, in no way limiting, packet radio, satellite uplinks, internet by SATCOM, air-ground internet services, very high frequency digital links, wireless fidelity (WiFi), WiMax, Bluetooth, ZigBee, a 3G radio signal, a code division multiple access radio signal (CDMA) , the Special Mobile Group (GSM), a 4G radio signal, a long-term evolution signal (LTE), Ethernet or any other combinations of these. The particular type or mode of radio communication is not essential in the embodiments of the present invention, and radio networks developed in the future are certainly contemplated as being within the scope of embodiments of the present invention. invention. Furthermore, the communication link 24 can be made to communicate with the computer 22 via a wired connection without changing the scope of embodiments of the invention. Although only one communication link 24 has been illustrated, it is envisaged that the aircraft 10, 11 may have multiple communication links allowing communication with the computer 22. These multiple communication links may give the aircraft The possibility of transmitting information in various ways to or from the aircraft 10.
    As illustrated, the computer 22 of the aircraft 10, 11 can communicate with a remote server 30, which can be anywhere, for example in a specific land station 32, via the communication link 24 The terrestrial station 32 may be any type of terrestrial communication station 32 including, by way of no limitation, an air traffic control center or an airport activity control center. The remote server 30 may comprise a computer searchable information database 34 accessible to the processor 36. The processor 36 may manage a set of executable instructions to access the computer searchable information database 34. The remote server 30 could comprise a multi-purpose computing device in the form of a computer, having a central unit, a system memory and a system bus, which couples to the central unit various components of the system including the system memory. The system memory may include a read-only memory (ROM) and a random access memory (RAM). The computer may also include a hard disk drive for reading and recording data on a magnetic hard disk, and an optical disk drive for reading or recording data on a removable optical disk such as a CD-ROM or a CD-ROM. other optical media. The computer-searchable information database 34 may be any suitable database, including a single database having multiple data sets, multiple individual databases related to one another, or even a simple table of data. It is contemplated that the computer-searchable information database 34 may group a number of databases or that the database may actually be a number of separate databases.
    During the operation of the aircraft 10, 11, the computer 22 can request or receive information from the remote server 30. In this way, the computer 22 can be part of a system to refine a trajectory for the aircraft 10, 11. According to another possibility or in addition, the system for fine-tuning the trajectory for the aircraft 10, 11 may comprise the computer 22 which may be part of the flight management system. According to another possibility or in addition, the memory may comprise a database component. The database component can be any suitable database, including a single database having multiple datasets, multiple individual databases related to each other, or even a simple data table. . It is envisaged that the computer-searchable information database component may aggregate a number of databases or that the database may actually be a number of separate databases. The database component may contain information including, but not limited to, airports, runways, airways, waypoints, navigation aids, airline / airline specific routes, and procedures such as Standard Instrument Departure (SID), Standard Terminal Approach (STAR) Routes and Approaches.
    Each aircraft 10, 11 may follow a route initially described by a flight plan which may include a series of waypoints. For example, an aircraft 10 may follow a route initially described by a flight plan that includes intermediate waypoints 40, 44 and waypoints at destination 46 (shown as an airport). In another example, an aircraft 11 may follow a route initially described by a flight plan that includes intermediate waypoints 42, 44 and waypoints at destination 46. The waypoints serve as navigation tags, but not as complete descriptions of the intended course of travel, as an aircraft can not change course instantly from one straight line segment to another, as represented by straight line segments 48, 50. On the contrary, a system such as the flight management system or other trajectory prediction system determines a four-dimensional trajectory 52, 54 that the aircraft can travel safely by passing near each waypoint approximately at the expected time for said waypoint.
    An update module (designated 314 in FIG. 5 and 414 in FIG. 6) of the trajectory refining system 28, included in the computer 22 or the remote server 30, can obtain data relating to the constraints or objectives specific to the series of four-dimensional trajectories 52, 54. The data may be information concerning any aspect of the route predicted to be followed by the aircraft including, but not limited to, latitude, longitude, hour, aircraft weight, fuel burn, vertical speed, ground speed, speed read Badin, temperature, turbulence, wind and combinations thereof.
    A constraint selection module (designated 316 in FIG. 5 and 416 in FIG. 6) of the trajectory refining system 28 included in the computer 22 or the remote server 30 may determine a series of constraints 56, 58 on the basis of the data obtained. In this way, the series of constraints 56, 58 represents a series of constraints or objectives that a four-dimensional trajectory created must consider, take into account or on which it must rest. Each constraint is a vector describing a single point on the four-dimensional trajectory. The constraint may include a series of values for any aspect of the four-dimensional trajectory at the representative point, including, but not limited to, altitude, latitude, longitude, expected arrival time, and order of points . The constraint selection module can select the constraints that make up the series of constraints according to any rule, strategy or initial criterion whose, in no way limiting, the distance between the constraints, the expected time of arrival of the aircraft in the places defined by the constraints, etc. For example, the stress selection module may regularly space the series of stresses 56 from the distance on the trajectory 52 for the aircraft 10. In another example, the stress selection module may set the stress series 58 near the intermediate waypoints 42, 44 and the destination waypoint 46 for the aircraft 11.
    The constraint selection module (designated in Figure 5 and 416 in Figure 6) manages the definition and selection of specific problem constraints. For example, if a goal of the trajectory refining system is to determine the best lateral path to achieve some predefined cost objective, the constraint selection module can divide the original four-dimensional trajectory into many constraints 56. Constraints are chosen from the spatio-temporal vector that defines the trajectory to be modified, so the constraints initially meet this four-dimensional trajectory. After having selected the initial series of constraints by a logic determined by the goal defined above, the constraint selection module defines a finite series which contains the selected constraints, 56. In optimal control, this series is commonly called a series of admissible constraints. . The constraint selection module defines the set of allowable constraints, a set of constraints that limits the search to the set of trajectories that fall within those constraints, and the aircraft-specific performance constraints taken in the trajectory predictor. .
    Figure 2 shows a processor 36 in communication with a land station 32 communicating to the aircraft 10, 11 a refined series of constraints according to various aspects described herein. The constraint selection module evaluates the set of constraints 56, 58 and, on the basis of the evaluation, selects a new set of constraints, disrupting the set of constraints 56, 58 in the set of allowable constraints. The constraint selection module selects a perturbation 60, 62 that modifies the series of aspects described by each constraint. The perturbation may modify any aspect of the constraint vector, including, but not limited to, the latitude, longitude, and time requirements of each constraint 56, 58, as shown in Figure 2 and discussed in more detail herein.
    Illustrating an embodiment of the trajectory refining system 128 to improve the flight path in cruising mode, Figure 3 depicts an example where the disturbance 160 can modify the altitudinal stresses 156. The cruising flight is a part horizontally of the course of an aircraft which occupies most of the flight time of an aircraft. That's why it offers the best chance of optimizing costs and fuel consumption. At the highest altitudes, aircraft tend to operate with better fuel efficiency; however, the altitudes accessible to the aircraft depend on the weight of the aircraft. As aircraft burn their fuel, their weight changes, and it becomes possible to save more fuel when going up or cruising to benefit from weather conditions to improve fuel efficiency. The climb or upward cruise described is thwarted by factors, including, in no way limiting, atmospheric conditions encountered by aircraft in flight, other strategic objectives of operators such as hourly performance, other minima and constraints on the air traffic and spacings imposed by air navigation service providers. Although the trajectory refining system 128 can calculate the optimum upward cruise in order to limit as much as possible the amount of fuel consumed before the start of the descent 170, the trajectory refining system 128 can refine the flight path. origin 152 to compensate for additional commercial and logistic objectives and to produce a refined trajectory 175. The additional commercial and logistic objectives may be any objective related to the operation of aircraft and may include, by no means restricting, the reduction in fuel consumption. fueling, coordinating the arrival times of multiple aircraft in a fleet, limiting missed connections by passengers, limiting costs for operators, etc. The aircraft 10, during the cruising phase of a route, can fly at a stable altitude provided by the trajectory prediction module (designated 312 in FIG. 5 and 412 in FIG. ), in part on the basis of the intermediate waypoints 140 which share a common altitude. The constraint selection module can determine a series of regularly spaced constraints 170, 156 on the trajectory 152. The constraint selection module can disturb a subset of the upper or lower constraints 174 at altitude in order to determine a series. defined final constraints 170, 174. The trajectory prediction module then determines a refined trajectory 172 according to the final set of constraints determined 174. In this way, the final trajectory followed by the aircraft 10 is refined at altitude only on the cruise regime part of the flight.
    Represented in Figure 3, the stress selection module selects a number of altitudes that initially fit into the path to be refined and constructs a series of allowable altitudinal constraints that encompass the initial altitudinal constraints 156 of the trajectory according to various aspects. described here. For example, to meet US altitudinal spacing standards, the set of permissible stresses could contain distinct altitude values encompassing the initial altitude and values at 610 meter (2000 feet) intervals from that altitude to at the maximum altitude for the aircraft concerned and up to a minimum altitude.
    FIG. 4 illustrates an example of a trajectory refining system 228 for implementing a course extension maneuver where a route is modified to push the required arrival time, compared to a scheduled time, to a time later, while respecting an objective defined by the operator according to various aspects described here. To complete a course extension maneuver, an aircraft departs from the nominal flight path and changes the relative speed to lengthen the overall flight path. A lengthening maneuver can be used to better plan the trajectory and the management of the trajectory with the help of considerations which may include, in no way limiting, the traffic of the sector, the meteorology, the emissions, fuel combustion or costs for the airline.
    To perform a course extension maneuver, the aircraft 10 can fly, on the course, in a spatially defined region 280 lending itself to deviations from the course in order to repel an arrival time at a point of departure. 244 final routing for arrival at destination 246. In parallel with the lateral deviation due to the change of the course length of the trajectory, the speed of the aircraft is modified so as to limit as much as possible an objective bearing, in no way limitation, on the limitation of costs or fuel consumption. The non-elongated portion of the trajectory 252, predicted by the trajectory prediction module, is based in part on the intermediary waypoints 240. The constraint selection module can determine a series of constraints 256 which are intermediate in time, intermediate waypoints 240, and a constraint 258 which is confined in space at the final waypoint 244 but is dynamic in time. The constraint selection module disturbs in space the series of constraints 256 according to a time constraint to be applied to respect a specific arrival time imposed on the constraint 258. Thus, the constraint selection module determines the position of the set of constraints 256 so that the time associated with the final stress 258 is consistent with a desired delayed arrival time.
    As shown in FIG. 4, the constraint selection module can determine the stresses 256 in the spatially defined region 280 as one of many possible sets of stresses 256A, 256B and 256C. Each series of stresses 256A, 256B, 256C gives a respective trajectory 282, 284, 286 calculated by the trajectory prediction module, each trajectory 282, 284, 286 will exclusively report the time of arrival of the aircraft 10 at the point of departure. final path 244 encoded in the final constraint 258. The trajectory prediction module determines a refined trajectory according to the determined final set of constraints which corresponds to the lengthening maneuver with the desired delay of the arrival time. to destination 246.
    Referring now to Figure 5, there is shown an exemplary block diagram of a line refining system 300 for predicting trajectories according to various aspects described herein. The trajectory refining system 300 includes a trajectory predictor 310 and a communication link 322.
    The trajectory predictor 310 includes an update module 314 coupled to communicate with a constraint selection module 316 and a prediction module 312 of trajectories. A memory module 318 comprising a database sub-module 320 communicates with the trajectory prediction module 312, the update module 314 and the constraint selection module 316. As shown, the trajectory predictor 310 comprises the update module 314, the constraint selection module 316 and the memory module 318, as well as the prediction module 312 of trajectories. The trajectory predictor 310 may be in an aircraft (eg, part of a flight management system) and communicate with one or more stations via the communication link 322. The predictor components 310 of trajectories may be find at the same location in the aircraft or be placed at various locations throughout the aircraft, according to the embodiment. The update module 314, the constraint selection module 316 and the trajectory prediction module 312 may comprise any appropriate combination of hardware and software elements necessary for the operation of the trajectory refining system 300, of which in no way limiting, specific application integrated circuits, flash memory, random access memory, user programmable gate arrays and combinations thereof including specific software and industry standards configured on said devices to provide required functions associated with the implementation of these modules.
    Referring now to Figure 6, there is shown an exemplary schematic diagram of a refining system 400 of trajectories for predicting trajectories according to various aspects described herein. The refining system 400 of paths is similar to that shown in Figure 5; therefore, the identical parts will be designated by the same numerical references increased by 100, it being understood that, unless otherwise mentioned, the description of the identical parts of the first trajectory refining system is valid for the second system. The refining system 400 of trajectories comprises a predictor 410 of trajectories having a trajectory prediction module 412. The trajectory prediction module 412 communicates via the communication link 422 with a remote refining component 424, physically separated from the flight management system 410. In this way, the trajectory refining system 400 may comprise a flight management system 424. conventional flight management 410. The remote refining component 424 may be part of a remote server managed, for example, in an air traffic control center or an airport activity control center.
    Figure 7 is a flowchart illustrating a process 500 for refining aircraft trajectories according to various aspects set forth herein. Starting with step 510, data relating to a flight plan not executed previously or a cruising aircraft are made available to the trajectory refining system. In step 512, the update module obtains data relating to a series of four-dimensional trajectories. The data may relate to any aspect of the four-dimensional trajectory including, but not limited to, altitude, latitude, longitude, expected time and order of arrival, wind speed, temperature, the speed read at Badminton, ground speed or combinations of these provided at waypoints. In step 514, the constraint selection module determines a series of allowable constraints that delimit the series of allowable four-dimensional trajectories that could respond to the problem. Like the data obtained by the update module, the set of constraints can include any aspect of the four-dimensional trajectory, including, but not limited to, altitude, latitude, longitude, time of arrival expected, the order if the order is included in the case where the same spatial coordinate is visited multiple times and the arrival time is not specified, etc.
    In a step 518, the constraint selection module 316, 416 converts target values associated with the four-dimensional trajectory series based on the determined set of constraints. Goals can be linked to any commercial or logistic objective, to the optimization of a fleet or to the optimization of the management of the flights on board, of which, in no way limiting, the maneuvers of extension of courses, the profiles optimal cruising speed, coordination of arrival time, fuel consumption, fuel cost, arrival time, flight duration, avoidance of harsh weather conditions, etc. The constraint selection module can map the set of constraints into goal-indicating metrics using any type of mapping that converts a four-dimensional trajectory into a value correlated to the achievement level of a goal of which, by no means limiting, the implementation of an objective function. By determining an objective function, the constraint selection module 316, 416 converts the relation between the values of the set of trajectories defined by the set of allowable constraints into a real number which represents a cost or goal associated with the four-dimensional trajectories. For example, flying an aircraft along the trajectories associated with the stress series in Figure 2 will result in some fuel expense.
    Disturbing the series of constraints among the series of allowable stresses in order to modify the trajectories will cause a different fuel expense. The constraint selection module 316, 416 converts the set of constraints by mapping to a value indicating fuel expense (eg cost). In another example, flying an aircraft along a trajectory determined by the altitudinal profile shown in Figure 3 will also cause some fuel expense. Disturbing the set of constraints to change the altitudinal profile will result in a different fuel expense. In yet another example, flying an aircraft following a course extension maneuver determined by the trajectory shown in Figure 4 will cause some delay in the arrival time at the destination of the flight. Limiting the course extension maneuver to a defined schedule constraint 258 causes the aircraft to fly with different speed profiles on each elongate side course to the destination. Thus, disrupting the length of a lateral path 252 by experimentally inserting a series of stresses 256A-C to make the trajectory 252 the paths 282, 284 and 286 while maintaining the time constraint 258 will lead to different fuel costs between intermediate waypoints 240 and final waypoint 244.
    In one case, in a step 518, the constraint selection module 316, 416 iteratively converts by mapping the set of constraints into goal by constructing the objective function based on observations of a number of constraints among the allowable series by evaluating the objective function for the induced trajectories and predicting unobserved objective function values using the pairs of observed trajectory-objective values, in a step 520. As the method 500 is iterative, previously calculated values for previously determined sets of constraints are the previous information used, in part, during the estimation of the objective function.
    The constraint selection module 316, 416 determines, in step 516, whether the calculated value converted by mapping to goal exceeds a predetermined threshold. For example, if the constraint selection module 316, 416 maps the goal values based on the set of constraints using an objective function, then the predetermined threshold can include calculating a value. of the objective function for the set of constraints in force that exceeds a predetermined threshold. The predetermined threshold is any limit that effectively leads the iterative process to its end which, by no means limiting, a limit on the computer budget (eg a total time or a number of computing cycles to devote to the calculation of a refined trajectory for the given computer hardware), the convergence towards an extremum of an objective function, and the exceeding of a predetermined value of the objective function. If the value calculated for the objective function does not exceed a predetermined threshold for the series of constraints then observed, the constraint selection module determined at step 514 a new set of constraints to be observed according to the estimate considered of the objective function. The constraint selection module can determine the following set of constraints by any process that reduces the uncertainty in estimating the objective function. For example, the constraint selection module can select the next set of constraints to be evaluated partially based on the estimated average and the uncertainty of the objective function. As part of the iterative process observed between steps 516 and 512, the constraint selection module communicates with the trajectory prediction module via the update module to transmit data concerning the set of constraints and the refined four-dimensional trajectories. Thus, at each iteration where the constraint selection module determines a next set of constraints, the update module transmits the data on the four-dimensional trajectories to the trajectory prediction module which calculates a refined trajectory. The refined path is then returned to the constraint selection module via the update module to determine the next set of constraints.
    When it is determined that a value calculated for the objective function exceeds a predetermined threshold, the update module provides the final set of constraints determined in a step 522 to the trajectory prediction module. Finally, during a step 524, the trajectory prediction module can determine the refined trajectory according to the final set of constraints. For purposes of illustrating the iterative process that may be part of the trajectory refining system, Figure 8 is a graph illustrating the relationship between sets of constraints and an estimated objective function. Thus, Figure 8 illustrates an iterative process for converging to a set of constraints that refines aircraft trajectories using a method such as that illustrated in Figure 7. In Figure 8, a partially known objective function is represented in dotted lines. The objective function is observed in Dl, D2 and D3 by four-dimensional trajectory calculations inscribed in Dl, D2 and D3 as well as aircraft performances collected in a database of performances and weather conditions in space derived from the meteorological model of the trajectory predictor and by an evaluation of the objective function from these trajectories to determine RI, R2 and R3 values. The unobserved or unassessed portion of the objective function is then calculated or approximated using a measure of the correlation between the pairs of observed and previously unobserved objective function values. The goal of any optimization procedure is to find in a minimum of time the global extrema values (ie minima or maxima) of the interesting objective function; in other words, the goal is a quick convergence to the maximum value (ie a benefit) or the minimum value (ie a cost) of the function. As shown in FIG. 8, the goal of the optimization procedure is achieved by the repeated observation of pairs of objective function values, the prediction of unobserved values and the section of new constraints to be evaluated until the convergence towards an approximated extremum or the achievement of a delay goal for the system to return a solution consisting of a series of constraints.
    As described above, the method is iterative, so the constraint selection module determines a first set of constraints D1 and then evaluates it to determine RI. Then, with the additional knowledge of the pair (D1, R1), the constraint selection module determines a second set of constraints D2, and then evaluates D2 to determine R2. Then, with the additional knowledge of the pair (D1, R1) as well as the pair (D2, R2), the constraint selection module determines a third set of constraints D3, then evaluates D3 to determine R3. If R3 does not exceed a predetermined threshold, the iterative process continues, otherwise the update module delivers the set of constraints D3 as a constraint which describes the refined trajectory.
    In the illustrated example and, more generally, in the optimization problem formulation partially solved by the constraint selection module, the constraint selection module estimates the objective function, and it does so to improve the reliability of the estimation since additional sets of constraints D are evaluated. Since the constraint selection module searches for an extremum of the objective function, each evaluation of a pair (D, R) increases the knowledge and reduces the uncertainty in the estimation of the objective function. In other words, from what is known from D1, D2 and D3 in the example shown in Figure 8, an extremum in the form of a maximum in the objective function appears to the left of D3. The shaded area superimposed on the dotted representation of the objective function represents the variance or uncertainty in the estimation of the objective function after the three iterations (D1, R1), (D2, R2) and (D3, R3). Initially, the estimate of the uncertainty would have been much larger and each iteration would reduce the uncertainty around a pair (D, R) evaluated as well as around D. As illustrated in the example of the Figure 8, the constraint selection module can choose a series of constraints just to the left of D3 and evaluate for R. The repetition of this strategy is likely to cause the constraint selection module to converge towards this maximum for the objective function .
    As evidenced by the large uncertainties to the left of Dl and to the right of D2, the strategy may not necessarily discover the global extremum. Since the objective function is unknown to the constraint selection module, a more optimal set of constraints beyond D1 or D2 can not be ruled out without evaluating sets of constraints D into these regions. When the constraint selection module determines a series of constraints D and evaluates the value of R, the uncertainty surrounding this pair (D, R) falls in such a way that with the set of constraints D considered the constraint selection module has a precise knowledge of R and is more certain of the value of R for the points close to the evaluated series D which represent sets of constraints similar to the set of constraints considered. Therefore, the constraint selection module can choose a next set of constraints D where the uncertainty is greatest to increase knowledge of the objective function.
    Consequently, the constraint selection module determines the following set of constraints D according to two fundamental objectives: the exploitation of the available information of the objective function to find an extremum that satisfies a predetermined threshold or the exploration of regions presenting the greater uncertainty. The constraint selection module can reconcile these objectives using any strategy designed to determine an extremum value of an unknown objective function as well as to reduce the uncertainty in an estimate of an unknown objective function, including, in no way limiting, the three strategies presented below.
    The constraint selection module can select the next set of constraints D to evaluate according to a coin-to-coin draw strategy. To start with, the constraint selection module can direct the draw to the choice of constraint sets in areas of greatest uncertainty. As the constraint selection module acquires knowledge of the objective function (eg, as the number of evaluations increases), the constraint selection module can direct the draw to a series of constraints. closest to the extremum predicts the objective function. Alternatively, the constraint selection module can set the number of iterations to select sets of constraints in areas of greatest uncertainty, then set a number of iterations to select sets of constraints closest to the extremum predicts the objective function. Alternatively, the constraint selection module can select sets of constraints in areas of greatest uncertainty up to a set of constraints where the evaluated value of the objective function exceeds a predetermined threshold.
    In real size, certain practical requirements and limits define the super-series of realizable constraints. The practical requirements and limits may be any known requirements and limits for limiting the set of achievable constraints and include, in no way limiting, the physical limits of the aircraft, the standards and operating procedures for air traffic, etc. In this way, the uncertainty D on the edges of the X axis decreases because the constraint selection module implicitly knows that these sets of constraints will not give a desirable value for the objective function.
    The method and the constraint selection module above can use any algorithms and strategies useful for iterative optimization, including, by no means limitation, the approximation of optimal aircraft trajectories by calculating a vector of optimal constraints using an allowable, problem-specific, four-dimensional trajectory constraint as well as implicit aircraft performance and meteorological constraints collected in the trajectory prediction module using a Bayesian Optimization method ( OB). In this implementation, the constraint selection module constructs a series of specific allowable constraints of the problem, such as allowable altitudinal constraints 156 on the cruising trajectory 152 of an aircraft, described in the problem of optimal profile of the aircraft. cruising regime shown in Figure 3. For the purpose of selecting in the admissible series the optimal constraint that results in convergence towards the overall maxima or minima of the objective function without the need to evaluate the objective function at each objective constraint the method forms a Gaussian joint distribution on the R (s) series of objective function values observed in Figure 8 and unobserved objective function values. Using a model describing the correlation between pairs of objective function values from the point of view of the admissible stress pairs and the Gaussian distribution of the observed and unobserved objective function values over the admissible stress series, D (s) on In Figure 8, the method predicts the average value of the objective function and the uncertainty around this average. Mean and uncertainty after three allowable stress observations (Dl, D2 and D3) are represented by the dashed line and hatched area of Figure 8. The latest knowledge of the mean and covariance of the objective function serve as to estimate the unobserved stress associated with the estimated optimum of the objective function. Based on the strategy implemented to further explore the uncertain regions of the objective function (ie, predicted high variance constraint values) or to exploit the measures in order to reach the extrema of the objective function the constraint associated with the estimated optima or another constraint associated with very uncertain constraints is evaluated. The optimal constraint approximation includes the prediction of trajectory refinement using the explicit constraints defined in the chosen constraint vector, along with the compliance with performance constraints and other aircraft specific constraints implicitly collected in the constraint vector. the trajectory prediction system (eg a flight management system or other trajectory prediction system). The trajectory refining system iteratively repeats the process of selecting a new constraint value for the prediction of trajectories, and thus the observation of the objective function value with this constraint, of updating the very last knowledge with the observation made at this time, of a new prediction of the Gaussian distribution of the values of objective functions on the series of admissible stresses and of the selection of an approximately minimizing stress until the convergence of the solution within a predetermined threshold. The predetermined threshold is any limit that effectively puts an end to the iterative process, including, by no means limiting, a limit on the computer budget (eg a total time or a number of calculation cycles to be devoted to the calculation of a refined trajectory for the given computer hardware), the convergence towards an extremum of the objective function, and the exceeding of a predetermined value of the objective function.
    The technical effects of the embodiments described above include a scalable, budget-conscious trajectory optimization system that determines computer-manageable trajectory improvements across multiple platforms. In particular, the above-described embodiments of the system and method could be implemented on aircraft (eg, in the SGV) to be part of a ground system.
    The volume of trajectories that must be predicted in order to evaluate and maximize the objective function is the main cause of the prohibitive IT cost that makes many optimization algorithms in aviation unmanageable. In this sense, the prediction of a four-dimensional trajectory is done using the SGV or other Predictor of Trajectories. Depending on the physical implementation of the Trajectory Predictor, the number of free parameters in the optimization problem, (optimization of the altitudinal profile of a single aircraft as opposed to the routing of new routes of a fleet of aircraft) and the amount of time available for determining a solution, the optimization problem can quickly become unmanageable. The embodiments of the system and method described above use prior knowledge and inferences to approach an optimal solution to better deal with the factors that lead to the unreliability discussed above. The sequential nature of the process allows a ceiling for the IT budget on the basis of a targeted hardware implementation, because, whatever the IT budget, at the end of any iterative sequence, it is guaranteed that the solution is at least as good or more optimal than the initial trajectory.
    The above-described system and process embodiments could serve as a basis for an on-board trajectory optimizer implemented in the Flight Management System (VMS) or a ground tool for fleet optimization. With respect to the embedded implementation, the embodiments of the system and method described above may be employed with a specific objective function of a problem to resolve the optimal upward cruise paths. The solution series, in this case, would consist of an optimal set of altitudinal constraints on the course, associated with the initial four-dimensional trajectories of aircraft recorded in the VMS. Likewise, an objective function allowing lengthening of the on-board journey in the VMS can be optimized.
    With respect to fleet optimization, the above-described system and process embodiments may be used to provide a trajectory plot taking into account local traffic, weather conditions, emissions and fuel combustion. or the costs to airlines by merging available data in the air and on the ground. The ground system has accurate information about local weather conditions, traffic and fleet goals. At the same time, knowledge of the exact capabilities of each aircraft is limited. In the air, the capabilities of aircraft are known precisely; however, knowledge of traffic and weather conditions is limited depending on the circumstances. Using the embodiments of the system and method described above, ground systems can achieve global optimization in the air sector to select substantially optimal four-dimensional trajectory constraints for each aircraft in the fleet. Each individual aircraft, using the accurate performance data collected in the VMS, would locally optimize the aircraft trajectory around the set of stresses provided by the ground. The proven benefits of an air-ground coupled optimization solution would be a strategic advantage for mixed ground-system VMS systems.
    Insofar as they are not already described, the various details and structures of the various embodiments can be used at will in combination with each other. The fact that a detail is not illustrated in all the embodiments is not intended to be interpreted as meaning that it can not be, but is only intended to make the description more concise. Thus, the various details of the various embodiments can be mixed and adapted to will to achieve new embodiments, and that the new embodiments are expressly described or not. All combinations or permutations of details described herein are covered by this disclosure.
    List of marks 10 - Aircraft 11 - Aircraft 12 - Engine 14 - Fuselage 16 - Cockpit 18 - Half-wing 20 - On-board systems 22 - Computer 24 - Communication link 26 - Memory 28 - Tracking system 30 - Server Remote 32 - Terrestrial Station 34 - Database 36 - Processor 40 - Intermediate Waypoint 42 - Intermediate Waypoint 44 - Intermediate Waypoint 46 - Waypoint Waypoint 48 - Segment in a Straight Line 50 - Segment in a Straight Line 52 - Initial 4D trajectory 54 - Initial 4D trajectory 56 - Constraint 58 - Constraint 60 - Disturbance 62 - Disturbance 128 - Trajectory refinement 140 - Intermediate waypoints 156 - Constraints 160 - Disturbance 170 - Constraints 172 - Fine trajectory 174 - Constraints 228 - Trajectory Refinement System 240 - Intermediate Waypoints 244 - Final Waypoint 256 - Constraints 258 - Constraint Finale 280 - Area 282 - Trajectory 284 - Trajectory 286 - Trajectory 300 - Trajectory refinement system 310 - Trajectory prediction 312 - Constraint prediction module 314 - Refresher module 316 - Constraint selection module 318 - Module memory 320 - Database 322 - Communication link 400 - Path refinement system 410 - Path prediction 412 - Path prediction module 414 - Update module 416 - Constraint selection module 418 - Memory module 420 - Database 422 - Communication link 424 - Remote refinement component 500 - Path refinement process 512 to 524 - Process steps
    权利要求:
    Claims (14)
    [1" id="c-fr-0001]
    A method (500) for refining aircraft trajectories, the method comprising: obtaining (512), a predictor (310, 410) of trajectories in a computer (22), data relating to a series of trajectories. four-dimensional (52, 54) for an aircraft (10 H); determining (514), by a constraint selection module (316, 416) present in the computer, a set of constraints (56, 58, 156, 170, 256, 258) for the four-dimensional trajectory series ( 52, 54); mapping (516) values in a processor of the target computer associated with the series of four-dimensional trajectories (52, 54) based on the determined set of constraints (56, 58, 156, 170, 256, 258); estimating (520) additional values in the processor for the purpose, based on the mapped values; and repeating the steps of obtaining, determining, mapping, and estimating (512, 514, 516, 520) until a target-converted vapor for a determined final set of constraints (170, 174) exceeds a predetermined threshold; a trajectory (282, 284, 286) of the aircraft being predicted from the determined final set of constraints (170, 174).
    [2" id="c-fr-0002]
    The method (500) of claim 1, wherein the step of determining (514) the set of constraints (56, 58, 156, 170, 256, 258) further comprises a step of selecting the set of constraints (56, 58, 156, 170, 256, 258) based on a Gaussian distribution over a series of observed and unobserved objective function values.
    [3" id="c-fr-0003]
    The method (500) of claim 1, further comprising the steps of reducing an uncertainty in estimating an objective function and selecting a set of constraints based on an estimate of a mean and uncertainty of the objective function.
    [4" id="c-fr-0004]
    The method (500) of claim 1, wherein the prediction data includes aircraft weight, fuel consumption, vertical speed, ground speed, speed read data. Badin, temperature, turbulence or wind on the four-dimensional path (52, 54).
    [5" id="c-fr-0005]
    The method (500) of claim 1, wherein the series of constraints (56, 58, 156, 170, 256, 258) is selected to refine a trajectory of an aircraft (10, 11) to execute a path lengthening maneuver which modifies the travel time on the flight path, the speed of the aircraft being modified so as to limit as far as possible an objective function relating to the reduction of the fuel consumption or the limitation of the costs of exploitation.
    [6" id="c-fr-0006]
    The method (500) of claim 1, wherein the series of constraints (56, 58, 156, 170, 256, 258) is selected to refine a series of trajectories (52, 54) to coordinate the hours of operation. arrival of multiple aircraft from a fleet.
    [7" id="c-fr-0007]
    The method (500) of claim 1, wherein the set of constraints (56, 58, 156, 170, 256, 258) includes values relating to altitude, latitude, longitude, time, or time. expected order of arrival.
    [8" id="c-fr-0008]
    8. A trajectory refining system (128, 138, 300, 400) comprising a computer (22), the computer comprising: a predictor (310, 340) of trajectories for predicting a series of four-dimensional trajectories (52, 54 ) for an aircraft 9 a constraint selection module (316, 416) able to determine (514), by a constraint selection module (316, 416), a set of constraints (56, 58, 156, 170, 256 , 258) for the four-dimensional trajectory series (52, 54); said constraint selection module being able to convert by mapping (516) values in a processor of the target computer associated with the series of four-dimensional trajectories based on the determined set of constraints (56, 58, 156, 170). 256, 258); said constraint selection module being able to estimate (520) additional values in the processor for the purpose based on the mapped values, and able to repeat the determination, mapping and estimation steps (514, 516, 520) until a value converted by goal mapping for a determined final set of constraints (170, 174) exceeds a predetermined threshold; and the computer further comprising an update module (314, 414) coupled to the constraint selection module (316, 416) and the trajectory predictor (310, 410) and adapted to obtain data relating to a four-dimensional trajectory 50, 52) calculated by the predictor (310, 410) of trajectories after each repetition has been completed by the constraint selection module (316, 416), a trajectory (52, 54) of the aircraft being predicted on the basis of the final set of constraints determined (170, 174)
    [9" id="c-fr-0009]
    The path refining system (128, 138, 300, 400) of claim 8, wherein the constraint selection module (316, 416) further determines the set of constraints (56, 58, 156, 170). , 256, 258) from a Gaussian distribution over a series of observed and unobserved objective function values.
    [10" id="c-fr-0010]
    A trajectory refining system (128, 138, 300, 400) according to claim 8, wherein the trajectory predictor (310, 410) is integrated into an aircraft flight management system (10, 11). ) and the constraint selection module (316, 416) is integrated in a system on the ground or in the aircraft.
    [11" id="c-fr-0011]
    The path refining system (128, 138, 300, 400) of claim 8, wherein the trajectory predictor (310, 410) and the constraint selection module (316, 416) are both integrated in a ground system.
    [12" id="c-fr-0012]
    12. The system for refining (128, 138, 300, 400) paths according to claim 8, wherein the predetermined threshold is a total time or a total number of cycles to be devoted to the calculation of a refined trajectory.
    [13" id="c-fr-0013]
    A trajectory refining system (128, 138, 300, 400) according to claim 8, wherein the constraint selection module (316, 416) is adapted to select the set of constraints (56, 58, 156, 170, 256, 258) to refine a trajectory (52, 54) of an aircraft (10, 11) to perform a path-lengthening maneuver during which the aircraft speed and the duration of the trip are modified to limit as far as possible an objective function relating to the reduction of fuel consumption or the limitation of operating costs.
    [14" id="c-fr-0014]
    A trajectory refining system (128, 138, 300, 400) according to claim 8, wherein the constraint selection module (316, 416) is adapted to select the set of constraints (56, 58, 156, 170, 256, 258) for refining a series of trajectories (52, 54) for coordinating arrival times of multiple aircraft in a fleet.
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    FR3038999B1|2020-11-20|
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    US20170018192A1|2017-01-19|
    GB2546842A|2017-08-02|
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    引用文献:
    公开号 | 申请日 | 公开日 | 申请人 | 专利标题
    US6463383B1|1999-04-16|2002-10-08|R. Michael Baiada|Method and system for aircraft flow management by airlines/aviation authorities|
    US20140018980A1|2012-07-12|2014-01-16|General Electric Company|Systems and methods for flight management|
    US6789011B2|1999-04-16|2004-09-07|R. Michael Baiada|Method and system for allocating aircraft arrival/departure slot times|
    US6873903B2|2001-09-07|2005-03-29|R. Michael Baiada|Method and system for tracking and prediction of aircraft trajectories|
    US7248963B2|2003-03-25|2007-07-24|Baiada R Michael|Method and system for aircraft flow management|
    AT445143T|2003-08-18|2009-10-15|Saab Ab|PASSIVE TERRAIN AVIGATION|
    KR101193115B1|2011-10-07|2012-10-19|한국항공우주산업 주식회사|Three dimention digital map system|
    US8798898B2|2011-10-31|2014-08-05|General Electric Company|Methods and systems for inferring aircraft parameters|
    US8977484B1|2013-08-22|2015-03-10|The Boeing Company|Using aircraft trajectory data to infer aircraft intent|EP2889579B1|2013-12-31|2018-02-14|The Boeing Company|System and method for defining and predicting aircraft trajectories|
    FR3040801B1|2015-09-09|2017-08-25|Thales Sa|OPTIMIZING THE TRACK OF AN AIRCRAFT|
    US9864368B2|2016-02-08|2018-01-09|Honeywell International Inc.|Methods and apparatus for global optimization of vertical trajectory for an air route|
    US10444763B2|2016-03-21|2019-10-15|Ford Global Technologies, Llc|Systems, methods, and devices for fusion of predicted path attributes and drive history|
    US10262546B2|2016-04-13|2019-04-16|Ge Aviation Systems Llc|Aircraft navigation using exponential map|
    US10228692B2|2017-03-27|2019-03-12|Gulfstream Aerospace Corporation|Aircraft flight envelope protection and recovery autopilot|
    US11017678B2|2017-09-22|2021-05-25|Vianair Inc.|Terminal and en-route airspace operations based on dynamic routes|
    JP6677774B2|2018-08-20|2020-04-08|Ｈａｐｓモバイル株式会社|Control device, program, control method, and flying object|
    CN111813144B|2020-06-11|2022-02-18|南京航空航天大学|Multi-unmanned aerial vehicle collaborative route planning method based on improved flocks of sheep algorithm|
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    优先权:
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    US14/801,494|US10269253B2|2015-07-16|2015-07-16|System and method of refining trajectories for aircraft|
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