Optimal energy supply method for distributed collaborative microwave-driven air platform

Abstract

The invention provides an optimal energy supply method for a distributed cooperative microwave-driven aerial platform, and the method comprises the steps: firstly building a mathematical relation between the distribution distance of ground energy emission base stations, the initial position coordinates of an unmanned plane, and the total energy consumption of the unmanned plane in the whole flight task process; calculating to obtain the total energy consumption of the unmanned aerial vehicle in the whole flight task process under the set flight task; then, based on the mathematical relationship, with the minimum total energy consumption in the whole flight mission process as an optimization target, an intelligent optimization algorithm is adopted to perform optimization based on a Kriging agent model, and the optimal values of the distribution spacing of the ground energy emission base stations and the initial position coordinates of the unmanned aerial vehicle are obtained; and calculating the whole process of a flight task based on the obtained optimal value to obtain a regulation factor, and correcting the output power of the ground energy emission base station by using the obtained regulation factor to obtain the optimal energy supply method. According to the invention, the energy utilization rate of the microwave-driven unmanned aerial vehicle can be effectively improved when the distributed ground transmitting terminal supplies energy.

Description

Technical Field

[0001] The present invention relates to the technical field of microwave energy supply for aircraft, and specifically to an optimal energy supply method for a distributed cooperative microwave-driven aerial platform. Background Art

[0002] Microwave-driven unmanned aerial vehicles mainly include a flight end (receiving and rectifying antenna), a distributed transmitting end, and a space-ground interconnected high-precision guidance and tracking system. Among them, the flight end uses a microstrip patch antenna fed by a coplanar waveguide to convert the microwave energy transmitted by the ground distributed transmitting end into electrical energy for the aircraft to use. And the distributed transmitting end is driven by a servo system to track the flight platform, forming a microwave synthesis coverage area in the flight airspace of the unmanned aerial vehicle to continuously supply energy to the unmanned aerial vehicle. For a microwave-driven unmanned aerial vehicle, if the loss and life characteristics of components are not considered, it can fly for an unlimited time under the drive of ground energy.

[0003] Currently, there is little research on the energy management scheme of microwave unmanned aerial vehicles driven by a distributed transmitting end in China. Existing research mainly focuses on optimizing the energy propagation process of the distributed transmitting end and the aspect of component optimization.

[0004] However, when using a distributed ground transmitting end for energy supply, there are mainly two influencing factors on the energy utilization efficiency of microwave-driven unmanned aerial vehicles. One is that the flight attitude of the unmanned aerial vehicle during the mission will affect the receiving projection area of microwave energy, resulting in low efficiency of microwave wireless power transfer; the other is that the distribution spacing of the distributed ground transmitting end and the energy distribution of each transmitting end will affect the real-time power transfer efficiency to the unmanned aerial vehicle, and its flight scenario is as Figure 1 shown; at present, the energy supply method considering the influence of flight attitude, the spacing of the distributed ground transmitting end and energy distribution has not been concerned by those skilled in the art. Summary of the Invention

[0005] To solve the above problems, the present invention proposes a method for optimal energy supply of a distributed cooperative microwave-driven aerial platform. By establishing the mathematical relationship between the distribution spacing of ground energy transmission base stations, the initial position coordinates of the unmanned aerial vehicle (UAV), and the total energy consumption of the UAV during the entire flight mission, it is possible to calculate the total energy consumption of the UAV during the entire flight mission based on the distribution spacing of the ground energy transmission base stations and the initial position coordinates of the UAV. Then, based on this mathematical relationship, with the lowest total energy consumption during the entire flight mission as the optimization objective, an intelligent optimization algorithm is used to perform optimization based on the Kriging surrogate model to obtain the optimal values of the distribution spacing of the ground energy transmission base stations and the initial position coordinates of the UAV. Next, based on the obtained optimal values, a calculation of the entire flight mission is performed to obtain a regulation factor, and the output power of the ground energy transmission base stations is corrected using the obtained regulation factor, which is the optimal energy supply method. The present invention optimizes the distribution spacing of the ground energy transmission base stations and the initial position coordinates of the UAV for a given flight mission, obtains a regulation factor matching the flight mission, and then corrects the output power of the ground energy transmission base stations to obtain an energy supply method with the lowest energy consumption for the flight mission, which can effectively improve the energy utilization rate of the UAV in the operating environment of distributed microwave energy supply base stations.

[0006] The technical solution of the present invention is specifically as follows and includes the steps:

[0007] Step 1: Based on the international standard atmosphere, establish an atmospheric environment model. For a given UAV, obtain the basic design parameters, initial state parameters, and the lift-to-drag ratio relationship of the UAV, and use the aircraft performance estimation method to calculate the climb angle boundary and flight speed boundary of the UAV under the set flight mission.

[0008] Step 2: Establish the mathematical relationship between the distribution spacing of ground energy transmission base stations, the initial position coordinates of the UAV, and the total energy consumption of the UAV during the entire flight mission. Through this mathematical relationship, it is possible to calculate the total energy consumption of the UAV during the entire flight mission based on the distribution spacing of the ground energy transmission base stations and the initial position coordinates of the UAV.

[0009] Step 3: Based on the given array distribution form, with the distribution spacing of the ground energy transmission base stations and the initial position coordinates of the UAV as design variables, and with the lowest total energy consumption during the entire flight mission as the optimization objective, use the mathematical relationship established in Step 2 as the objective evaluation method, and use an intelligent optimization algorithm to perform optimization based on the Kriging surrogate model to obtain the optimal values of the distribution spacing of the ground energy transmission base stations and the initial position coordinates of the UAV.

[0010] Step 4: According to the distribution spacing of the ground energy emission base stations and the optimal values of the initial position coordinates of the UAVs obtained in Step 3, return to Step 2 to perform a calculation of the entire flight mission process. Calculate the regulation factor through the effective energy reception area of each UAV for each ground energy emission base station at each time step, and multiply the obtained regulation factor by the initial output power of each ground energy emission base station. The resulting value is used as the final output power of each ground energy emission base station; achieve the optimal energy supply for the distributed collaborative microwave-driven aerial platform.

[0011] Further, the basic design parameters of the UAV in Step 1 include the gravity of the UAV , the mass of the UAV , the wing area of the UAV , the motor power of the UAV , the zero-lift drag of the UAV , the maximum lift coefficient of the UAV , the maximum battery capacity of the UAV , the induced factor ; the maximum energy reception area of the UAV ; the climbing angle of attack , the cruise angle of attack , the cruise altitude ;

[0012] The state initial parameters include the initial time , the time step , the initial coordinate position of the UAV , the initial pitch angle , the initial yaw angle , the initial roll angle , the initial climbing angle , the initial flight speed , the initial directional speed , the initial directional speed , the initial directional speed , the initial output powers of ground energy emission base stations , the initial battery power ;

[0013] The lift-to-drag ratio relationship of the UAV refers to the relationship between the lift-to-drag ratio of the UAV and the altitude , the pitch angle , the roll angle . Its mathematical form is expressed as follows:

[0014]

[0015] Among them is in the form of a function, which is determined by the aerodynamic performance of a given unmanned aerial vehicle and is a conventional technology in the field of aircraft design.

[0016] Furthermore, step 1 specifically includes the following sub-steps:

[0017] Step 1.1: Establish an atmospheric environment model based on the international standard atmosphere;

[0018] Specifically: Take the sea-level reference temperature = 288.15K, the sea-level reference pressure = 101325N / m 2 , the standard gravitational acceleration g = 9.80665Kg / m 2 , the temperature and pressure at different altitudes are determined by the standard formulas in the following table, where is the altitude of the aircraft;

[0019] Table 1 Temperature and Pressure Formulas at Different Altitudes

[0020]

[0021] After determining the temperature and pressure from the altitude, the local atmospheric density is obtained from the following formula :

[0022]

[0023] In the formula, the constant , is the absolute temperature, is the atmospheric pressure;

[0024] Step 1.2: Calculate the flight speed boundary of the unmanned aerial vehicle;

[0025] At a given altitude, the available power of the unmanned aerial vehicle hardly changes with the speed, while the required power first decreases and then increases with the increase of the speed. By using the two intersection points of the required power curve and the available power curve, the maximum speed and the minimum speed of the unmanned aerial vehicle are obtained. Specifically:

[0026] According to = , the following mathematical relationship satisfied by the flight speed is obtained:

[0027]

[0028] Among them Take the intersection point value of the required power curve and the available power curve, Let \(V\) be the flight speed of the UAV. Solving the above equation gives two speed values corresponding to the two intersection points of the required power curve and the available power curve. They are the minimum and maximum speeds during level flight, respectively.

[0029] Since the UAV has a stall speed, the minimum speed is compared with the stall speed again, and the smaller value is taken as the minimum level flight speed. The stall speed formula is as follows:

[0030]

[0031] The final minimum speed is:

[0032]

[0033] Furthermore, the speed boundary of the UAV is obtained as follows:

[0034] ;

[0035] Step 1.3: Calculate the climb angle boundary of the UAV;

[0036] The height range from the ground to the cruise altitude of the aircraft is divided into a set number of height intervals. In each height interval, assuming that the climb angle range remains unchanged within this height interval, the left boundary of the height interval is taken as the current climb height, and the climb angle range of this height interval is obtained by using the graphical method. as the climb angle range of this height interval;

[0037] The specific process of obtaining the climb angle range of each height interval by using the graphical method is as follows:

[0038] Assume that the UAV performs a steady climb at the current climb height with a climb angle . According to the dynamic equation, we get:

[0039]

[0040]

[0041]

[0042] In the formula, is the pull of the UAV, is the lift of the UAV, is the drag of the UAV, is the gravity of the UAV, is the climb angle of the UAV, is the mass of the UAV, is the flight speed of the UAV, is the curvature radius of the UAV’s trajectory;

[0043] Combining the above formulas, we get the climbing rate for:

[0044]

[0045] Get the climb angle:

[0046]

[0047] The resistance calculation expression

[0048]

[0049] Substituting into the climb rate Evaluate the expression and get:

[0050]

[0051] The above equations are solved by graphical method, namely Plot for the independent variable The function graph is within the velocity boundary of the UAV obtained in step 1.2. The minimum speed on the function graph of The slope of the line connecting the point and the origin is the minimum climbing angle Considering the actual carrying capacity of the aircraft, the maximum climb angle is limited to 2°, that is, ;

[0052] Then the climbing angle boundary of the UAV is obtained as follows:

[0053] .

[0054] Furthermore, step 2 specifically includes the following sub-steps:

[0055] Step 2.1: First, establish the coordinate transformation relationship from the navigation axis system to the body axis system; the origin of the body axis system coordinate system is located at the center of gravity of the aircraft. The axis is located in the symmetry plane of the aircraft, parallel to the fuselage axis, and points forward. The axis lies in the plane of symmetry of the aircraft and is perpendicular to The axis points downward. The axis is perpendicular to the symmetry plane and points to the right, satisfying the right-hand system; the navigation axis system is a ground inertial coordinate system, and the origin of the coordinate is the absolute coordinate (0,0,0);

[0056] Place the transmitting base station in the center of the array at the origin of the navigation axis coordinate system (0,0,0), and calculate the The corresponding position coordinates of the ground energy transmitting base station ; Calculate the yaw angle of the UAV entering the field according to the initial position coordinates of the UAV 、 、 ; ;

[0057] Step 2.2: Starting from the initial moment, calculate the energy consumption of the UAV at the current time step based on the position coordinates of the ground energy emission base station, the position coordinates of the UAV at the current time step, the roll angle, the pitch angle, and the yaw angle , specifically:

[0058] Based on the position relationship between the navigation axis system and the body axis system, obtain the transformation matrix for the direction (roll), the transformation matrix for the direction (pitch), and the transformation matrix for the direction (yaw). The specific expressions are as follows:

[0059]

[0060]

[0061]

[0062] In the formula, 、 、 are the transformation matrix for the direction (roll), the transformation matrix for the direction (pitch), and the transformation matrix for the direction (yaw) respectively; 、 、 are the roll angle, pitch angle, and yaw angle of the UAV at the current time step. At the initial moment, the roll angle is , the pitch angle is , and the yaw angle is ;

[0063] Based on the position coordinates of the UAV at the current time step and the position coordinates of the ground energy emission base stations, obtain the normal vector of the energy irradiation surface:

[0064]

[0065] Let the initial body axis system matrix of the UAV be:

[0066]

[0067] Then the transformed body axis system matrix is:

[0068]

[0069] The plane of the UAV body is the receiving surface, and the normal direction of the receiving surface in the body axis system is the direction vector , and through transformation, the normal vector of the receiving surface at the current time step is obtained:

[0070]

[0071] According to the calculation principle of the plane angle in space geometry, the angle between the receiving plane and the normal vector of the irradiation plane is between , and the effective energy receiving area of the UAV for each ground energy emission base station at the current time step is calculated by the following formula :

[0072]

[0073] In the formula, is the maximum receiving energy area of the UAV;

[0074] Furthermore, the receiving efficiency of the energy emitted by each energy emission base station at the current time step is calculated by the following formula :

[0075]

[0076] In the formula, is the initial output power of the th ground energy emission base station;

[0077] Furthermore, the energy consumption of the UAV at the current time step is calculated by the following formula

[0078]

[0079] In the formula, is the maximum capacity of the UAV battery;

[0080] Step 2.3: Sum up the energy consumption of the UAV at each time step to obtain the total energy consumption of the UAV during the entire flight mission .

[0081] Furthermore, the calculation methods of the position coordinates, roll angle, pitch angle, and yaw angle of the UAV at each time step in Step 2.2 are specifically as follows:

[0082] ① Calculate the corresponding atmospheric density according to the altitude of the aircraft;

[0083] Starting from the initial moment, let the time , at this time the altitude of the UAV is , calculate the atmospheric density corresponding to the current altitude based on the atmospheric environment model in step 1.1 ;

[0084] ② Calculate the pulling force, lift force , and drag force of the UAV at the current time step ;

[0085] Let the flight speed be , according to the motor power of the UAV , calculate the pulling force of the UAV at the current time step:

[0086]

[0087] Based on the zero-lift drag , induced factor , UAV weight , UAV wing area in the initial parameters of the UAV state and the atmospheric density corresponding to the current altitude, flight speed , calculate the drag force of the UAV at the current time step:

[0088]

[0089] According to the altitude , pitch angle , roll angle of the UAV at the current time, based on the lift-drag ratio relationship of the UAV, calculate the lift-drag ratio of the UAV, and then calculate the lift force of the UAV at the current time step:

[0090]

[0091] At the initial moment, the altitude is , the pitch angle is , the roll angle is ;

[0092] ③ Based on the forces acting on the UAV, calculate the acceleration of the UAV at the current time step, specifically:

[0093] According to Newton's second law, the forward accelerations of the UAV at the current time step are respectively:

[0094]

[0095]

[0096]

[0097] In the formula, represents the -direction acceleration of the UAV, represents the -direction acceleration of the UAV, represents the -direction acceleration of the UAV;

[0098] If the current flight speed exceeds the speed boundary of the UAV determined in step 1.2, i.e., or at this time, the acceleration of the UAV at the current time step is regulated as follows:

[0099]

[0100]

[0101]

[0102] , , is the set acceleration regulation parameter;

[0103] ④ Based on the acceleration of the UAV at the current time step, calculate the yaw angle, flight speed, and position coordinates of the UAV at the next time step. Specifically:

[0104] Based on the flight speed at the current time step, calculate the yaw angle at the next moment through the following formula as:

[0105]

[0106] In the formula, , , are the -direction speeds of the UAV at the current time step, respectively. At the initial moment, , , ;

[0107] Assume that the UAV performs uniformly accelerated motion within one time step. Based on the acceleration and speed of the UAV at the current time step, calculate the speed of the UAV at the next time step from the following formula:

[0108]

[0109]

[0110]

[0111] In the formula, is the set time step, , , are respectively the direction speeds of the unmanned aerial vehicle (UAV) at the next time step;

[0112] Assume that the UAV undergoes a uniformly accelerated motion within a time step. Based on the acceleration and speed of the UAV at the current time step, the displacement of the UAV at the current time step is calculated by the following formula:

[0113]

[0114]

[0115]

[0116] In the formula, , , are respectively the direction displacements of the UAV at the current time step;

[0117] Furthermore, the position coordinates of the UAV at the next time step are calculated by the following formula:

[0118]

[0119]

[0120]

[0121] In the formula, , , are respectively the position coordinates of the UAV at the current time step. At the initial moment , , ; , , are respectively the position coordinates of the UAV at the next time step, i.e., used as the altitude of the UAV at the next time step;

[0122] ⑤ Based on the minimum energy consumption of the UAV at the current time step calculate the roll angle and pitch angle of the UAV at the next time step. Specifically:

[0123] Based on the roll angle at the current time step and the climb angle boundary calculated in step 1.3, the optional intervals of the pitch angle and roll angle at the next time step are established as follows according to the following formula:

[0124] ϕ range ∈[ϕ - ∆ϕ, ϕ + ∆ϕ]

[0125] γ range ∈[ γ min , γ max ]

[0126]

[0127] In the formula, 、 are the optional intervals of the roll angle and climb angle at the next time step, is the set roll angle change amount; is the angle of attack of the UAV at the current time step. When , ; when , , where is the climb angle of attack, is the cruise angle of attack;

[0128] For and , randomly sample 50 samples each within 、 to form a 50 × 50 matrix. Use the method described in step 2.2 to calculate and compare the energy consumption of the UAV corresponding to the 2500 combinations at the current time step, and select the minimum value corresponding to and as the roll angle and pitch angle at the next time step.

[0129] Furthermore, the acceleration control parameter , , takes -2m / s 2 to 2m / s 2 .

[0130] Furthermore, step 3 specifically includes the following sub-steps:

[0131] Step 3.1: Using the distribution spacing of the ground energy emission base stations and the initial position coordinates of the UAV as design variables, with the lowest total energy consumption throughout the flight mission as the optimization objective, set the value range of the design variables, conduct Latin hypercube sampling within the set value range of the design variables to obtain a number of sample points; use the mathematical relationship established in Step 2 as the optimization objective evaluation method to calculate the sample points, obtain the total energy consumption throughout the flight mission of each sample point, and thus obtain a sample data set composed of a number of sample points and their corresponding total energy consumption;

[0132] Step 3.2: Based on the obtained sample data set, establish a Kriging surrogate model, and then use an intelligent optimization algorithm to optimize based on the Kriging surrogate model to obtain the optimal values of the distribution spacing of the ground energy emission base stations and the initial position coordinates of the UAV.

[0133] Further, Step 4 specifically includes the following sub-steps:

[0134] Step 4.1: According to the optimal values of the distribution spacing of the ground energy emission base stations and the initial position coordinates of the UAV obtained in Step 3, return to Step 2 to perform a calculation of the entire process of a flight mission, and obtain the effective energy reception area of the UAV for each ground energy emission base station at each time step , ;

[0135] Step 4.2: Calculate the regulation factor using the following formula :

[0136]

[0137]

[0138]

[0139] In the formula, is the regulation factor of the th ground energy emission base station, is the mean value of the effective received energy area of the th ground energy emission base station under the traversed mission time steps, is the sum of the mean values of the effective received energy areas of the ground energy emission base stations under the traversed mission time steps;

[0140] Step 4.3: Multiply the regulation factor obtained in Step 4.2 by the initial output power of each ground energy emission base station to obtain the final output power of each ground energy emission base station:

[0141]

[0142] In the formula, is the initial output power of the th ground energy emission base station, the th ground energy emission base station's final output power.

[0143] Beneficial effects:

[0144] The present invention proposes a distributed collaborative microwave-driven aerial platform optimal energy supply method. By establishing the mathematical relationship between the distribution spacing of ground energy emission base stations, the initial position coordinates of the unmanned aerial vehicle (UAV) and the total energy consumption of the UAV during the entire flight mission, it is possible to calculate the total energy consumption of the UAV during the entire flight mission based on the distribution spacing of the ground energy emission base stations and the initial position coordinates of the UAV; then, based on this mathematical relationship, with the lowest total energy consumption during the entire flight mission as the optimization goal, an intelligent optimization algorithm is used to perform optimization based on the Kriging surrogate model to obtain the optimal values of the distribution spacing of the ground energy emission base stations and the initial position coordinates of the UAV; then, based on the obtained optimal values, a calculation of the entire flight mission is performed once to obtain a regulation factor, and the output power of the ground energy emission base station is corrected with the obtained regulation factor, which is the optimal energy supply method; the present invention optimizes the distribution spacing of the ground energy emission base stations and the initial position coordinates of the UAV for a set flight mission, obtains a regulation factor matching the flight mission, and then corrects the output power of the ground energy emission base station to obtain an energy supply method with the lowest energy consumption for the flight mission, which can effectively improve the energy utilization rate of the UAV in the operating environment of distributed microwave energy supply base stations. Description of the drawings

[0145] Figure 1 is a schematic diagram of the flight scenario of the microwave-powered UAV of the present invention;

[0146] Figure 2 is a flowchart for implementing the solution of the embodiment of the present invention;

[0147] Figure 3 is a schematic diagram for solving the maximum and minimum speeds based on the graphical method of required power and available power in the embodiment of the present invention;

[0148] Figure 4 is a schematic diagram of the aircraft speed boundary in the embodiment of the present invention;

[0149] Figure 5 is a schematic diagram for solving the minimum climb angle based on the graphical method in the embodiment of the present invention;

[0150] Figure 6 is a schematic diagram of the body axis system in the embodiment of the present invention;

[0151] Figure 7 is a schematic diagram of rectangular distribution in the embodiment of the present invention;

[0152] Figure 8 In the embodiment of the present invention, it is the process of climbing to a height of 8000m after introducing the regulatory factor and the control without introducing the regulatory factor. Specific Embodiments

[0153] In order to make the technical problems, technical solutions and beneficial effects solved by the present invention clearer and more understandable, and enable those skilled in the art to better understand the present invention, the present invention will be further described in detail and completely below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0154] As Figure 1 shown, in this embodiment, taking a certain type of microwave-powered unmanned aerial vehicle as an example, based on the energy supply scenario of a rectangular distributed array ground energy emission base station, the method proposed by the present invention is used to perform distributed collaborative microwave-driven optimal energy supply flight planning for this type of unmanned aerial vehicle under the set flight mission. In this embodiment, the set flight mission is for the unmanned aerial vehicle to take off from the ground and climb to the cruising altitude of 18000m, and the rectangular distributed array includes ground energy emission base stations.

[0155] As Figure 2 shown, the distributed collaborative microwave-driven optimal energy supply method for the aerial platform in this embodiment includes the following steps:

[0156] Step 1: Establish an atmospheric environment model based on the international standard atmosphere. For a given unmanned aerial vehicle, obtain the basic design parameters, initial state parameters, and lift-to-drag ratio relationship of the unmanned aerial vehicle, and use the aircraft performance estimation method to calculate the climb angle boundary and flight speed boundary of the unmanned aerial vehicle under the set flight mission;

[0157] Step 2: Establish a mathematical relationship between the distribution spacing of the ground energy emission base stations, the initial position coordinates of the unmanned aerial vehicle, and the total energy consumption of the unmanned aerial vehicle throughout the flight mission. Through this mathematical relationship, the total energy consumption of the unmanned aerial vehicle throughout the flight mission can be calculated based on the distribution spacing of the ground energy emission base stations and the initial position coordinates of the unmanned aerial vehicle;

[0158] Step 3: Based on the given array distribution form, using the distribution spacing of the ground energy emission base stations and the initial position coordinates of the unmanned aerial vehicle as design variables, with the lowest total energy consumption throughout the flight mission as the optimization goal, using the mathematical relationship established in Step 2 as the target evaluation method, and using an intelligent optimization algorithm to perform optimization based on the Kriging surrogate model to obtain the optimal values of the distribution spacing of the ground energy emission base stations and the initial position coordinates of the unmanned aerial vehicle;

[0159] Step 4: According to the distribution spacing of the ground energy emission base stations and the optimal values of the initial position coordinates of the UAV obtained in Step 3, return to Step 2 to calculate the entire process of a flight mission. Calculate the regulation factor through the effective energy reception area of the UAV for each ground energy emission base station at each time step, and multiply the obtained regulation factor by the initial output power of each ground energy emission base station. The obtained result is used as the final output power of each ground energy emission base station; to achieve the optimal energy supply for the distributed collaborative microwave-driven aerial platform.

[0160] In this embodiment, the basic design parameters of the UAV in Step 1 include the gravity of the UAV , the mass of the UAV , the wing area of the UAV , the motor power of the UAV , the zero-lift drag of the UAV , the maximum lift coefficient of the UAV , the maximum battery capacity of the UAV , the induced factor ; the maximum energy reception area of the UAV ; the climbing angle of attack , the cruise angle of attack , the cruise altitude ;

[0161] Specifically in this embodiment, the gravity of the UAV = 3000N, the mass of the UAV = 306.12 kg, the wing area of the UAV = 50 m 2 , the motor power of the UAV = 7.9 KW, the zero-lift drag of the UAV = 0.0085, the maximum lift coefficient of the UAV = 0.725, the maximum battery capacity of the UAV = 61200000 Wh, the induced factor = 0.05; the maximum energy reception area of the UAV = 17 m 2 ; the climbing angle of attack = 2°, the cruise angle of attack = 2°, the cruise altitude = 18000 m;

[0162] The state initial parameters include the initial time = 0 s, the time step = 0.3 s, the initial coordinate position of the UAV = 200 m, = 300 m, = 500 m, the initial pitch angle = 4°, initial yaw angle = 35°, initial roll angle = 20°, initial climb angle = 2°, initial flight speed = 10.3 m / s, initial directional speed = 9.67 m / s, initial directional speed = 3.52 m / s, initial directional speed = 0.36 m / s, initial output power of a ground energy emission base station = 1.7 KW, initial battery power = 1, the distribution form of the ground energy emission base stations is rectangular, initial spacing = 2000 m;

[0163] The lift-to-drag ratio relationship of the UAV refers to the lift-to-drag ratio of the UAV and altitude 、pitch angle 、roll angle The relationship is as follows in mathematical form:

[0164]

[0165] where is in the form of a function, determined by the aerodynamic performance of the given UAV, and is a conventional technique in the field of aircraft design.

[0166] In this embodiment, the relationship between the lift-to-drag ratio of the UAV and altitude, pitch angle, and roll angle is as follows:

[0167] .

[0168] In this embodiment, the process of calculating the climb angle boundary and flight speed boundary of the UAV under the set flight mission by using the aircraft performance estimation method in step 1 specifically includes the following sub-steps:

[0169] Step 1.1: Establish an atmospheric environment model based on the international standard atmosphere;

[0170] Specifically: Take the sea-level reference temperature = 288.15 K, the sea-level reference pressure = 101325 N / m 2 , the standard gravitational acceleration g = 9.80665 Kg / m 2 , the temperature and pressure at different altitudes are determined by the standard formulas in the following table, where is the altitude of the aircraft;

[0171] Table 2 Temperature and Pressure Formulas at Different Heights

[0172]

[0173] After determining the temperature and pressure by height, the local atmospheric density is obtained from the following formula :

[0174]

[0175] In the formula, the constant , is the absolute temperature, and

[0176] Step 1.2: Calculate the speed boundary of the UAV;

[0177] At a given altitude, the available power of the aircraft varies little with speed, while the required power first decreases and then increases with the increase of speed. Using the two intersections of the required power curve and the available power curve, as Figure 3 shown, the maximum and minimum speeds of the aircraft are obtained. Specifically:

[0178] According to = , the mathematical relationship satisfied by the flight speed is as follows:

[0179]

[0180] where takes the intersection value of the required power curve and the available power curve, is the flight speed of the UAV. Solving the above formula gives two speed values corresponding to the two intersections of the required power curve and the available power curve , which are the minimum and maximum speeds during level flight in turn;

[0181] Since the UAV has a stall speed, the minimum speed is compared with the stall speed again, and the smaller value is taken as the minimum level flight speed. The stall speed formula is as follows:

[0182]

[0183] The final minimum speed is:

[0184]

[0185] Furthermore, the speed boundary of the UAV is obtained as follows:

[0186] ;

[0187] In this embodiment, the calculated speed boundary of the drone is as follows: Figure 4 The aerodynamic boundary shown in the figure is the calculated stall speed. .

[0188] Step 1.3: Calculate the climb angle boundary of the UAV;

[0189] From the ground to the aircraft's cruising altitude The altitude range is divided into a set number of altitude intervals. In each altitude interval, assuming that the climbing angle range remains unchanged within this altitude interval, the left boundary of the altitude interval is taken as the current climbing altitude, and the climbing angle range of the altitude interval is obtained by using a graphical method. , as the climbing angle range of the altitude interval; the climbing angle range of each altitude interval is obtained by using the graphical method The specific process is:

[0190] Assume that the UAV is climbing at the current climbing altitude with a climbing angle Perform steady climb and obtain according to the dynamic equation:

[0191]

[0192]

[0193]

[0194] In the formula, is the pulling force of the drone, For the lift of the drone, For the resistance of the drone, is the gravity of the drone, is the climbing angle of the UAV, For the quality of the drone, is the flight speed of the drone, is the curvature radius of the UAV’s trajectory;

[0195] Combining the above formulas, we get the climbing rate for:

[0196]

[0197] Get the climb angle:

[0198]

[0199] The resistance calculation expression

[0200]

[0201] Substituting into the climb rate Evaluate the expression and get:

[0202]

[0203] Solve the above equation by the graphical method, that is, draw the function image of with the independent variable, and within the speed boundary of the UAV obtained in step 1.2, find the point on the function image of corresponding to the minimum speed . The slope of the line connecting this point and the origin is the minimum climb angle , as shown in ; considering the actual load capacity of the aircraft, limit the maximum climb angle to 2°, that is, take Figure 5 ;

[0204] Furthermore, obtain the climb angle boundary of the UAV as follows:

[0205] .

[0206] In this embodiment, the height range from the ground to the cruise altitude of the aircraft is divided into 5 height intervals. Within each height interval, the calculated climb angle boundary of the UAV is as follows:

[0207] At an altitude between 0 - 1500 m, the climb angle range is 0.0533° - 2°;

[0208] At an altitude between 1500 - 4500 m, the climb angle range is 0.0508° - 2°;

[0209] At an altitude between 4500 - 7500 m, the climb angle range is 0.1619° - 2°;

[0210] At an altitude between 7500 - 13500 m, the climb angle range is 0.0565° - 2°;

[0211] At an altitude between 13500 - 18000 m, the climb angle range is 0.0391° - 2°;

[0212] In this embodiment, step 2 specifically includes the following sub - steps:

[0213] Step 2.1: First, establish the coordinate transformation relationship from the navigation axis system to the body axis system; the origin of the body axis system is located at the aircraft's center of gravity, axis is located in the aircraft's symmetric plane, parallel to the fuselage axis, pointing forward, axis is located in the aircraft's symmetric plane, perpendicular to the axis, pointing downward, The axis is perpendicular to the symmetry plane and points to the right, satisfying the right-hand system; the navigation axis system is the ground inertial coordinate system, and the coordinate origin is the absolute coordinate (0, 0, 0).

[0214] Place the position of the central transmitting base station of the array at the coordinate origin (0, 0, 0) of the navigation axis system, and calculate the corresponding position coordinates of ground energy transmitting base stations according to the array distribution form and the spacing between base stations ; According to the initial position coordinates of the UAV 、 , calculate the yaw angle when the UAV enters the field;

[0215] Step 2.2: Starting from the initial moment, calculate the energy consumption of the UAV at each time step based on the position coordinates of the ground energy transmitting base stations, the position coordinates of the UAV at the current time step, the roll angle, the pitch angle, and the yaw angle , specifically:

[0216] Based on the position relationship between the navigation axis system and the body axis system, obtain the transformation matrix for the direction (roll), the transformation matrix for the direction (pitch), and the transformation matrix for the direction (yaw). The specific expressions are as follows:

[0217]

[0218]

[0219]

[0220] In the formula, 、 、 are the transformation matrix for the direction (roll), the transformation matrix for the direction (pitch), and the transformation matrix for the direction (yaw) respectively; 、 、 are the roll angle, pitch angle, and yaw angle of the UAV at the current time step. At the initial moment, the roll angle , the pitch angle , and the yaw angle ;

[0221] Based on the position coordinates of the UAV at the current time step and the position coordinates of ground energy transmitting base stations, obtain the normal vector of the energy irradiation surface:

[0222]

[0223] Let the initial body-axis system matrix of the drone be:

[0224]

[0225] Then the body-axis system matrix after transformation is:

[0226]

[0227] The plane of the drone body is the receiving surface, and the normal of the receiving surface in the body-axis system is direction vector , and through transformation, the normal vector of the receiving surface at the current time step is obtained:

[0228]

[0229] According to the calculation principle of the plane angle in space geometry, the angle between the normal vector of the receiving plane and the illumination plane is and , and the effective energy receiving area of the drone for each ground energy emission base station at the current time step is calculated using the following formula :

[0230]

[0231] In the formula, is the maximum receiving energy area of the drone;

[0232] Furthermore, the receiving efficiency of the energy emitted by each energy station at the current time step is calculated using the following formula :

[0233]

[0234] In the formula, is the initial output power of the th ground energy emission base station;

[0235] Furthermore, the energy consumption of the drone at the current time step is calculated using the following formula

[0236]

[0237] In the formula, is the maximum capacity of the drone battery;

[0238] Step 2.3: The energy consumption of the drone at each time step Perform superposition summation to obtain the total energy consumption of the entire process of the UAV flight mission 。

[0239] In this embodiment, the calculation methods for the position coordinates, roll angle, pitch angle, and yaw angle of the UAV at each time step in step 2.2 are specifically as follows:

[0240] ① Calculate the corresponding atmospheric density based on the altitude of the aircraft;

[0241] Starting from the initial moment, let the time , at this time the altitude of the UAV , calculate the atmospheric density corresponding to the current altitude based on the atmospheric environment model in step 1.1 ;

[0242] ② Calculate the thrust , lift and drag of the UAV at the current time step;

[0243] Let the flight speed , according to the motor power of the UAV , calculate the thrust of the UAV at the current time step:

[0244]

[0245] Based on the zero-lift drag , induced factor , UAV weight , UAV wing area and the atmospheric density corresponding to the current altitude, flight speed , calculate the drag of the UAV at the current time step:

[0246]

[0247] According to the altitude , pitch angle , roll angle of the UAV at the current time, based on the lift-to-drag ratio relationship of the UAV, calculate the lift-to-drag ratio of the UAV, and then calculate the lift of the UAV at the current time step:

[0248]

[0249] At the initial moment, the altitude , pitch angle , roll angle ;

[0250] ③Based on the forces acting on the drone, calculate the acceleration of the drone at the current time step. Specifically:

[0251] According to Newton's second law, the directional accelerations of the drone at the current time step are respectively:

[0252]

[0253]

[0254]

[0255] In the formula, represents the directional acceleration of the drone, represents the directional acceleration of the drone, represents the directional acceleration of the drone;

[0256] If the current flight speed exceeds the speed boundary of the drone determined in step 1.2, that is, or at this time, the acceleration of the drone at the current time step is adjusted as follows:

[0257]

[0258]

[0259]

[0260] , , is the set acceleration adjustment parameter; in this embodiment, , , are all taken as 1 m / s 2 ;

[0261] ④Based on the acceleration of the drone at the current time step, calculate the yaw angle, flight speed, and position coordinates of the drone at the next time step. Specifically:

[0262] Based on the flight speed at the current time step, calculate the yaw angle at the next moment through the following formula which is:

[0263]

[0264] In the formula, , , The velocity of the drone at the current time step in each direction. At the initial moment, , , , ;

[0265] Assume that the drone undergoes uniform acceleration within one time step. Based on the acceleration and velocity of the drone at the current time step, the velocity of the drone at the next time step is calculated by the following formula:

[0266]

[0267]

[0268]

[0269] In the formula, is the set time step size, , , are the velocities of the drone at the next time step in the direction respectively;

[0270] Assume that the drone undergoes uniform acceleration within one time step. Based on the acceleration and velocity of the drone at the current time step, the displacement of the drone at the current time step is calculated by the following formula:

[0271]

[0272]

[0273]

[0274] In the formula, , , are the displacements of the drone at the current time step in the direction respectively;

[0275] Furthermore, the position coordinates of the drone at the next time step are calculated by the following formula:

[0276]

[0277]

[0278]

[0279] In the formula, , , are the position coordinates of the drone at the current time step. At the initial moment , , ; , , are the position coordinates of the UAV at the next time step, i.e., the altitude of the UAV at the next time step;

[0280] ⑤ Based on the energy consumption of the UAV at the current time step is minimized, calculate the roll angle and pitch angle of the UAV at the next time step, specifically:

[0281] Based on the roll angle at the current time step and the climb angle boundary calculated in step 1.3, establish the following optional intervals for the pitch angle and roll angle at the next time step according to the following formula:

[0282] ϕ range ∈[ϕ - ∆ϕ, ϕ + ∆ϕ]

[0283] γ range ϵ γ min , γ max ]

[0284]

[0285] In the formula, , are the optional intervals for the roll angle and climb angle at the next time step, is the set roll angle change amount, taking in this embodiment ; is the angle of attack of the UAV at the current time step. When , , when , , in the formula is the climb angle of attack, is the cruise angle of attack;

[0286] For and , randomly sample 50 samples each within , to form a 50 × 50 matrix. Use the method described in step 2.2 to calculate the energy consumption of the UAV corresponding to 2500 combinations at the current time step and compare them. Select the minimum value corresponding to and as the roll angle and pitch angle for the next time step.

[0287] In this embodiment, step 3 specifically includes the following sub-steps:

[0288] Step 3.1: Taking the distribution spacing of the ground energy emission base stations and the initial position coordinates of the UAV as design variables, with the minimum total energy consumption throughout the flight mission as the optimization objective, setting the value range of the design variables, conducting Latin hypercube sampling within the set value range of the design variables to obtain a number of sample points; using the mathematical relationship established in Step 2 as the optimization objective evaluation method to calculate the sample points, obtaining the total energy consumption throughout the flight mission for each sample point, thereby obtaining a sample data set composed of a number of sample points and their corresponding total energy consumption;

[0289] Step 3.2: Based on the obtained sample data set, establish a Kriging surrogate model, and then use an intelligent optimization algorithm to perform optimization based on the Kriging surrogate model to obtain the optimal values of the distribution spacing of the ground energy emission base stations and the initial position coordinates of the UAV.

[0290] In this embodiment, the change ranges of the variables are set as follows: the base station spacing range is between 500 m and 2500 m, the pitch angle range is between 2° and 6°, the roll angle range is between 15° and 25°, the yaw angle range is between -180° and 180°, the flight speed range is between the speed boundaries of the UAV determined in Step 1.2. 1380 sample points are obtained through sampling, and an intelligent optimization algorithm is used to perform optimization based on the Kriging surrogate model. After optimization, the optimal distribution spacing of the rectangular energy supply array is 1040.16 m, and the optimal value of the initial coordinates of the UAV is (435.3, 502.3, 500);

[0291] In this embodiment, Step 4 specifically includes the following sub-steps:

[0292] Step 4.1: According to the optimal values of the distribution spacing of the ground energy emission base stations and the initial position coordinates of the UAV obtained in Step 3, return to Step 2 to perform a calculation of the entire process of a flight mission, and obtain the effective energy receiving area of the UAV for each ground energy emission base station at each time step , ;

[0293] Step 4.2: Calculate the regulation factor using the following formula :

[0294]

[0295]

[0296]

[0297] In the formula, is the regulation factor of the th ground energy emission base station, is the th average value of the effective received energy area of the ground energy emission base station under the traversal task time step, is the sum of the average values of the effective received energy areas of the ground energy emission base stations under the traversal task time step;

[0298] Step 4.3: Multiply the regulation factor obtained in Step 4.2 by the initial output power of each ground energy emission base station to obtain the final output power of each ground energy emission base station:

[0299]

[0300] In the formula, is the th initial output power of the ground energy emission base station, the th final output power of the ground energy emission base station.

[0301] In this embodiment, according to the optimal values of the distribution spacing of the ground energy emission base stations, the pitch angle, yaw angle, roll angle, and flight speed of the unmanned aerial vehicle obtained in Step 3, the regulation factors of 9 ground energy emission base stations are calculated as shown in the following table:

[0302] Table 3 Regulation Factors of Each Base Station

[0303]

[0304] In this embodiment, based on the rectangular array distribution form, the above-obtained regulation factors are introduced to correct the output power of the ground energy emission base stations. Taking the functional method without introducing the regulation factors as a control, the course of the unmanned aerial vehicle climbing 8000 m has a change trend as Figure 8 shown. It can be seen from the figure that at a climbing height of about 8000 m, the consumption can be reduced by about 12%, proving that the energy supply method of the present invention can effectively improve the energy utilization rate of the unmanned aerial vehicle in the operating environment of the distributed microwave energy supply base station.

[0305] Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention without departing from the principles and purposes of the present invention.

Claims

1. The optimal energy supply method for distributed collaborative microwave-driven aerial platforms, characterized in that: It includes the following steps: Step 1: Establish an atmospheric environment model based on the international standard atmosphere. For a given unmanned aerial vehicle (UAV), obtain the basic design parameters, initial state parameters, and the lift-drag ratio relationship of the UAV. Use the aircraft performance estimation method to calculate the climb angle boundary and flight speed boundary of the UAV under the set flight mission; Step 2: Establish a mathematical relationship between the distribution spacing of ground energy emission base stations, the initial position coordinates of the UAV, and the total energy consumption of the UAV throughout the flight mission. Through this mathematical relationship, the total energy consumption of the UAV throughout the flight mission can be calculated based on the distribution spacing of ground energy emission base stations and the initial position coordinates of the UAV; Step 3: Based on the given array distribution form, with the distribution spacing of ground energy emission base stations and the initial position coordinates of the UAV as design variables, and the lowest total energy consumption throughout the flight mission as the optimization goal, use the mathematical relationship established in Step 2 as the objective evaluation method, and use an intelligent optimization algorithm to perform optimization based on the Kriging surrogate model to obtain the optimal values of the distribution spacing of ground energy emission base stations and the initial position coordinates of the UAV; Step 4: According to the optimal values of the distribution spacing of ground energy emission base stations and the initial position coordinates of the UAV obtained in Step 3, return to Step 2 to perform a calculation of the entire flight mission process. Calculate the regulation factor by calculating the effective energy reception area of the UAV for each ground energy emission base station at each time step. Multiply the obtained regulation factor by the initial output power of each ground energy emission base station, and the resulting value is used as the final output power of each ground energy emission base station; Realize the optimal energy supply for the distributed cooperative microwave-driven aerial platform.

2. The optimal power supply method for the distributed cooperative microwave-driven aerial platform according to claim 1, wherein: The basic design parameters of the drone described in Step 1 include the gravity of the drone , the mass of the drone , the wing area of the drone , the motor power of the drone , the zero-lift drag of the drone , the maximum lift coefficient of the drone , the maximum capacity of the drone's battery , the induced factor ; Maximum receiving energy area of the drone ; Climbing angle of attack , Cruise angle of attack , Cruise altitude ; The state initial parameters include the initial time , time step , the initial coordinate position of the UAV , initial pitch angle , initial yaw angle , initial roll angle , initial climb angle , initial flight speed , initial directional speed , initial directional speed , initial directional speed , the initial output power of ground energy emission base stations , initial battery power ; The lift-to-drag ratio relationship of the UAV refers to the lift-to-drag ratio of the UAV and altitude , pitch angle , roll angle The relationship is as follows in mathematical form: wherein is in the form of a function and is determined by the aerodynamic performance of a given drone.

3. The optimal energy supply method for the distributed cooperative microwave-driven aerial platform according to claim 2, characterized in that: Step 1 specifically includes the following sub-steps: Step 1.1: Establish an atmospheric environment model based on the international standard atmosphere; Step 1.2: Calculate the flight speed boundary of the UAV; Based on the two intersection points of the required power curve and the available power curve of the UAV, use the formula Solve to obtain two speed values corresponding to the two intersection points of the required power curve and the available power curve , which are respectively the minimum speed and the maximum speed during level flight; among them is the local atmospheric density determined according to the atmospheric environment model, is the flight speed of the UAV, Take the intersection point value of the required power curve and the available power curve; Compare the minimum speed calculated from the above formula with the stall speed Conduct a secondary comparison and take the smaller value as the minimum level flight speed: Furthermore, obtain the speed boundary of the UAV as follows: ； Step 1.3: Calculate the climb angle boundary of the UAV; The altitude range from the ground to the cruising altitude of the aircraft is divided into a set number of altitude intervals. Within each altitude interval, assuming that the climb angle range remains unchanged within this altitude interval, the left boundary of the altitude interval is taken as the current climb altitude, and the climb angle range of this altitude interval is obtained by using the graphical method as the climb angle range of this altitude interval.

4. The optimal energy supply method for the distributed collaborative microwave-driven aerial platform according to claim 3, characterized in that: The climb angle range of each height interval is obtained by using the graphical method to solve The specific process is as follows: Assume that the UAV performs a steady climb at the current climbing altitude with a climb angle and according to the dynamic equation, we can obtain: Wherein, is the pulling force of the UAV, is the lifting force of the UAV, is the drag force of the UAV, is the gravity of the UAV, is the climbing angle of the UAV, is the mass of the UAV, is the flight speed of the UAV, is the radius of curvature of the UAV's trajectory; Combining the above formulas, the climb rate is obtained as follows: Take the climb angle: The drag calculation expression Substitute into the climb rate Calculate the expression to obtain: Solve the above equation by the graphical method, that is, plot the as the independent variable and plot the function image. Within the speed boundary of the drone obtained in step 1.2, find the function image of the minimum speed corresponding point. The slope of the line connecting this point to the origin is the minimum climb angle ; the maximum climb angle is determined according to the actual load capacity of the drone.

5. The optimal power supply method for the distributed cooperative microwave-driven aerial platform according to claim 3, characterized in that: Step 2 specifically includes the following sub-steps: Step 2.1: First, establish the coordinate transformation relationship from the navigation axis system to the body axis system; the origin of the body axis system is located at the aircraft's center of gravity, the axis is located in the aircraft's symmetry plane, parallel to the fuselage axis, and points forward, the axis is located in the aircraft's symmetry plane and is perpendicular to the axis and points downward, the axis is perpendicular to the symmetry plane and points to the right, satisfying the right-hand system; the navigation axis system is the ground inertial coordinate system, and the origin of the coordinates is the absolute coordinate (0, 0, 0); Place the position of the base station emitting at the center of the array at the coordinate origin (0, 0, 0), and calculate the corresponding position coordinates of ground energy emission base stations according to the array distribution form and the spacing between base stations ; According to the initial position coordinates of the UAV , , , calculate the yaw angle of the UAV entering the field; Step 2.2: Starting from the initial moment, calculate the energy consumption of the UAV at each time step based on the position coordinates of the ground energy emission base station, the position coordinates of the UAV at the current time step, the roll angle, the pitch angle, and the yaw angle , specifically: Based on the position coordinates of the drone at the current time step and the position coordinates of ground energy emission base stations, obtain the normal vector of the energy irradiation surface: Let the initial body-axis system matrix of the UAV be: Then the transformed body-axis system matrix is: , , are respectively the transformation matrix between the navigation axis system and the body axis system for the direction, the transformation matrix for the direction, and the transformation matrix for the direction; For the airframe of the unmanned aerial vehicle If the plane is the receiving surface, then in the body axis system, the normal direction of the receiving surface is direction vector , and through transformation, the normal vector of the receiving surface at the current time step is obtained as follows: The effective energy reception area of the UAV for each ground energy transmission base station at the current time step is calculated using the following formula :[[]]END]] In the formula, is the maximum receiving energy area of the UAV; Furthermore, the following formula is used to calculate the reception efficiency of the energy emitted by each energy-emitting base station at the current time step : In the formula, is the initial output power of the th ground energy emission base station; Furthermore, the following formula is used to calculate the energy consumption of the UAV at the current time step In the formula, is the maximum capacity of the UAV battery; Step 2.3: Sum up the energy consumption of the UAV at each time step to obtain the total energy consumption during the entire UAV flight mission .

6. The optimal power supply method for the distributed cooperative microwave-driven aerial platform according to claim 5, characterized in that: The calculation methods for the position coordinates, roll angle, pitch angle, and yaw angle of the UAV at each time step in Step 2.2 are specifically as follows: ①According to the altitude of the UAV calculate the corresponding atmospheric density ; ② Calculate the thrust of the UAV at the current time step , lift and drag ; ③ Based on the forces acting on the UAV, calculate the acceleration of the UAV at the current time step; ④ Based on the acceleration of the UAV at the current time step, calculate the yaw angle, flight speed, and position coordinates of the UAV at the next time step; ⑤ Based on the energy consumption of the UAV at the current time step being the minimum, calculate the roll angle and pitch angle of the UAV at the next time step: Based on the roll angle at the current time step and the climb angle boundary calculated in Step 1.3, establish the optional intervals for the pitch angle and roll angle at the next time step according to the following formula: In the formula, , are the optional intervals of the roll angle and the climb angle at the next time step, is the set change amount of the roll angle; is the angle of attack of the UAV at the current time step. When , . When , . In the formula, is the climb angle of attack, is the cruise angle of attack; For and , randomly sample G samples from each of , to form a matrix of G G. Use the method described in step 2.2 to calculate the energy consumption of the drones corresponding to the G G combinations at the current time step and compare them. Select the minimum value corresponding to and and as the roll angle and pitch angle for the next time step.

7. The optimal power supply method for the distributed cooperative microwave-driven aerial platform according to claim 6, wherein: Calculate the thrust of the drone at the current time step , lift and drag The process is as follows: Let the flight speed , according to the power of the drone motor , calculate the pulling force of the drone at the current time step : Based on the zero-lift drag of the UAV among the initial parameters of the UAV state , the induction factor , the weight of the UAV , the wing area of the UAV and the air density corresponding to the current altitude , the flight speed , calculate the drag of the UAV at the current time step : The altitude of the UAV according to the current time , pitch angle , roll angle , based on the lift-drag ratio relationship of the UAV, the lift-drag ratio of the UAV calculated , and then calculate the lift of the UAV at the current time step : At the initial moment, the altitude , the pitch angle , the roll angle .

8. The optimal energy supply method for the distributed cooperative microwave-driven aerial platform according to claim 6, characterized in that: The process of calculating the acceleration of the UAV at the current time step based on the forces acting on the UAV is specifically: According to Newton's second law, the acceleration in the current time step of the UAV is as follows: In the formula, represents the longitudinal acceleration of the UAV, represents the lateral acceleration of the UAV, represents the vertical acceleration of the UAV; If the current flight speed exceeds the speed boundary of the UAV determined in Step 1.2, i.e., or at this time, the acceleration of the UAV at the current time step is regulated as follows: , , is the set acceleration control parameter.

9. The optimal power supply method for the distributed collaborative microwave-driven aerial platform according to claim 1, wherein: Step 3 specifically includes the following sub-steps: Step 3.1: Taking the distribution spacing of the ground energy transmission base stations and the initial position coordinates of the UAV as design variables, with the minimum total energy consumption during the entire flight mission as the optimization objective, setting the value range of the design variables, conducting Latin hypercube sampling within the set value range of the design variables to obtain a number of sample points; using the mathematical relationship established in Step 2 as the optimization objective evaluation method to calculate the sample points, obtaining the total energy consumption during the entire flight mission of each sample point, thereby obtaining a sample data set composed of a number of sample points and their corresponding total energy consumption; Step 3.2: Based on the obtained sample data set, establish a Kriging surrogate model, and then use an intelligent optimization algorithm to optimize based on the Kriging surrogate model to obtain the optimal values of the distribution spacing of the ground energy transmission base stations and the initial position coordinates of the UAV.

10. The optimal power supply method for the distributed cooperative microwave-driven aerial platform according to claim 1, characterized in that: Step 4 specifically includes the following sub-steps: Step 4.1: According to the distribution spacing of the ground energy transmission base stations and the optimal values of the initial position coordinates of the UAVs obtained in Step 3, return to Step 2 to perform the calculation of the entire flight mission process, and obtain the effective energy reception area of each UAV for each ground energy transmission base station at each time step , ; Step 4.2: Calculate the regulatory factor using the following formula :[[]]END]] In the formula, is the regulation factor of the th ground energy emission base station, is the mean value of the effective received energy area of the th ground energy emission base station under the traversal task time step, is the sum of the mean values of the effective received energy areas of the ground energy emission base stations under the traversal task time step; Step 4.3: Multiply the regulation factor obtained in Step 4.2 by the initial output power of each ground energy transmission base station to obtain the final output power of each ground energy transmission base station: In the formula, is the initial output power of the th ground energy emission base station, and is the final output power of the

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