Unmanned gliding aircraft trajectory optimization method and device, and storage medium

Abstract

The invention discloses an unmanned gliding aircraft trajectory optimization method and device and a storage medium, and the method comprises the steps: introducing an energy loss rate and maneuvering efficiency based on a mass center motion equation of an unmanned gliding aircraft, the description units are used for respectively describing the loss degree of the mechanical energy of the unmanned gliding aircraft and the utilization degree of the unmanned gliding aircraft on the maneuvering capability during gliding flight; establishing a comprehensive optimization function by combining the energy loss and the maneuvering efficiency of the unmanned gliding aircraft during gliding; based on the comprehensive optimization function, taking the minimum value as a performance index to optimize the trajectory of the unmanned gliding aircraft; aiming at the index construction problem in trajectory optimization, a new concept of a comprehensive optimization function is provided, the function fully considers kinetic energy loss and maneuvering efficiency of the unmanned gliding aircraft during gliding flight, a height weighting function is introduced, and a new thought and a new method are provided for gliding trajectory design of gliding of the unmanned gliding aircraft.

Description

Technical Field

[0001] The present invention relates to the technical field of aircraft, and particularly to a method, device and storage medium for optimizing the trajectory of an unmanned gliding aircraft. Background Technique

[0002] Unmanned gliding aircraft have advantages such as low cost, low loss, and zero casualties, and can be widely used in military and civilian fields such as battlefield reconnaissance, long-range strikes, and environmental monitoring. In order to improve the performance of unmanned gliding aircraft such as endurance and payload, their trajectories can be optimized. At present, such optimization indicators are relatively one-sided, such as the farthest gliding distance, the shortest gliding time, the minimum gliding energy consumption, etc., or these indicators are simply combined, making it difficult to reflect the comprehensive performance of the aircraft during gliding flight. Summary of the Invention

[0003] The main purpose of the present invention is to provide a method, device and storage medium for optimizing the trajectory of an unmanned gliding aircraft, aiming to solve the existing technical problems.

[0004] To achieve the above object, the present invention provides a method for optimizing the trajectory of an unmanned gliding aircraft, including:

[0005] Based on the centroid motion equation of the unmanned gliding aircraft, the energy loss rate and the maneuver efficiency are introduced to respectively describe the loss degree of the mechanical energy of the unmanned gliding aircraft and the utilization degree of the maneuverability of the unmanned gliding aircraft during gliding flight;

[0006] Combining the energy loss and the maneuver efficiency during the gliding of the unmanned gliding aircraft, a comprehensive optimization function is established;

[0007] Based on the comprehensive optimization function, taking its minimum value as the performance index to optimize the trajectory of the unmanned gliding aircraft.

[0008] Further, the energy loss rate where V Max and C xMax respectively represent the maximum flight speed of the aircraft and the maximum drag coefficient at this speed. Since the speed of the unmanned gliding aircraft during gliding will not exceed 1Ma, therefore, V / V Max can be approximately replaced by the current flight Mach number of the aircraft. Denote the maximum value of the drag coefficient C x as A, and rewrite the energy loss rate η E into the following form

[0009] Further, the maneuver efficiency

[0010] In the formula, n and n Maxrespectively represent the current normal overload and the maximum normal overload of the unmanned gliding aircraft, sign(x) is the sign function, α, α Max respectively represent the current angle of attack and the maximum angle of attack of the unmanned gliding aircraft, e -y is the weighting function, and y represents the current altitude of the aircraft.

[0011] Furthermore, the comprehensive optimization function η is used to describe the degree of energy loss and the degree of utilization of maneuverability of the unmanned gliding aircraft during gliding flight;

[0012] η = η E η M

[0013] Substituting equations (1) and (2) into the above formula, we can get:

[0014] Furthermore, minimizing the comprehensive optimization function is used as a performance index, as shown in the following formula,

[0015] The boundary condition constraints are considered as:

[0016]

[0017] In the formula, (x 0 , y 0 ), V 0 and θ 0 respectively represent the coordinates, speed and track angle of the unmanned gliding aircraft at the start of gliding, (x f , y f ), V fmin , θ fmin and θ fmax respectively represent the coordinates, minimum speed, minimum track angle and maximum track angle of the unmanned gliding aircraft when it reaches the end point. Due to the limited maneuverability of the unmanned gliding aircraft, therefore, during the gliding flight of the aircraft, the track angle and the angle of attack also need to satisfy the following constraint conditions:

[0018]

[0019] Furthermore, it also includes using the hp - adaptive Radau pseudospectral method to solve the trajectory optimization problem. The specific steps are as follows:

[0020] Divide the time domain and transform the sub - intervals to (-1, 1);

[0021] Use polynomial interpolation to approximate the control variables and state variables;

[0022] Discretize the performance index and constraint conditions;

[0023] Convert the original problem into a nonlinear programming problem;

[0024] Solve the non - linear programming problem using numerical optimization algorithms;

[0025] Check whether the constraint error meets the accuracy. If so, end the operation. If not, adjust the order of the interpolation function or the division of sub - intervals and restart.

[0026] An electronic device, the device includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, the computer program is configured to implement the steps of the unmanned glider trajectory optimization method as described above.

[0027] A storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the unmanned glider trajectory optimization method as described above.

[0028] The beneficial effects of the present invention are reflected in:

[0029] In view of the problem of index construction in trajectory optimization, the present invention proposes a new concept of a comprehensive optimization function. This function fully considers the kinetic energy loss and maneuver efficiency of an unmanned glider during gliding flight, and introduces a height - weighted function, providing new ideas and methods for the design of the gliding trajectory of an unmanned glider. Description of the Drawings

[0030] Figure 1 Schematic diagram of the unmanned glider trajectory optimization method of the present invention;

[0031] Figure 2 Schematic diagram of the hp - adaptive Radau pseudospectral method algorithm flow of the present invention;

[0032] Figure 3 Schematic diagram of the unmanned glider trajectory curve of the present invention;

[0033] Figure 4 Schematic diagram of the angle of attack curve of the unmanned glider of the present invention;

[0034] Figure 5 Schematic diagram of the speed curve of the unmanned glider of the present invention;

[0035] Figure 6 Schematic diagram of the track angle curve of the unmanned glider of the present invention;

[0036] Figure 7 Schematic diagram of the optimized trajectory curve of the unmanned glider of the present invention;

[0037] Figure 8 Schematic diagram of the optimized angle of attack curve of the unmanned glider of the present invention;

[0038] Figure 9 Schematic diagram of the optimized speed curve of the unmanned gliding aircraft of the present invention;

[0039] Figure 10 Schematic diagram of the optimized track angle curve of the unmanned gliding aircraft of the present invention. Specific implementation manner

[0040] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Without conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0041] Please refer to Figure 1 , the present invention provides a method for optimizing the trajectory of an unmanned gliding aircraft, including:

[0042] Based on the centroid motion equation of the unmanned gliding aircraft, the energy loss rate and the maneuver efficiency are introduced to respectively describe the loss degree of the mechanical energy of the unmanned gliding aircraft and the utilization degree of the maneuverability of the unmanned gliding aircraft during gliding flight;

[0043] Combining the energy loss and the maneuver efficiency during the gliding of the unmanned gliding aircraft, a comprehensive optimization function is established;

[0044] Based on the comprehensive optimization function, taking its minimum as the performance index to optimize the trajectory of the unmanned gliding aircraft.

[0045] Among them, considering the trajectory optimization problem of the unmanned gliding aircraft in the longitudinal plane, the motion equations of the unmanned gliding aircraft in the longitudinal plane can be expressed by the following formula

[0046]

[0047] In the formula, m is the mass of the aircraft, x, y, V, and θ are the flight distance, height, speed, and track angle of the aircraft respectively, and X and Y are the drag and lift forces received by the aircraft respectively.

[0048] The mechanical energy possessed by the aircraft can be expressed by formula (2)

[0049]

[0050] In the formula, E represents mechanical energy, m is the mass of the aircraft, and V and y are the flight speed and flight height of the aircraft respectively. Taking the derivative of formula (2) with respect to time t, the change rate of the energy of the unmanned gliding aircraft with respect to time can be obtained, as shown in formula (5)

[0051]

[0052] Substituting those in Equation (1), we get and into the above equation, we obtain

[0053]

[0054] It can be seen from Equation (6) that the energy change rate of the unmanned gliding aircraft is always negative and related to the product of the speed and the drag, which can be regarded as the power of the drag. To minimize the energy loss during the flight of the aircraft, the drag on the aircraft should be reduced as much as possible. However, a smaller drag will cause the flight speed of the aircraft to increase rapidly, which instead increases the energy loss. Therefore, it is necessary to comprehensively consider the energy loss problem during the gliding flight of the unmanned gliding aircraft.

[0055] The energy loss rate where V Max and C xMax respectively represent the maximum flight speed of the aircraft and the maximum drag coefficient at this speed. Since the speed of the unmanned gliding aircraft during gliding does not exceed 1 Ma, therefore, V / V Max can be approximately replaced by the current flight Mach number of the aircraft. Denote the maximum value of the drag coefficient C x as A. Rewrite the energy loss rate η E into the following form

[0056] The maneuver efficiency

[0057] In the formula, n and n Max respectively represent the current normal overload and the maximum normal overload of the unmanned gliding aircraft. sign(x) is the sign function, α, α Max respectively represent the current angle of attack and the maximum angle of attack of the unmanned gliding aircraft, e -y is the weighting function, and y represents the current altitude of the aircraft.

[0058] The comprehensive optimization function η is used to describe the degree of energy loss and the degree of utilization of maneuverability during the gliding flight of the unmanned gliding aircraft;

[0059] η = η E η M

[0060] Substituting Equations (7) and (8) into the above equation, we get:

[0061] Taking the minimum of the comprehensive optimization function as the performance index, as shown in the following equation

[0062] The boundary condition constraints are considered as follows:

[0063]

[0064] In the formula, (x 0 , y 0 ), V 0 and θ 0 respectively represent the coordinates, velocity and flight path angle of the unmanned glider at the start of gliding. (x f , y f ), V fmin , θ fmin and θ fmax respectively represent the coordinates, minimum velocity, minimum flight path angle and maximum flight path angle of the unmanned glider at the end point. Since the maneuverability of the unmanned glider is limited, therefore, during the gliding flight of the glider, the flight path angle and the angle of attack need to satisfy the following constraint conditions:

[0065]

[0066] The unmanned glider needs to satisfy the dynamic constraint formula (1) during the gliding flight. The constraint parameters in formulas (9) and (10) are shown in Table 1.

[0067] Table 1 Constraint parameters

[0068]

[0069]

[0070] Please refer to Figure 2 , and it also includes using the hp - adaptive Radau pseudospectral method to solve the trajectory optimization problem. The specific steps are as follows:

[0071] Divide the time domain and transform the sub - intervals to (-1, 1);

[0072] Use polynomial interpolation to approximate the control variables and state variables;

[0073] Discretize the performance index and constraint conditions;

[0074] Convert the original problem into a nonlinear programming problem;

[0075] Use a numerical optimization algorithm to solve the nonlinear programming problem;

[0076] Whether the constraint error meets the accuracy. If so, end the operation. If not, adjust the order of the interpolation function or the division of the sub - intervals and start over.

[0077] This method has the advantages of fast convergence speed, high solution accuracy and insensitivity to the initial value.

[0078] The present invention provides the following digital simulation verification process to prove the beneficial effects of the method:

[0079] Comparison with other performance indicators: To verify the reliability and superiority of optimizing the glide trajectory based on the Minimization of Comprehensive Optimization Function (MCOF), it is simulated and compared with the Minimization of Composite Efficiency Factor (MCEF), Minimization of Normal Acceleration (MNA), and Minimization of Flight Time (MFT) below. The optimization functions of each performance indicator are shown in the following formula

[0080] (1) MCOF

[0081]

[0082] (2) MCEF

[0083]

[0084] where K and K max correspond to the lift-to-drag ratio and the maximum lift-to-drag ratio at the current moment respectively, and n and n max correspond to the normal overload corresponding to the current rudder deflection and the maximum rudder deflection respectively.

[0085] (3) MNA

[0086]

[0087] where a m is the normal acceleration of the aircraft during flight.

[0088] (4) MFT

[0089] minJ MFT = t f - t 0

[0090] where t 0 and t f are the moments when the aircraft starts to glide and reaches the end point respectively.

[0091] Based on the GPOPS software, the above indicators are used to optimize the glide trajectory respectively, and the results are as Figures 3 - 6 shown in Table 2 below.

[0092] Table 2 Terminal Parameters of Glide Trajectory

[0093] Performance indicators Flight time (s) Terminal velocity (m / s) Terminal track angle (°) MCOF 72.92 227.98 -40 MCEF 69.43 210 -40 MNA 68.84 210 -25.98 MFT 68.37 210 -20

[0094] From Figures 3 - 6As can be seen from the above table, all four gliding trajectories can reach the end point under the constraints. However, the gliding trajectory designed by minimizing the comprehensive optimization function has obvious advantages in kinetic energy reserve and is very suitable as an index for gliding trajectory optimization.

[0095] Performance index of the composite comprehensive optimization function: The comprehensive optimization function characterizes the kinetic energy loss and maneuvering efficiency of the unmanned gliding aircraft. By using the comprehensive optimization function to construct the Lagrange-type performance index, a reasonable gliding trajectory can be optimized. However, the flight time of the unmanned gliding aircraft is also an extremely important index. To solve the problem that the gliding trajectory designed by minimizing the comprehensive optimization function has a long flight time, this patent combines the minimization of the comprehensive optimization function with the traditional minimization of flight time to construct a performance index of the composite comprehensive optimization function, which is defined as follows

[0096]

[0097] In the formula, k is the weighting coefficient, which can adjust the proportion of the Mayer-type index and the Lagrange-type index. The larger k is, the more the composite index focuses on flight time, and the smaller it is, the more it focuses on kinetic energy reserve. The weighting coefficient cannot be too large, and generally 0.01 - 0.05 can be taken. If the weighting coefficients are k = 0.01 and k = 0.02 respectively, the gliding trajectories optimized by the above indexes are as follows Figures 7 - 10 shown.

[0098] From Figures 7 - 10 it can be seen that the trajectory of the composite comprehensive optimization function is lower, the initial angle of attack is smaller, and it gradually decreases with the increase of the weighting coefficient. This is to increase the flight speed of the aircraft in the initial stage of gliding to reduce the flight time. Therefore, the flight speed of the composite comprehensive optimization function increases significantly in the middle section of the trajectory.

[0099] An electronic device, characterized in that the device includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the computer program is configured to implement the steps of the unmanned gliding aircraft trajectory optimization method as described above.

[0100] A storage medium, characterized in that a computer program is stored on the storage medium, and when the computer program is executed by a processor, it implements the steps of the unmanned gliding aircraft trajectory optimization method as described above.

[0101] It should be noted that the above-described work process is only illustrative and does not limit the protection scope of the present invention. In actual applications, those skilled in the art can select some or all of them according to actual needs to achieve the purpose of the solution of this embodiment, and no limitation is made here.

[0102] In addition, for the technical details not described in detail in this embodiment, reference may be made to the unmanned gliding aircraft trajectory optimization method provided in any embodiment of the present invention, which will not be elaborated here.

[0103] In addition, it should be noted that in this article, the terms "including", "comprising" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or system. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of another identical element in the process, method, article or system including that element.

[0104] The serial numbers of the above embodiments of the present invention are only for description and do not represent the superiority or inferiority of the embodiments.

[0105] Through the description of the above embodiments, those skilled in the art can clearly understand that the above embodiment methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. The computer software product is stored in a storage medium (such as a read-only memory (ROM) / RAM, magnetic disk, optical disk), and includes several instructions for causing a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in various embodiments of the present invention.

[0106] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structure or equivalent process transformation made by using the specification and drawings of the present invention, or directly or indirectly applied to other related technical fields, shall be equally included in the patent protection scope of the present invention.

Claims

1. A method for optimizing the trajectory of an unmanned gliding aircraft, characterized in that ,include: Based on the center-of-mass motion equation of the unmanned gliding aircraft, the energy loss rate and maneuvering efficiency are introduced to describe the degree of mechanical energy loss of the unmanned gliding aircraft and the degree of utilization of maneuvering ability of the unmanned gliding aircraft during gliding flight. Combined with the energy loss and maneuvering efficiency of the unmanned glider aircraft during gliding, a comprehensive optimization function is established; Based on the comprehensive optimization function, its minimum is taken as the performance index to optimize the trajectory of the unmanned gliding aircraft.

2. The unmanned gliding vehicle trajectory optimization method according to claim 1, characterized in that: The energy loss rate Among them, V Max and C xMax They represent the maximum flight speed of the aircraft and the maximum drag coefficient at this speed. Since the speed of an unmanned gliding aircraft will not exceed 1 Ma, V / V Max The current flight Mach number of the aircraft can be used as an approximation, and the drag coefficient C is recorded as x The maximum value of A is the energy loss rate η E Rewrite it into the following form 3. The unmanned gliding aircraft trajectory optimization method according to claim 2, characterized in that: The maneuvering efficiency In the formula, n and n Max They represent the current normal overload and maximum normal overload of the unmanned glider respectively, sign(x) is the sign function, α, α Max They represent the current angle of attack and the maximum angle of attack of the unmanned glider respectively, and e -y is a weighted function, and y represents the current altitude of the aircraft.

4. The unmanned gliding vehicle trajectory optimization method according to claim 2, characterized in that: The comprehensive optimization function η is used to describe the degree of energy loss and the degree of utilization of maneuverability of the unmanned gliding aircraft during gliding flight; the=the E or M Substituting equations (1) and (2) into the above equation, we can obtain:

5. The unmanned gliding vehicle trajectory optimization method according to claim 1, characterized in that: The minimum comprehensive optimization function is used as the performance indicator, as shown in the following formula: The boundary condition constraints are considered as follows: Where (x0, y0), V0 and θ0 represent the coordinates, speed and track angle of the unmanned glider when it starts gliding. f ,y f ), V fmin ,θ fmin and θ fmax They represent the coordinates, minimum speed, minimum track angle, and maximum track angle of the unmanned gliding aircraft when it reaches the end point. Due to the limited maneuverability of the unmanned gliding aircraft, the track angle and angle of attack must also meet the following constraints when the aircraft is gliding:

6. The unmanned gliding aircraft trajectory optimization method according to claim 1, characterized in that: It also includes solving the trajectory optimization problem using the hp-adaptive Rado pseudo-spectral method. The specific steps are as follows: Divide the time domain and transform the subintervals to (-1,1); Use polynomial interpolation to approximate control variables and state variables; Discretize performance indicators and constraints; Convert the original problem into a nonlinear programming problem; Solve nonlinear programming problems using numerical optimization algorithms; Check whether the constraint error satisfies the accuracy. If so, end the operation. If not, adjust the order of the interpolation function or the division of the subintervals and start again.

7. An electronic device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the unmanned gliding aircraft trajectory optimization method according to any one of claims 1 to 6.

8. A storage medium, characterized in that: The storage medium stores a computer program, which, when executed by a processor, implements the steps of the unmanned gliding aircraft trajectory optimization method according to any one of claims 1 to 6.

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