A multi-umbrella wing unmanned aerial vehicle semi-physical simulation system

Abstract

The application discloses a kind of multiple umbrella wing unmanned aerial vehicle semi-physical simulation systems, including host computer and multiple umbrella wing unmanned aerial vehicle controllers;The host computer is connected with each umbrella wing unmanned aerial vehicle controller;The host computer includes path planning module, unmanned aerial vehicle state acquisition module and first communication module;The path planning module is used to carry out path planning to the formation of multiple umbrella wing unmanned aerial vehicle, and the expected path of each umbrella wing unmanned aerial vehicle is output;The unmanned aerial vehicle state acquisition module is used to calculate the update value of unmanned aerial vehicle state according to the control amount output by umbrella wing unmanned aerial vehicle controller and unmanned aerial vehicle dynamics model;The first communication module is used to send the expected path, the update value of unmanned aerial vehicle state to corresponding umbrella wing unmanned aerial vehicle controller;The umbrella wing unmanned aerial vehicle controller is used to calculate control amount according to expected path and unmanned aerial vehicle state.Utilize the system, multiple umbrella wing unmanned aerial vehicle cooperative air-drop system can be calibrated, and the function and performance of each equipment of system are more directly and effectively verified.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of multi-parawing unmanned aerial vehicle simulation, and particularly relates to a kind of multi-parawing unmanned aerial vehicle semi-physical simulation system. BACKGROUND

[0002] Parawing unmanned aerial vehicle, also known as unmanned powered parawing, is provided with lift by ram air parawing, thrust by brushless motor, and flight direction is controlled by rudder, which has large load and excellent control performance, and can solve the problem of long-distance and high-load material dropping. In order to meet the demand of large amount of material dropping, multiple parawing unmanned aerial vehicles are needed to be used for cooperative air drop, so the research on cooperative air drop of multiple parawing unmanned aerial vehicles is of great significance. At present, some documents have researched on the path planning or cooperative control of multiple parawing unmanned aerial vehicle formation, but how to calibrate the path planning or cooperative control and test the function and performance of each device is a problem to be solved in practice. SUMMARY

[0003] The present application provides a kind of multi-parawing unmanned aerial vehicle semi-physical simulation system, which can calibrate the cooperative air drop system of multiple parawing unmanned aerial vehicles, and more directly and effectively test the function and performance of each device of the system.

[0004] Technical scheme: the present application adopts the following technical scheme:

[0005] A kind of multi-parawing unmanned aerial vehicle semi-physical simulation system, comprising a host computer and multiple parawing unmanned aerial vehicle controllers; the host computer is connected with each parawing unmanned aerial vehicle controller;

[0006] The host computer comprises a path planning module, an unmanned aerial vehicle state acquisition module and a first communication module; the path planning module is used for path planning of the formation composed of multiple parawing unmanned aerial vehicles, and outputs the expected path of each parawing unmanned aerial vehicle; the unmanned aerial vehicle state acquisition module is used for calculating the update value of the unmanned aerial vehicle state according to the control quantity output by the parawing unmanned aerial vehicle controller and the unmanned aerial vehicle dynamics model; the first communication module is used for sending the expected path and the update value of the unmanned aerial vehicle state to the corresponding parawing unmanned aerial vehicle controller;

[0007] The parawing unmanned aerial vehicle controller is used for calculating the control quantity according to the expected path and the unmanned aerial vehicle state.

[0008] Further, the path planning module plans the path of the lead parawing unmanned aerial vehicle in the formation composed of multiple parawing unmanned aerial vehicles; the expected path of the follower parawing unmanned aerial vehicle in the formation is the sum of the expected path of the lead parawing unmanned aerial vehicle and the position bias of the follower parawing unmanned aerial vehicle; the position bias of the follower parawing unmanned aerial vehicle is the relative displacement from the lead parawing unmanned aerial vehicle to the follower parawing unmanned aerial vehicle in the formation.

[0009] Further, the path planning module converts the desired path into latitude and longitude coordinates using Gaussian inverse calculation and displays on the electronic map.

[0010] Further, the UAV state acquisition module inputs the control quantity into the parachute-wing UAV dynamics equation to obtain an updated value of the UAV state;

[0011] The control quantity includes a left rear edge down deflection amount δ Left and a right rear edge down deflection amount δ Right , a motor thrust Thrust;

[0012] The parachute-wing UAV dynamics equation is a 6-DOF model, specifically:

[0013]

[0014] where M is the total mass of a single parachute-wing UAV system, M F is an apparent mass matrix, is a wing-parachute-load system center of mass velocity, is a tangential acceleration; W b is a total gravity, W b1 is a parachute-wing UAV load gravity; and respectively represent aerodynamic forces acting on the parachute and the load; ω is a rotational angular velocity, is a rotational angular acceleration; Ω is an angular velocity cross product matrix, ω(1), ω(2), and ω(3) are respectively three components of the rotational angular velocity ω, is a normal acceleration;

[0015] X gp is a distance vector of the parachute center of mass, X gp (1), X gp (2), and X gp (3) are respectively three components of X gp ; I represents a parachute body inertia matrix; I F is an apparent inertia matrix; is an aerodynamic moment, V p is a parachute center of mass velocity, V p (1), V p (2), and V p (3) are respectively three components of V p ; and are both calculation intermediate variables, and the calculation formulae are as follows:

[0016]

[0017]

[0018] where rho is air density, S p is the canopy area, sign(·) is the sign function, respectively represent the lift and drag coefficients induced by the asymmetric downwash, respectively represent the lift and drag coefficients induced by the symmetric downwash. respectively represent the roll and yaw moment coefficients induced by the asymmetric downwash, b is the wing-parachute span length, and t is the wing-parachute thickness.

[0019] symmetric trailing edge deflection delta s =(delta Left +delta Right ) / 2, asymmetric trailing edge deflection delta a =delta Left -delta Right .

[0020] Further, the parachute-wing unmanned aerial vehicle controller adopts a double closed-loop PID flight control algorithm to calculate the control amount.

[0021] Further, the parachute-wing unmanned aerial vehicle controller adopts an ADRC algorithm to calculate the control amount.

[0022] Further, the host computer and the parachute-wing unmanned aerial vehicle controller are connected through a multi-port high-speed expansion dock.

[0023] Further, the path planning module adopts a segmented homing algorithm or an optimal homing planning algorithm to plan paths for a formation composed of multiple parachute-wing unmanned aerial vehicles.

[0024] Further, the parachute-wing unmanned aerial vehicle controller adopts an STM32F103C8T6 microprocessor chip as a processor chip.

[0025] Further, the multi-port high-speed expansion dock is a USB seven-port high-speed expansion dock.

[0026] Beneficial effects: The multiple-parachute-wing unmanned aerial vehicle semi-physical simulation system disclosed by the application can further calibrate the mathematical model of the multiple-parachute-wing unmanned aerial vehicle system air-drop system, and more directly and effectively verify the functions and performances of various devices in the system. BRIEF DESCRIPTION OF DRAWINGS

[0027] Figure 1 Fig. 1 is a connection schematic diagram of the multiple-parachute-wing unmanned aerial vehicle semi-physical simulation system in the embodiment;

[0028] Figure 2 Fig. 2 is a connection schematic diagram of various components of the host computer; and

[0029] Figure 3 a display diagram for planning a flight path;

[0030] Figure 4 a diagram of a double closed loop PID flight control algorithm;

[0031] Figure 5 a two-dimensional plan view and a three-dimensional view of the planned flight path;

[0032] Figure 6 a two-dimensional trajectory view and a three-dimensional trajectory view of the flight positions of the six parafoil UAVs in formation;

[0033] Figure 7 a control amount comparison diagram of the six sets of parafoil UAV controllers;

[0034] Figure 8 a formation position tracking error curve diagram. DETAILED DESCRIPTION

[0035] The application will be further illustrated below in combination with the accompanying drawings and specific embodiments.

[0036] The application discloses a multi-parafoil UAV semi-physical simulation system, and the formation of six parafoil UAVs is taken as an example to illustrate the simulation system in the embodiment, the system comprises a host computer and a plurality of parafoil UAV controllers; in the embodiment, there are six parafoil UAVs, so there are six parafoil UAV controllers, and the controllers correspond to the UAVs one by one. The host computer is connected with each parafoil UAV controller; in the embodiment, the host computer and the parafoil UAV controllers are connected through a multi-port high-speed docking station, specifically a USB seven-port high-speed docking station, the docking station adopts a GL3520 chip and is compatible with a USB3.0 protocol. Six COM ports in the docking station are connected with the six parafoil UAV controllers through a USB to TTL module, and another COM port is connected with the host computer; the connection mode is as shown in the figure. Figure 1 Figure 1 In the figure, UAV1-UAV6 are six parafoil UAV controllers, corresponding to six parafoil UAVs P1-P6. The six parafoil UAVs form a triangular formation, wherein P1 is a leading parafoil UAV, and the remaining five are following parafoil UAVs. The host computer has an interface to display the formation of the UAV formation.

[0037] As shown in the figure, Figure 2 ​As shown, the host computer includes a flight path planning module 101, a UAV state acquisition module 102, and a first communication module 103; the flight path planning module 101 is configured to plan a flight path for the formation composed of multiple parafoil UAVs, and output the expected flight path of each parafoil UAV; the UAV state acquisition module 102 is configured to calculate the updated value of the UAV state according to the control amount output by the parafoil UAV controller; and the first communication module 103 is configured to send the expected flight path and the updated value of the UAV state to the corresponding parafoil UAV controller.

[0038] In this embodiment, the flight path planning module plans a flight path for the lead parafoil UAV P1 in the formation composed of multiple parafoil UAVs; and the expected flight path of the follower parafoil UAVs P2-P5 in the formation is the sum of the expected flight path of the lead parafoil UAV and the position offset of the follower parafoil UAVs from the lead parafoil UAV. Thus, only the lead parafoil UAV needs to be planned, and the expected flight path of the remaining parafoil UAVs can be obtained by simply adding the relative displacement of the follower parafoil UAVs from the lead parafoil UAV to the expected flight path of the lead parafoil UAV.

[0039] In this embodiment, the flight path planning module 101 converts the expected flight path into latitude and longitude coordinates using Gauss forward and reverse calculation, and displays the expected flight path on an electronic map. As shown in Figure 3 As shown, the expected flight path is displayed on the Baidu map, and the expected flight path is composed of a series of flight path points. The circle in the northeast of the right upper corner of the figure is the starting position point, the UAV formation first flies straight in the southwest direction, then descends along a circular arc trajectory, and finally reaches the destination in the southwest direction in a straight line.

[0040] The flight path planning module plans a flight path for the formation composed of multiple parafoil UAVs using a segmented homing algorithm or an optimal homing planning algorithm. In this embodiment, the flight path planning module uses mixed programming of Qt and MATLAB, compiles the parafoil UAV flight path planning algorithm (m file) in MATLAB into a DLL file in C++ language, then adds the flight path planning algorithm DLL related LIB file in the Qt project, and finally calls the function in the DLL in Qt, and transmits the input and output parameters through the MwArray class. This method as a general method enables multiple planning algorithms to be implemented in Qt, facilitating testing and use in semi-physical simulation.

[0041] The umbrella-wing unmanned aerial vehicle controller is used to calculate control variables according to a desired flight path and an unmanned aerial vehicle state; the unmanned aerial vehicle state acquisition module 102 calculates an updated value of the unmanned aerial vehicle state according to the control variables output by the umbrella-wing unmanned aerial vehicle controller; the updated unmanned aerial vehicle state is sent to the umbrella-wing unmanned aerial vehicle controller to calculate new control variables, and the unmanned aerial vehicle state acquisition module calculates the updated value of the unmanned aerial vehicle state again, and thus the process is repeated until the unmanned aerial vehicle formation lands and the flight ends. In the embodiment, the umbrella-wing unmanned aerial vehicle controller uses an STM32F103C8T6 microprocessor chip as a processor chip.

[0042] The unmanned aerial vehicle state acquisition module 102 inputs the control variables into the umbrella-wing unmanned aerial vehicle dynamics equation to obtain an updated value of the unmanned aerial vehicle state; in the embodiment, the control variables include a left rear edge down deflection amount δ Left and a right rear edge down deflection amount δ Right , a motor thrust Thrust;

[0043] The umbrella-wing unmanned aerial vehicle dynamics equation can be based on a 6-degree-of-freedom model or a 9-degree-of-freedom model, and in the embodiment, is based on a 6-degree-of-freedom model, and specifically is as follows:

[0044]

[0045] where M is the total mass of a single umbrella-wing unmanned aerial vehicle system, M F is an apparent mass matrix, is a wing-parachute-load system center of mass velocity, is a tangential acceleration; W b is a total gravity, W b1 is an umbrella-wing unmanned aerial vehicle load gravity; and respectively represent aerodynamic forces acting on the parachute and the load; ω is a rotational angular velocity, is a rotational angular acceleration; Ω is an angular velocity cross product matrix, ω(1), ω(2), and ω(3) are respectively three components of the rotational angular velocity ω, is a normal acceleration;

[0046] X gp is a distance vector of the parachute center of mass, X gp (1), X gp (2), and X gp (3) are respectively three components of X gp ; I represents a parachute body inertia matrix; I F is an apparent inertia matrix; is an aerodynamic moment, V p is a parachute center of mass velocity, V p (1), Vp (2), V p (3) are three components, respectively; p and are all intermediate variables in calculation, and the calculation formula is as follows:

[0047]

[0048]

[0049] wherein p is air density, S p is canopy area, sign(·) is a sign function, respectively represent the lift coefficient and the drag coefficient induced by the asymmetric downwash, respectively represent the lift coefficient and the drag coefficient induced by the symmetric downwash; respectively represent the roll and yaw moment coefficients induced by the asymmetric downwash, b is the wing parachute span length, and t is the wing parachute thickness;

[0050] symmetric trailing edge deflection δ s = (δ Left + δ Right ) / 2, asymmetric trailing edge deflection δ a = δ Left - δ Right . When the control quantities δ Left , δ Right and Thrust are determined, the following can be calculated according to the dynamics equation: and are all three-dimensional data, and the position and the rotation angle of the parachute-wing unmanned aerial vehicle in translation can be obtained by twice integration of the six variables in , and then the three-dimensional coordinate values (X_r, Y_r, Z_r) of the unmanned aerial vehicle, the glide angle Mu_r, the heading angle Psi_r and the ground speed (the speed of the aircraft relative to the earth's surface in navigation) V_r can be obtained.

[0051] The parachute-wing unmanned aerial vehicle controller adopts a control algorithm to calculate the control quantities. In the embodiment, a double closed-loop PID flight control algorithm is adopted to calculate the control quantities, as shown in Figure 4 , which is divided into outer loop control and inner loop control; the outer loop control is responsible for tracking the position of the unmanned aerial vehicle, and the expected attitude angle is obtained through the guidance command and the limiting command, and the attitude angle includes the heading angle and the glide angle; the inner loop control adjusts the left trailing edge downwash δ Left and the right trailing edge downwash δ Rig h t ​, motor thrust Thrust to adjust the attitude of the UAV, using PID control algorithm, the yaw angle and glide angle of the UAV are adjusted, and finally the control amount of the rudder actuator is obtained by merging and calculating.

[0052] The UAV control amount, i.e. left rear edge down deflection δ Left and right rear edge down deflection δ Right , motor thrust Thrust is input into the UAV dynamics equation to obtain the updated state information of the UAV, the new state information is brought into the outer loop to obtain the error amount by subtracting the expected position, and the new state information is brought into the inner loop to obtain the attitude error amount by subtracting the expected value, and the UAV control state is made to converge by modifying the PID parameters.

[0053] The parachute wing UAV controller can also use ADRC algorithm to calculate the control amount, which also adjusts the heading angle and glide angle of the UAV by adjusting the left rear edge down deflection δ Left and right rear edge down deflection δ Right , motor thrust Thrust, so as to calculate the control amount.

[0054] The first communication module 103 sends data to the X parachute wing UAV controller, X = 1, 2, …, 6, and uses the following self-defined communication protocol. In this communication protocol, the first communication module 103 sends the expected track point (obtained from the planned track output by the track planning module 101), the updated value of the UAV state (output by the UAV state acquisition module 102), and the position information of other parachute wing UAVs in the formation to the X parachute wing UAV controller. The X parachute wing UAV controller returns the control amount of the X parachute wing UAV after calculation. In this embodiment, the first communication module 103 sends the data to be sent in two frames, waits for the X parachute wing UAV controller to respond between the two frames of data, and waits for the X parachute wing UAV controller to return the control amount of the X parachute wing UAV after the second frame of data is sent. The current communication ends.

[0055] The two frames of data sent by the first communication module 103 are as follows:

[0056] First frame:

[0057] #STATE1, <1>, <2>, <3>, <4>, <5>, <6>, <7>, … <9>, <10>, <11>, <12>

[0058] #STATE1 is a frame header, which is a first frame data identifier. Wherein <1>~<6> are the current expected track points of the X number of parafoil wing unmanned aerial vehicle, represented by (X_d, Y_d, Z_d, Mu_d, Psi_d, V_d), <7>~<12> are the state of the X number of parafoil wing unmanned aerial vehicle (X_r, Y_r, Z_r, Mu_r, Psi_r, V_r), which is output by the unmanned aerial vehicle state acquisition module. After receiving the first frame data, the X number of parafoil wing unmanned aerial vehicle controller will parse the first frame data, and verify its legality according to the frame header, and then send an acknowledgement signal to the first communication module.

[0059] Second frame:

[0060] #STATE2, <1>, <2>, <3>, <4>, <5>, <6>, <7>, … <29>, <30>.

[0061] #STATE2 is a frame header, which is a second frame data identifier. Wherein <1>~<3> are the expected formation (Formation) coordinate positions. <4>~<21> are the positions of six sets of parafoil wing unmanned aerial vehicles, and the position of each unmanned aerial vehicle is represented by three bytes, such as <4><5><6> are the coordinates X, Y, Z of the parafoil wing unmanned aerial vehicle numbered P1, and <19><20><21> are the coordinates X, Y, Z of the parafoil wing unmanned aerial vehicle numbered P6. After receiving the messages of other unmanned aerial vehicles, the parafoil wing unmanned aerial vehicle can realize the anti-collision and formation operation of the parafoil wing unmanned aerial vehicle. <22>~<24> are the center of mass velocity vector of the X number of parafoil wing unmanned aerial vehicle; <25>~<27> are the angular velocity vector of the X number of parafoil wing unmanned aerial vehicle in the parafoil body fixed coordinate system; <28>~<30> are the Euler attitude angles phi, theta, psi of the X number of parafoil wing unmanned aerial vehicle.

[0062] After receiving the second frame data, the parafoil wing unmanned aerial vehicle controller will parse the second frame data, verify its legality according to the frame header, integrate the first frame data and the second frame data into the control amount calculation program, and obtain the control amount of the left and right rudders and the motor to send to the upper computer.

[0063] This embodiment verifies whether the unmanned aerial vehicle formation cooperative control task can be completed, i.e. verifies the flight feasibility, by using the above-mentioned multi-parafoil wing unmanned aerial vehicle semi-physical simulation system. All the hardware used is the same as that of the actual flight test, and the sensor data format simulated by the semi-physical simulation platform is also in accordance with the data protocol adopted by the actual sensor, such as the simulated GPS data adopts the same NMEA protocol as the actual GPS sensor. The semi-physical simulation platform mainly consists of six STM32 parafoil wing unmanned aerial vehicle controllers, six sets of parafoil wing unmanned aerial vehicle models, six USB to TTL modules, rudder power supply system, USB seven-port high-speed splitter (USB3.0 GL3520), and DCDC module.

[0064] First, set the initial point and target point on the host computer, by running the trajectory planning program, planning a set of desired trajectory points, display the optimal planning trajectory information of the UAV; planning trajectory of two-dimensional plane and three-dimensional graph as shown in Figure 5 (a) and (b).

[0065] In the simulation process, the desired trajectory points are sent to the STM32 controller through serial communication. In the simulation environment, random wind interference is added to simulate the influence of air flow in the real atmospheric environment on the position and attitude of the parachute wing UAV. The STM32 controller program receives the data frame sent by the first communication module 103, which includes the desired trajectory points (obtained from the planned trajectory output by the trajectory planning module 101) and the updated value of the UAV state (output by the UAV state acquisition module 102), and then analyzes the data and sends it to the double closed-loop PID control program to calculate the control amount of the UAV. The control amount is output to the rudder and brushless motor, and at the same time, the control amount is uploaded to the host computer, which is input to the parachute wing UAV dynamics model by the UAV state acquisition module 102, to complete the state update of the current value, and save the state record for analysis after the simulation is completed. Figure 6 (a) and (b) are respectively the two-dimensional trajectory graph and the three-dimensional trajectory graph of the six parachute wing UAV formation flight positions.

[0066] By comparing the planned trajectory in Figure 5 with the six UAV formation flight trajectories in the semi-physical simulation system in Figure 6 , it can be preliminarily concluded that the STM32 parachute wing UAV controller has good cooperative control.

[0067] Figure 7 (a)-(f) in the figure are the glide angles generated by the control amounts calculated by the six sets of parachute wing UAV controllers, compared with the glide angle values in the simulation environment. It can be seen from the figure that the glide angles generated by the control amounts calculated by the six sets of parachute wing UAV controllers are basically consistent with the glide angle curves in the simulation environment, that is, the control amounts of the left and right rudders and the thrust motor calculated by the six sets of parachute wing UAV controllers can basically realize the control of the parachute wing UAV.

[0068] Figure 8 The position tracking error analysis results of the multi-parachute wing UAV formation are as follows: the position tracking error of three of the five follower parachute wing UAVs remains within 150 m, and the position tracking error of the remaining two follower parachute wing UAVs remains within about 200 m. In this embodiment, the planning trajectory range spanned in the simulation is about 2500 m, and it can be concluded that the STM32 parachute wing UAV controller installed on each set of parachute wing UAV basically realizes the cooperative control task of the multi-parachute wing UAV.

Claims

1. A hardware-in-the-loop simulation system for a multi-parachute unmanned aerial vehicle (UAV), characterized in that, It includes a host computer and multiple paraglider drone controllers; the host computer is connected to each paraglider drone controller; The host computer includes a trajectory planning module, a UAV status acquisition module, and a first communication module. The trajectory planning module is used to plan the trajectory of a formation composed of multiple paragliding UAVs and output the desired trajectory of each paragliding UAV. The UAV status acquisition module is used to calculate the updated value of the UAV status based on the control quantity output by the paragliding UAV controller and the dynamic equation of the paragliding UAV. The first communication module is used to send the desired trajectory and the updated value of the UAV status to the corresponding paragliding UAV controller. The paraglider-wing UAV controller is used to calculate control quantities based on the desired flight path and the UAV status. The UAV status acquisition module inputs the control quantity into the paraglider UAV dynamic equation to obtain the updated value of the UAV status; The control quantity includes the left trailing edge deflection of the paraglider UAV. and right posterior edge deflection Motor thrust ; The dynamic equations of the paraglider UAV are a 6-DOF model, specifically: in The total mass of a single paraglider unmanned aerial vehicle system, For the apparent quality matrix, For the center-of-gravity velocity of the parachute-load system, It is tangential acceleration; For total gravity, ; For the payload gravity of the paraglider drone; and These represent the aerodynamic forces acting on the canopy and the load, respectively. Let ω be the rotational angular velocity. It is the angular acceleration of rotation; The cross product matrix of angular velocities. ; , , They are the rotational angular velocities. The three components, Normal acceleration; , Let be the distance vector from the centroid of the umbrella canopy. , , They are respectively The three components; Represents the umbrella's inertia matrix; The apparent inertia matrix; For aerodynamic torque, , For the velocity of the umbrella canopy's center of mass, , , They are respectively The three components; and These are all intermediate variables in the calculation, and the calculation formulas are as follows: in air density, For the area of ​​the umbrella canopy, For symbolic functions, , These represent the lift coefficient and drag coefficient induced by the asymmetric downward deflection, respectively. , These represent the lift coefficient and drag coefficient induced by the symmetrical downward deflection, respectively; , These represent the roll and yaw moment coefficients induced by the asymmetric under-bias, respectively. To extend the parachute's span, It refers to the thickness of the parachute; Symmetrical trailing edge deflection Asymmetric trailing edge deflection .

2. The multi-parachute unmanned aerial vehicle (UAV) hardware-in-the-loop simulation system according to claim 1, characterized in that, The trajectory planning module performs trajectory planning for the lead paraglider drone in a formation of multiple paraglider drones; the expected trajectory of the follower paraglider drones in the formation is the sum of the expected trajectory of the lead paraglider drone and the position offset of the follower paraglider drones; the position offset of the follower paraglider drones is the relative displacement between the lead paraglider drone and the follower paraglider drones in the formation.

3. The multi-parachute unmanned aerial vehicle (UAV) hardware-in-the-loop simulation system according to claim 1, characterized in that, The trajectory planning module converts the desired trajectory into latitude and longitude coordinates using Gaussian forward and inverse calculations and displays it on an electronic map.

4. The multi-parachute unmanned aerial vehicle (UAV) hardware-in-the-loop simulation system according to claim 1, characterized in that, The paraglider UAV controller uses a dual-closed-loop PID flight control algorithm to calculate the control input.

5. The multi-parachute unmanned aerial vehicle (UAV) hardware-in-the-loop simulation system according to claim 1, characterized in that, The paragliding UAV controller uses the ADRC algorithm to calculate control quantities.

6. The multi-parachute unmanned aerial vehicle (UAV) hardware-in-the-loop simulation system according to claim 1, characterized in that, The host computer and the paraglider UAV controller are connected via a multi-port high-speed expansion dock.

7. The multi-parachute unmanned aerial vehicle (UAV) hardware-in-the-loop simulation system according to claim 1, characterized in that, The trajectory planning module uses a segmented homing algorithm or an optimal homing planning algorithm to plan the trajectory of a formation composed of multi-parachute UAVs.

8. The multi-parachute unmanned aerial vehicle (UAV) hardware-in-the-loop simulation system according to claim 1, characterized in that, The paragliding drone controller uses an STM32F103C8T6 microprocessor chip as its processor chip.

9. The hardware-in-the-loop simulation system for multi-parachute unmanned aerial vehicles according to claim 6, characterized in that, The multi-port high-speed expansion dock is a seven-port USB high-speed expansion dock.

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