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  • 专利说明
  • 权利要求
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    • 专利摘要:
    A method (200) for operating a vehicle in a manner that minimizes the cost of movement from an origin to a destination, which includes finding input information from a flight management system that limits as much as possible the direct operating costs. The method uses an energy regime approximation.
    公开号:FR3053458A1
    申请号:FR1755992
    申请日:2017-06-29
    公开日:2018-01-05
    发明作者:Reza Ghaemi；Eric Richard Westervelt；Mark Lawrence Darnell
    申请人:General Electric Co；
    IPC主号:
    专利说明:

    ® FRENCH REPUBLIC
    NATIONAL INSTITUTE OF INDUSTRIAL PROPERTY © Publication number: 3,053,458 (to be used only for reproduction orders) (© National registration number: 17 55992
    COURBEVOIE © Int Cl 8 : G 01 C 21/00 (2017.01), B 64 D 43/00, G 06 F 17/00
    A1 PATENT APPLICATION
    (§) Date of filing: 29.06.17. © Applicant (s): GENERAL ELECTRIC COMPANY— © Priority: 29.06.16 US 15196892. US. @ Inventor (s): GHAEMI REZA, WESTERVELT ERIC RICHARD and DARNELL MARK LAWRENCE. (43) Date of public availability of the request: 05.01.18 Bulletin 18/01. ©) List of documents cited in the report preliminary research: The latter was not established on the date of publication of the request. (© References to other national documents @ Holder (s): GENERAL ELECTRIC COMPANY. related: ©) Extension request (s): @ Agent (s): CASALONGA & ASSOCIES.
    (M) GUIDANCE SYSTEM AND METHOD BASED ON ENERGY STATE ESTIMATE.
    Method (200) for operating a vehicle in a manner that minimizes the cost of traveling from an origin to a destination, which includes finding the input information of a flight management system which minimize direct operating costs. The method uses an energy regime approximation.
    FR 3 053 458 - A1

    Guidance system and method based on an estimate of an energy state
    The field of the invention relates generally to flight management, and the invention relates more particularly to systems, devices and an operating mode for flight management, and to applications thereof.
    Since fuel costs account for a large share of operating costs in commercial aviation, fuel efficiency and fuel savings drive research into aircraft design and operation. We are mainly interested in fuel-saving technologies: aircraft and engine design, piloting design and flight path planning (what is called flight guidance).
    Current flight management systems (SGV) determine constant climb, cruise and descent speeds and a constant cruise altitude to minimize direct operating costs taking into account the take-off weight and radius. action and in the assumption of a constant thrust for the ascent and the thrust at idle for the descent. These simplifying assumptions have been applied to implement practical systems, but the simplifications give suboptimal performance and compromise energy savings.
    What is needed are systems and methods which overcome the problem of optimizing for all flight phases without simplifying assumptions and using digital methods to make the guidance tend towards the optimum.
    According to a first aspect, the problem of optimization is reformulated for in-flight activity without simplifying assumptions and using modern digital methods to tend the guidance towards the optimum. In one embodiment, a method for optimizing the guidance of a vehicle in order to limit the direct operating costs of a given mission as much as possible may include obtaining a mathematical model of the movement of a vehicle; the elimination of fast dynamic regime variables in the mathematical model; obtaining a reduced order mathematical model of vehicle movement in the form of a set of algebraic differential equations which represent slow dynamic regimes and which includes mass as a slow state variable; determining operating points in quasi-steady state in a flight envelope for the given mission by solving the reduced order model for thrust, drag and fuel flow at uniform energy intervals; the creation of a register of points of operation in quasi stable regime with energy as independent input variable and thrust, drag and fuel flow as dependent output variables; the selection of speed as a control variable and the use of optimal control methods to define a Hamiltonian function as direct operating cost per unit of energy; at uniform energy intervals, the use of a numerical method to determine the speed which minimizes the Hamiltonian function for fixed energy per interval; the construction of an optimal path at quasi-stable speed-energy on the basis of the minimized Hamiltonian function and of a path with corresponding velocity-altitude regime derived therefrom using equations of motion of reduced order; determining an optimal angle of flight path to start from the course at velocity-altitude regime and arrive at a given targeted cruising regime; an ascending vertical integration from the cruising speed in question using an approximate mass and the optimal angle of flight trajectory previously determined in order to define a starting point where the speed course crosses the speed course velocity-altitude; and the creation of an optimal guidance solution which includes the course at velocity-altitude regime from a predetermined initial position to the point of departure and the course at regime from the point of departure to the target cruising regime, which is determined by the target cruising speed and altitude.
    In a first embodiment, a system is proposed. In yet another exemplary embodiment, a hardware medium can implement at least some aspects of the methods according to the present invention.
    The present invention aims to carry out a method for optimizing vehicle guidance in order to limit as much as possible the direct operating cost of a given mission. The method comprises a step of obtaining a mathematical model of a movement of a vehicle; a step of elimination of fast dynamic regime variables in the mathematical model; a step of derivation of a reduced order mathematical model of the movement of the vehicle in the form of a set of algebraic differential equations which represent slow dynamic regimes and includes mass as variable in slow regime: a point determination step operating in near steady state in a flight envelope for the given mission by solving the reduced order model for thrust, drag and fuel flow at uniform energy intervals; a step of creating a register of the points of operation in quasi stable regime with the energy as independent input variable and the thrust, the drag and the fuel flow as dependent output variables; a step of selecting speed as a control variable and using optimal control methods in order to define a Hamiltonian function as direct operating cost per unit of energy; a speed determining step which minimizes the Hamiltonian function for fixed energy per interval at uniform energy intervals, using a numerical method; a step of construction of an optimal path at quasi-stable speed-energy on the basis of the minimized Hamiltonian function and of a path at corresponding velocity-altitude regime derived therefrom using order motion equations reduced; a step of determining the optimum angle of flight path for starting from the course at velocity-altitude regime and arriving at a given targeted cruising regime; a step of upward vertical integration from the cruising speed aimed using an approximate mass and the optimal angle of flight trajectory previously determined in order to define a starting point where the speed course crosses the course at velocity regime; and a step of creating an optimal guidance solution which includes the course at velocity-altitude regime from a predetermined initial position to the starting point and the regime course from the starting point to the cruising speed in question , which is determined by the target cruising speed and altitude.
    For example, the movement of the vehicle represented by the mathematical model is defined only in a longitudinal plane by zeroing lateral and directional movement variables.
    In one embodiment, the mathematical model of vehicle movement includes an accurate model of the vehicle's aerodynamic forces and moments and mass properties, and a physical-based model of the engines that represent the thrust forces and the fuel flow rate.
    The fast dynamic regime variables can be eliminated by setting the pitch moment and the vertical forces to equilibrium values.
    In one embodiment, the thrust, drag, and fuel flow for the reduced order model are a function of the altitude, velocity, mass, and efficiency of the engines.
    In one embodiment, the optimal angle of flight trajectory at which one starts from the course at velocity-altitude regime to reach the altitude and the cruising velocity is a maximum or a minimum, depending on whether the targeted cruising regime is respectively below or above the course at velocity-altitude regime.
    In one embodiment, the target cruising speed is based on a target cruising speed and altitude.
    In one embodiment, the energy includes the altitude, velocity, mass and efficiency parameters of the motors.
    In one embodiment, the Hamiltonian function has velocity as the input control variable and energy as the independent variable.
    The invention will be better understood on detailed study of a few embodiments taken by way of nonlimiting examples and illustrated by the appended drawings in which:
    Figure 1 shows a schematic perspective view of an example of a flight management system for guidance and navigation according to one embodiment;
    FIGS. 2A and 2B illustrate an example flow diagram of a process according to an embodiment;
    FIGS. 3A and 3B show a process test by energy regime approximations (ARE) according to an exemplary embodiment;
    FIG. 4 illustrates an example, according to one embodiment, of comparison of the fuel saved in the form of an average of the ARE process compared to a constant speed process according to the prior art; and Figure 5 shows a block diagram of a system or device capable of executing the processes described here, according to an exemplary embodiment.
    Unless the context obviously requires otherwise, articles defined and undefined in the singular cover references to the plural.
    Most flight management systems currently in use globally determine constant climb, cruise and descent speeds and a constant cruise altitude to minimize direct operating cost given the take-off weight and radius of action and in the assumption of a constant thrust for the ascent and of the thrust at idle for the descent. Many simplifying assumptions are applied in prior art systems.
    Considering Figure 1, there is shown an illustration of an exemplary system for the guidance and navigation of vehicles such as aircraft. The flight management system 10 is coupled to an automatic pilot 20 which is used to control the operation of the vehicle 30. A multitude of sensors 40 are used to measure certain properties of the vehicle and / or the environmental and operating parameters.
    The flight management system 10 comprises a guidance section 50, a steering module 60 and a navigation unit 70. The data from the sensors is supplied to a navigation unit 70, which feeds the totalization block 55 of the management system flight which also receives, from the guide section 50, input signals for feedback piloting of the vehicle 30. In one example, the guide section 50 of the present system optimizes the open loop control and limits the costs of direct exploitation. The output signals from the summation section 55 are supplied to the travel control 65. The travel control 65 provides traditional feedback and control rules.
    The output of the flight management system 10 is coupled to the autopilot and auto-joystick section 70. More specifically, the output signals of the travel control 65 are input signals from the flight unit. totalization 75, which also receives data from the sensors 40. The totalized signals delivered by the autopilot totalization unit 75 serve as input signals for the rotation control 80, which are then delivered to the actuators which operate aerodynamic control surfaces to control the vehicle 30.
    According to one embodiment, the present invention reformulates the optimization problem for uphill flight (1) without simplifying assumptions and (2) using modern digital methods to achieve guidance closer to optimal performance.
    This description considers optimal performance for uphill flight, namely, for a given altitude and a cruising speed, the determination of the optimal flight trajectory which begins at a given initial altitude, speed and mass and burns less of fuel compared to any other route that begins under the same initial conditions and ends at the same altitude and cruising speed - and takes place over the same horizontal distance.
    The process developed and detailed here is based on an approach using regime or energy state approximations (ARE). The energy regime approximation (ARE) method is derived from a method based on the Pontryaguine minimum principle approach where the specific energy is the independent variable and other regimes are considered to be a function of specific energy. The approach is used by Erzberger and Bryson, where the Pontryaguine minimum principle is used to approximate the optimal flight profile. The approach described here uses, in certain embodiments, the ARE method to approach part of an optimal flight trajectory called here singular arc, over which the partial angle of a flight trajectory (nor the maximum angle nor the minimum angle of the flight path) is the optimal profile. In certain embodiments, the optimal complete climb profile includes: 1) an optimal arrival on the singular arc from an initial altitude and velocity, 2) a displacement on the singular arc, and 3) an optimal departure on the singular arc and an arrival at a target altitude and velocity.
    The formulation of the problem and the design of the process assumed that the fast dynamics are in steady state conditions and include only the slow dynamics, in order to reduce the order of the system. In this hypothesis, it follows that the set of differential equations of motion corresponding to fast dynamics becomes a set of algebraic differential equations (EDA) and allows a larger sampling interval. This establishes that the elements with slow dynamics are altitude, distance, mass and energy. The allowable setting is defined by a number of variables including: limitations in airspace, vehicle performance, limits due to the quality of the journey, and piloting can also be limited by autopilot and auto -controller.
    Figure 2 is a flowchart illustrating a process in an exemplary embodiment of the present invention. During operation 205, a mathematical model of movement is obtained for a vehicle. The vehicle can be a fixed-wing aircraft where the mathematical model is a representation of the performance characteristics for the aircraft, including the engine configurations specific to it. During operation 210, fast dynamic regime variables are eliminated from the mathematical model. Proceeding to operation 215, a reduced order mathematical model of the movement of the vehicle is derived in the form of a set of algebraic differential equations. The set of algebraic differential equations represents slow dynamic regimes and includes mass as a slow regime variable.
    During operation 220, operating points in quasi-steady state within a flight domain are determined for the given mission of the aircraft by solving the reduced order model for a certain number of parameters. Parameters can include thrust, drag, and fuel flow at uniform energy intervals. Going to operation 225, a register of points ίο of operation in quasi-stable regime can be created with energy as independent input variable and thrust, drag and fuel flow as dependent output variables. During operation 230 of FIG. 2A, the speed is chosen as the control variable and optimal control methods are used to define a Hamiltonian function as direct operating cost per unit of energy.
    Process 200 continues as illustrated in Figure 2B. In Figure 2B, operation 235 includes the use, at uniform energy intervals, of a numerical method to determine the speed which minimizes the Hamiltonian function for fixed energy by interval. On the basis of the minimized Hamiltonian function determined during operation 235, an optimal course at quasi-stable speed-energy is constructed during operation 240. A course with corresponding velocity-altitude regime is derived from the optimal course at speed- quasi-stable energy using reduced order equations of motion. This course at velocity-altitude regime is called here the singular arc.
    During operation 245, an optimal angle of flight trajectory is determined for starting from the course at velocity-altitude regime (that is to say from the singular arc) and arriving at a given targeted cruising regime. Process 250 includes an upward vertical integration from the cruising speed in question using an approximate mass for the aircraft and the optimal angle of flight path previously determined in order to define a starting point where the course at speed crosses the course at speed-altitude regime. Moving on to operation 255, process 200 concludes with the creation of an optimal guidance solution which includes the course at velocity-altitude regime from a predetermined initial position to the starting point and the course at regime from the point from departure to speed at the target cruise point, which is determined by the target cruise speed and altitude.
    Considering Figure 3A, the simulated results of a single ARE test are illustrated. The graph 305 is a graph of the altitude as a function of the distance, comprising plots for a course 325 according to the prior art and a course according to Erzberger 330, each arriving at the same target altitude. The graph 310 is a graph of the speed as a function of the distance, comprising plots for the course 340 according to the prior art and the course 335 according to Erzberger, each arriving at the same target speed. Graph 315 shows the total fuel burned as a function of the distance traveled for route 345 according to the prior art as well as route 350 according to Erzberger, while Graph 320 shows by plot 360 the fuel saved in relation to the distance traveled for the ARE test. Some of the parameters and their corresponding values for the single ARE test in Figure 3 are listed in Table 365. Figure 3B is a table listing some of the parameters and their corresponding values used in the test example in Figure 3A. Here, some advantages of the ARE method are that the processing in the present invention is very efficient by computer and less sensitive to unsmoothed data.
    As shown in Graph 400 in Figure 4, we have simulated the ARE process described and we have found that it gives, in a simulation example, an average fuel savings of 0.66%. This result was evaluated during approximately 180 simulation tests with five different routes with different radius of action which included a variable take-off weight and mass, as well as a different altitude and a number of Mach (cruise) terminals. Such a percentage of fuel savings, considered on the many flights that take place, can represent huge savings for airlines. Fuel savings are just one example of a measure that can be used to quantify a benefit and improvement achievable using the methods and systems described here.
    The design procedure consists of three stages: modeling the installation, designing the optimization routine, and estimating the value of the design. First, a non-linear, time-varying model of the vehicle such as an aircraft is developed. The equations of motion for a rigid body with 6 degrees of freedom (6 DDL) are derived in terms of aerodynamic and thrust forces and moments and mass properties of the vehicle. As the focus is on performance optimization, the equations that define the aerodynamic and thrust forces are not derived, so they are given. For this analysis, the vehicle is an aircraft that employs aircraft engines.
    To simplify the optimization problem, the equations of the system define the movement of the vehicle in the longitudinal plane. There is no loss of generality with this approach, since the regular lateral movement of all stable fixed wing airplanes is the same, so the lateral performances can be studied independently. The movement in the longitudinal plane is defined by zeroing the derivatives of the lateral and directional movement variables. Furthermore, as the vehicle operates in the atmosphere at subsonic speeds, it is assumed to be a flat Earth, which does not rotate.
    The equations thus obtained consist of fast and slow dynamics of the vehicle. Assuming that fast dynamics have a negligible effect on fuel economy, fast dynamics are eliminated and this results in a set of reduced order algebraic differential equations which represent only the slow dynamics of the vehicle. This approach allows a larger time step for digital integration compared to the time increment required if fast dynamics are included. With the reduced order model, the independent variable is led to spend time at altitude.
    In the second step, a Hamiltonian function is created as a function of energy and velocity and is parameterized by cruising velocity and fuel flow.
    To validate the process as a third step, the fuel burned using guidance by the SGV according to the prior art is compared with the fuel burned using improved, more optimal control. The 6 DDL model is used to measure the performance obtained by following each guidance solution. To assess fuel savings, consider the difference in horizontal distance where the top of the climb occurs for each guidance solution.
    At each energy level, the method by the golden ratio method is used to find the speed which minimizes the Hamiltonian function as a function of energy. As the solver does not calculate derivatives during the search for a minimum, this process is more efficient and robust. However, in some cases, the piloting may not be optimal due to the approximation of the regime. In this way, the fuel savings made by each process are compared to determine a possible penalty introduced by TARE.
    One embodiment of the present invention is to operate an air vehicle in a manner which greatly limits the cost of flying from an origin to a destination. It is therefore necessary to find the input signal supplied to the flight management system which limits the direct operating cost (CED). The approach described here is based on the calculation of variations and the principle of Pontryaguine minimum. An example describes how an optimal driving theory is applied to formulate and solve the problem of performance optimization. According to this process, the equations of the system are appended to a cost function by Lagrange multipliers. Thus, an example begins by deriving the equations of motion of the system which are then used to formulate the problem.
    In some ways, as the fuel burns and the weight of the airplane changes, the best cruising altitude for optimal operating efficiencies also changes. Unfortunately, operating airplanes in this manner is usually not possible due to the limited capabilities of the air traffic management system. On the other hand, to address the problem of aircraft spacing and traffic flow management, aircraft are assigned constant cruising altitudes as a convenient compromise between performance and safety. In this way, the instrument flight rules with which commercial airplane operators must comply require an ascent from a departure airport to a constant assigned cruising altitude, at which the airplane operates until he must descend to arrive at the destination airport. The mission of commercial transport is therefore characterized by flight phases including ascent, cruise and descent. More specifically, a flight can be broken down into a series of successive phases which include takeoff, climb, cruise, descent, approach and landing. The movement of the airplane on the ground to and from the runways is called taxiing and ground maneuvers.
    Piloting the airplane during take-off, approach and landing is usually dictated by traffic flow and security in the terminal area, which requires precise piloting in accordance with specific procedures. During these phases, the pilot can in fact practically not choose how to fly the airplane to limit a cost such as fuel consumption. Consequently, performance optimization is only possible for the climb, cruise and descent phases, where the pilot has more latitude to fly according to instrument flight rules (IVR).
    Instrument flight rules can be expressed as follows in the form of constraints affecting the engine speed and vehicle control variables:
    Climb - The aircraft rises from the initial point of the climb phase, the altitude increases monotonously until it reaches the assigned altitude and cruising speed.
    Cruise - the aircraft is level flying at the assigned assigned constant cruising altitude. The initial cruising speed is the value listed in the FAA approved (authorized) flight plan.
    Descent - The aircraft descends, the altitude decreases monotonously until it reaches the initial point of the approach phase.
    A regular cruise at a constant altitude is not an optimum, since the weight of the aircraft changes as a result of fuel consumption. However, under instrument flight rules, a constant assigned cruising altitude is the primary means of spacing aircraft and planning traffic flow.
    The ultimate objective is an optimization system which determines the piloting history which gives the minimum direct operating cost (CED) of all the climb, cruise and descent phases of the mission while respecting the IVR and constraints affecting vehicle performance. However, the cost of the cruise phase depends on the uphill and downhill performance and vice versa. Thus, the interdependence of climb, cruise and descent performance must be taken into account to determine the optimal piloting for all of the mission's climb, cruise and descent phases. Thus, optimal piloting for the mission cannot be determined by optimizing performance independently during each phase.
    Thus, the problem of optimizing the climb is formulated on the assumption that the optimal altitude and cruising speed are determined a priori, so the initial cruising regime is applied as a terminal constraint at the time of the climb. At first glance, this approach may seem contradictory, since the optimal altitude and cruising speed are a function of the weight at the start of the cruising phase, so the optimal cruising speed depends on the amount of fuel burned during the climb. . Of course, with the problem thus formulated, an iterative process is needed to find the optimal combination of uphill and cruise piloting. However, the cruise depends first on the climb. Thus, the iterative process begins with a prediction of the climb until a first estimate of the cruising altitude. The initial cruising mass is then determined by the fuel consumption during the first climb prediction.
    At the end, once the optimal combination of uphill and cruise control is determined by an iterative convergence on the mass while cruising, the optimization of the climb is expressed with the cruising regime as terminal constraint. Therefore, the approach presented here to define the optimization of the climb is valid. In fact, the climb optimization algorithm developed is integrated with the method to find the optimal piloting when cruising - as well as with the method to find the optimal piloting when descending - in order to determine the optimal piloting for the entire cycle. flight uphill, cruise and descent.
    For the energy regime approximation (ARE) process, here are several details: it is derived from the Erzberger process based on the principle of Pontryaguine minimum; speed and thrust (throttle angle) control certain variables; the independent variable spends time on specific energy; the golden ratio method finds the minimum.
    The formulation and the conception of the problem for the process have the following peculiarities: fast dynamics supposed to have a negligible effect on the fuel consumption; the set of differential equations of motion becomes a set of differential algebraic equations; the possibility of a larger sampling interval. Eligible pilotage, defined by: Airspace limitations; Vehicle performance; Trip quality limits, Piloting, can also be limited by Autopilot and Auto-joystick.
    Some advantages of the energy regime approximation method described here could include very high computing efficiency and less sensitivity to unsmoothed data.
    The present system can reduce the fuel consumption of fixed-wing aircraft by calculating the flight history and a speed journey which limits the fuel consumption as much as possible. According to an exemplary embodiment, the piloting history is generated using the following steps: 1- Derive a model of the aircraft and the engine. 2- Check that the model agrees with the experimental data. 3- Project the model on the vertical plane so that it defines a movement only in the longitudinal plane. 4- Eliminate fast dynamics by establishing the moment of pitch and vertical forces at equilibrium values. 5 Form the reduced order model in the form of algebraic differential equations, algebraic equations replacing the fast dynamics. 6- Using an offline process, determine stable operating points within the flight envelope by solving the reduced order model (equations of motion) for thrust, drag and fuel flow taking into account uniform intervals of altitude, velocity, mass and wear of the engines and save the results on a table where the altitude, velocity, mass and wear of the engines are the variables independent and thrust, drag and fuel flow are the output. Choose the velocity as input data and build the Hamiltonian function as a function of the input data (velocity) and the independent variable (specific energy). 7- Use energy as an independent variable. 8- For a fixed energy, use the golden ratio method to find the velocity that limits the Hamiltonian function.
    9- Construct the velocity-energy profile and therefore the velocity-altitude profile, and call it singular arc. 10- Determine the optimal flight path angle to start from the singular angle and reach the target speed. The flight path angle is maximum or minimum if the target speed is below or above the singular arc. 11- Perform an ascending vertical integration from the target speed and an approximate mass with the optimal angle of flight path determined during step 10 until it strikes the singular arc, the point d intersection of the two routes on the velocity-altitude profile being called the starting point. 12- The optimal trajectory slides on the singular arc until it reaches the starting point; from there, the optimal trajectory slides along the route determined during step 11 until it reaches the target speed, which is determined by the target speed and the target velocity.
    The present system allows the creation of flight paths which minimize fuel consumption. The performances of the present method, compared to other methods according to the prior art, are superior in that they result in lower fuel consumption. In one example, the technical effects of the present method offer the advantages below compared to other methods: 1- requires little IT resources, which makes it compatible with real-time implementation; 2- unifies the engine and aircraft model to increase fuel savings; 3- moderate the assumption that the speed is constant throughout the cruising and descending phases to achieve fuel savings; 4- can include the wind profile in the optimization; 5- includes engine wear as a parameter so that the flight path can be formed to achieve maximum fuel savings given the actual stage of engine wear; 6- includes mass as a state in the optimization and therefore moderates the postulate that the mass is constant during the flight.
    In order to demonstrate the concept, a simulation was carried out involving the development of a detailed model of narrow-body commercial aircraft engines. The creation of the optimal flight path for the aircraft is detailed here with some changes from the origin. The flight path created using the detailed model was traveled using the simulation and the fuel consumption was measured during the climb phase. A fuel saving of 1% was observed during the simulated ascent phase, which is considered a high figure for the industry.
    Although the present system and method have been described with reference to an aircraft, other vehicles such as ships, trains and self-propelled vehicles may employ the techniques.
    The exemplary embodiments of methods, systems and devices for flight management are not limited to the specific embodiments described here; on the contrary, elements of systems and / or steps of the processes can be used independently and separately from other elements and / or steps described here. For example, the methods can also be used in combination with other flight management systems and their implementation is not limited to the systems and methods described here.
    Figure 5 is a block diagram illustrating a device 500 according to an example of some embodiments. The device 500 may include a computing device and may execute instructions from a program to perform any of the functions described here. The device 500 may include the implementation of a server, a specialized device activated by a processor, and other systems, including systems deployed in the aircraft and, in certain embodiments, systems deployed in a ground control center or facility. The device 500 may, in certain embodiments, include other elements, not shown.
    The device 500 comprises a processor 505 cooperating with a communication device 515, a data storage device 530, one or more input devices 510, one or more output devices 520 and a memory 525. The communication device 515 can facilitate communication with external devices such as a client communicating data, or a data storage device. The 510 input device (s) may / may include, for example, a keyboard, keypad, mouse or other pointing device, microphone, button or switch, infrared (IR) port, station and / or a touch screen. The input device (s) 510 can / can be used, for example, to enter information into the device 500. The output device (s) 520 can / can include, for example, a display (p. e.g. display screen), speaker and / or printer.
    The data storage device 530 can include any suitable permanent storage device, including combinations of magnetic storage devices (eg magnetic tape, hard drives and flash memory), storage devices semiconductors, optical storage devices, read only memory devices (ROM), random access memory (RAM), archive class memory (SCM) and any other fast access memory.
    Services 535, a server 540 and an application 545 may include instructions of a program executed by the processor 505 to cause the device 500 to perform any one or more of the processes described here, including, without limitation, the process 200. The embodiments are not limited to the execution of these processes by a single device.
    Data 550 (cached or an entire database) can be stored in volatile memory such as memory 525. The data storage device 530 can also store data and other program code to provide additional functionality and / or which are necessary for the operation of the device 500, such as device drivers, operating system files, etc.
    List of landmarks
    10 Flight management system 20 Automatic pilot 5 30 Vehicle 40 Sensors 50 Guide section 55 Totalization block 60 Steering module 10 65 Travel command 70 Navigation unit 75 Totalization block 80 Rotation control 200 Process 15 205 Information flow 210 Information flow 215 Information flow 220 Information flow 225 Information flow 20 230 Information flow 235 Information flow 240 Information flow 245 Information flow 250 Information flow 25 255 Information flow 305 Graphic 310 Graphic 315 Graphic 320 Graphic
    325 Legend according to the prior art
    330 Legend according to Erzberger
    335 Erzberger route
    340 Route according to the prior art
    345 Legend according to the prior art
    350 Legend according to Erzberger
    360 data plotting
    400 Graphics
    500 System
    505 Processor
    510 Input device (s)
    515 Communication device
    520 Output device (s)
    525 Memory
    530 Storage device
    535 Services
    540 Server
    545 Application
    550 Data
    权利要求:
    Claims (9)
    [1" id="c-fr-0001]
    1. A method for optimizing the guidance of a vehicle in order to limit as much as possible the direct operating costs of a given mission, the method comprising:
    obtaining a mathematical model of the movement of a vehicle (205); the elimination of fast dynamic regime variables in the mathematical model (210);
    obtaining a reduced order mathematical model of vehicle movement in the form of a set of algebraic differential equations which represent slow dynamic regimes and which includes mass as a slow state variable (215);
    determining quasi steady state operating points in a flight envelope for the given mission by solving the reduced order model for thrust, drag and fuel flow at uniform energy intervals (220);
    creation of a register of points of operation in quasi-stable regime with energy as the independent input variable and the thrust, drag and fuel flow as dependent output variables (225);
    selecting speed as a control variable and using optimal control methods to define a Hamiltonian function as direct operating cost per unit of energy (230);
    at uniform energy intervals, using a numerical method to determine the speed which minimizes the Hamiltonian function for fixed energy per interval (235);
    the construction of an optimal path at quasi-stable speed-energy on the basis of the minimized Hamiltonian function and of a path with corresponding velocity-altitude regime derived therefrom using equations of motion of reduced order ( 240);
    determining an optimal angle of flight path to start from the course at velocity-altitude regime and arrive at a given target cruising regime (245);
    an ascending vertical integration from the cruising speed in question using an approximate mass and the optimal angle of flight trajectory previously determined in order to define a starting point where the speed course crosses the speed course velocity (250); and the creation of an optimal guidance solution which comprises the course at velocity-altitude regime from a predetermined initial position to the starting point and the course at regime from the starting point to the target cruising regime, which is determined by the target cruising speed and altitude (255).
    [2" id="c-fr-0002]
    2. Method according to claim 1, in which the movement of the vehicle represented by the mathematical model is defined only in a longitudinal plane by setting variables of lateral and directional movement to zero.
    [3" id="c-fr-0003]
    Method according to claim 1, in which the mathematical model of the movement of the vehicle comprises an accurate model of the aerodynamic forces of the vehicle and of the moments and mass properties and a model based on the physical motors which represent the thrust forces and the fuel flow.
    [4" id="c-fr-0004]
    4. Method according to claim 1, in which the variables of fast dynamic regimes can be eliminated by fixing the pitch moment and the vertical forces at equilibrium values.
    [5" id="c-fr-0005]
    5. The method of claim 1, wherein the thrust, drag and fuel flow for the reduced order model are a function of the altitude, velocity, mass and efficiency of the engines.
    [6" id="c-fr-0006]
    6. Method according to claim 1, in which the optimal angle of flight trajectory at which one starts from the course at speed
    5 velocity-altitude to reach the altitude and the cruising velocity is a maximum or a minimum, depending on whether the cruising speed in question is respectively below or above the course at velocity-altitude regime.
    [7" id="c-fr-0007]
    7. The method of claim 1, wherein the diet
    10 target cruise is based on a target cruise speed and altitude.
    [8" id="c-fr-0008]
    8. The method of claim 1, wherein the energy comprises the parameters of altitude, velocity, mass and efficiency of the motors.
    15
    [0009]
    9. The method of claim 1, wherein the Hamiltonian function has velocity as an input control variable and energy as an independent variable.
    1/7 ο
    CO <J
    2/7
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