Design of Hover Experiment Platform for Unmanned Aerial Vehicle and Frequency Domain Identification Method

Abstract

The present invention discloses a design of an unmanned aerial vehicle hovering experimental platform and a frequency domain identification method. The method provided by the present invention comprises: inputting a first logarithmic frequency sweep signal and a first dipole square wave signal to the unmanned aerial vehicle at the hovering point, collecting the output data of the unmanned aerial vehicle; preprocessing the output data to obtain input and output frequency response data, respectively obtaining the parameter values of the transfer function model and the parameter values of the state space model; and then performing time domain verification on the transfer function model and the state space model. The present invention utilizes the frequency domain identification method to decouple the data between axes in the frequency domain, and can select a suitable amount of data for identification within a reasonable frequency range, thereby improving computational efficiency and making up for the shortcomings of the time domain identification method.

Description

Technical Field

[0001] The present invention relates to the technical field of aircraft flight control and modeling, and in particular to a design of an unmanned aerial vehicle hovering experimental platform and a frequency domain identification method. Background Art

[0002] As the functions of quadcopters (UAVs) continue to improve and their mission scope continues to expand, higher requirements are being placed on their maneuverability in certain specific missions. Furthermore, the practical value of UAVs lies more in their automated control systems. The symmetrical platform structure of the UAVs also provides strong applicability for their payloads, allowing users to deploy a variety of payloads with different sizes and shapes on the UAV fuselage platform according to different needs. These diverse demands have led to the emergence of more advanced UAV controllers and unconventional aerodynamic configurations. To address these challenges, existing methods that require manual adjustment of controller parameters lack a theoretical basis. Furthermore, the conclusions and assumptions drawn from classic mechanism modeling and analytical modeling methods are not applicable to the new aerodynamic configurations mentioned above. Therefore, the traditional "modeling analysis-controller design" approach is unlikely to meet future demands in terms of development efficiency, modeling accuracy, and controller performance.

[0003] Therefore, it is necessary to design and verify a new method for identifying the hovering modal model of unmanned aerial vehicles, and ultimately provide a more reliable theoretical basis for related work on aircraft multimodal modeling. Summary of the Invention

[0004] In response to the above problems in the prior art, the present invention provides a design of an unmanned aerial vehicle hovering experiment platform and a frequency domain identification method.

[0005] To achieve the above objectives, in a first aspect, the present invention provides a frequency domain identification method for an unmanned aerial vehicle hovering experiment, comprising:

[0006] Inputting a first logarithmic frequency sweep signal and a first dipole square wave signal to the unmanned aerial vehicle at a hovering point, and collecting output data of the unmanned aerial vehicle;

[0007] Preprocessing the output data to obtain input-output frequency response data, inputting the input-output frequency response data into a transfer function model, and obtaining parameter values of the transfer function model based on the input-output frequency response data, a cost function not greater than a first threshold, and an error response function not greater than a second threshold;

[0008] The parameter values in the transfer function model are used as input values of the state-space model, and the parameter values of the state-space model are obtained according to the relative value of the Cramér–Rao bound that is not greater than a third threshold and the insensitivity that is not greater than a fourth threshold;

[0009] The unmanned aerial vehicle is controlled to perform a test flight according to the second logarithmic swept frequency signal, and the transfer function model and the state space model are verified in the time domain using the second dipole square wave signal to determine whether the matching error between the test flight output value and the state space model output value is no greater than a fifth threshold value.

[0010] If it is not greater than the fifth threshold, the transfer function model and the state space model are reliable.

[0011] Otherwise, the parameter values of the transfer function model and / or the state-space model are adjusted until the matching error is no greater than a fifth threshold.

[0012] In a second aspect, the present invention provides an unmanned aerial vehicle hovering experimental platform, comprising:

[0013] an acquisition module, configured to input a first logarithmic frequency sweep signal and a first dipole square wave signal to the unmanned aerial vehicle at a hovering point, and to acquire output data of the unmanned aerial vehicle;

[0014] a transfer function module configured to preprocess the output data to obtain input-output frequency response data, input the input-output frequency response data into a transfer function model, and obtain parameter values of the transfer function model based on the input-output frequency response data, a cost function not greater than a first threshold, and an error response function not greater than a second threshold;

[0015] a state-space module configured to use parameter values in the transfer function model as input values of the state-space model and obtain parameters of the state-space model based on a relative value of a Cramér–Rao bound that is no greater than a third threshold and an insensitivity that is no greater than a fourth threshold;

[0016] The time domain verification module is used to control the unmanned aerial vehicle to perform a test flight according to the second logarithmic sweep frequency signal, use the second dipole square wave signal to perform time domain verification on the transfer function model and the state space model, and determine whether the matching error between the test flight output value and the state space model output value is no greater than a fifth threshold value.

[0017] If it is not greater than the fifth threshold, the transfer function model and the state space model are reliable.

[0018] Otherwise, the parameter values of the transfer function model and / or the state-space model are adjusted until the matching error is no greater than a fifth threshold.

[0019] In a third aspect, the present invention provides a real-time simulation system for an unmanned aerial vehicle, which executes the frequency domain identification method for the unmanned aerial vehicle hovering experiment of the first aspect.

[0020] The unmanned aerial vehicle hovering experimental platform and frequency domain identification method of the present invention have the following beneficial effects:

[0021] (1) In the present invention, two signals are selected during the flight test and the test flight is completed in the same test, which can reduce the number of aircraft sorties, improve the efficiency of pilots in flight tests, reduce costs, and increase the effective closed-loop data;

[0022] (2) In the present invention, the frequency domain identification method can be used to decouple the data between axes in the frequency domain, and an appropriate amount of data can be selected for identification within a reasonable frequency range, which can improve the computational efficiency and make up for the shortcomings of the time domain identification method;

[0023] (3) In the present invention, the transfer function model and the state space model are verified in the time domain, and the verification results are more accurate and the verification efficiency can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a flow chart of a frequency domain identification method for an unmanned aerial vehicle hovering experiment provided by the first embodiment of the present invention;

[0025] Figure 2 This is a flow chart of an unmanned aerial vehicle hovering experiment platform provided by the second embodiment of the present invention;

[0026] Figure 3 is a frequency response curve diagram of input and output of an experimental example of the present invention;

[0027] Figure 4 This is a graph showing the verification results of the pitch channel space model of the experimental example of the present invention;

[0028] Figure 5a ) is a comparison diagram of the transfer function model data of the roll channel obtained from the actual test flight and the identification model of the unmanned aerial vehicle in the experimental example of the present invention;

[0029] Figure 5b ) is a comparison diagram of the transfer function model data of the pitch channel obtained from the actual test flight and the identification model of the unmanned aerial vehicle in the experimental example of the present invention;

[0030] Figure 5c ) is a comparison diagram of the transfer function model data of the yaw channel obtained from the actual test flight of the unmanned aerial vehicle in the experimental example of the present invention and the identification model;

[0031] Figure 5d ) is a comparison diagram of the transfer function model data of the altitude channel obtained by the actual test flight of the unmanned aerial vehicle in the experimental example of the present invention and the identification model. DETAILED DESCRIPTION

[0032] The preferred embodiments of the present invention are described in detail below with reference to the accompanying drawings so that the advantages and features of the present invention can be more easily understood by those skilled in the art, thereby making a clearer and more precise definition of the protection scope of the present invention.

[0033] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0034] Figure 1 This is a flowchart of a frequency domain identification method for an unmanned aerial vehicle hovering experiment provided in Example 1 of the present invention. This method can be applied to a terminal or server capable of data processing, such as a cloud or local exploration server, and is mainly used to determine the collection of input and output data during the unmanned aerial vehicle test flight.

[0035] Specifically, the method in this embodiment mainly includes:

[0036] Step S101: inputting a first logarithmic frequency sweep signal and a first dipole square wave signal to the UAV at a hovering point to collect output data of the UAV.

[0037] The flight test mainly involves testing the four control axes (pitch, roll, yaw, and collective pitch) of the UAV under hovering conditions. The pilot operates the remote control to make the UAV reach the specified flight conditions. When the UAV is balanced, the pilot triggers the preset signal to make the UAV execute the corresponding command.

[0038] In the present invention, frequency sweep input is performed on the four channels of the UAV respectively. When frequency sweep input is performed on one of the four channels, the pilot should fine-tune the input of other channels so that the other channels of the UAV are in a balanced position.

[0039] In the present invention, the logarithmic frequency sweep signal has no prior knowledge about the dynamic system to be identified, excites the system in a wide frequency range, and has nearly constant power in all frequencies.

[0040] Doublet square wave signals are simple and easy to implement in flight testing. They maximize data information and collect the required information within a short test time. These signals, composed of steps, differ significantly from logarithmic frequency sweeps, making them suitable for testing the predictive capabilities of identification models.

[0041] In a preferred embodiment of the present invention, the sweep frequency corresponding to the first logarithmic sweep frequency signal is 0.1-10 Hz, and / or the sweep frequency corresponding to the first dipole square wave signal is 1 Hz.

[0042] In the present invention, the server can use any method to obtain input signals and output data and further process this data. The data required for UAV flight testing includes: altitude and ground speed measured by GPS, acceleration and angular velocity measured by IMU, attitude quaternion measured by AHRS, barometric altitude, and the start and end flags of the excitation signal. For example, the user can directly import the required data, which the server can receive; alternatively, electronic devices other than the server can send the required data to the server, which can then receive it.

[0043] Step S102: preprocess the output data to obtain input-output frequency response data, input the input-output frequency response data into the transfer function model, and obtain parameter values of the transfer function model based on the input-output frequency response data, a cost function not greater than a first threshold, and an error response function not greater than a second threshold.

[0044] In the present invention, the test data of the UAV is used to identify the transfer function model, which can accurately describe the frequency response from the input to the output of the UAV within the frequency range of interest.

[0045] In a preferred embodiment of the present invention, the transfer function model of the pitch channel is obtained by formula 1:

[0046]

[0047] Where q represents the pitch angular rate; δ lon represents the pitch input, τ lon represents the equivalent time delay of the pitch channel, M lon The pitch moment of the UAV is used to determine the lon Find the partial derivative to obtain, M q represents pitch damping, ω lag is a constant obtained by the UAV motor, and s represents a fixed variable.

[0048] In a preferred embodiment of the present invention, the transfer function model of the roll channel is obtained by equation 3:

[0049]

[0050] Where p represents the roll angular rate; δ lat represents the roll input, τ lat represents the equivalent time delay of the roll channel, L latThe rolling moment lat Find the partial derivative to obtain, L P Indicates roll damping.

[0051] In a preferred embodiment of the present invention, the transfer function model of the yaw channel is obtained by formula 5:

[0052]

[0053] Where r represents the yaw rate, δ dir Indicates yaw input, N dir Denotes the yaw moment to yaw input δ dir Find the partial derivative, ω lead is a constant, representing the pole constant of the motor leading link, τ dir represents the equivalent time delay of the yaw channel, N r Indicates yaw damping.

[0054] In a preferred embodiment of the present invention, the transfer function model of the height channel is obtained by formula 7:

[0055]

[0056] Among them, a z represents the z-axis acceleration of the UAV, δ col Indicates the height input, Z col Indicates the force on the z-axis of the UAV relative to the height input δ col Find the partial derivative, τ col represents the equivalent time delay of the height channel, Z w Find the partial derivative of the force on the z-axis of the UAV with respect to w.

[0057] In the present invention, the denominator in the transfer function model is the control input and the numerator is the state output. From the above transfer function model, there is a control derivative M lon 、L lat 、N dir , Z col ; Damping derivative M q 、L P 、N r ; Stable derivative Z w , equivalent time delay τ lon , τ lat , τ dir , τ col During the identification process, the high-frequency gain of the transfer function, i.e., the control derivative of each axis, is first identified. Then, this parameter is fixed and the others are identified. Finally, the equivalent time delay is identified.

[0058] In the present invention, the indicators for evaluating the accuracy of the transfer function model are the cost function and the error response function. In the present invention, there is no specific limitation on the values of the first threshold and the second threshold, and those skilled in the art can set them according to actual conditions.

[0059] For example, if the first threshold is 100, then the cost function J ≤ 100, indicating that the identified transfer function model is within an acceptable accuracy range. Alternatively, if the first threshold is 50, then J ≤ 50, it is assumed that the frequency response data generated by the transfer function model is essentially the same as the response data directly obtained from the UAV test data.

[0060] In the present invention, the error response function is derived by fitting the envelope of the maximum unnoticed dynamic increment (MUAD) with frequency as the horizontal axis, as provided in the MIL-STD-1797 standard. If the second indicator, the flight data matching error response curve, falls within this curve range, the transfer function model's response is determined to be no significantly different from the actual flight response.

[0061] Step S103: using the parameter values in the transfer function model as input values of the state-space model, and obtaining the parameter values of the state-space model according to the relative value of the Cramér–Rao boundary not greater than a third threshold and the insensitivity not greater than a fourth threshold.

[0062] The transfer function model provides a clearer understanding of the model and its dynamic characteristics. However, when designing a control system, the transfer function is not sufficient; a state-space model of the system is also required. State-space models are more convenient for multi-input and multi-output systems, so a four-channel state-space model is required.

[0063] In a preferred embodiment of the present invention, the state space model of the pitch channel is obtained by formula 2:

[0064]

[0065] Among them, u represents the linear velocity of the UAV in the x-axis direction, q represents the pitch angular rate, θ represents the pitch angle, and X u The X axis force Fx of the unmanned aerial vehicle is obtained by taking the partial derivative of u, q By taking the partial derivative of Fx with respect to q, we can obtain X lon Through Fx to δ lon Find the partial derivative to obtain, M u The pitch moment of the UAV is obtained by taking the partial derivative of u, M q Pitch damping, represents the pitch input δ lon In a preferred embodiment of the present invention, the state space model of the roll channel is obtained by formula 4:

[0066]

[0067] Among them, v represents the linear velocity of the UAV in the y-axis direction, Y v The y-axis force F of the UAV y Taking partial derivative of v, we get Y p By F y Taking partial derivative of p, we get Y lat By F y δ lat Find the partial derivative to obtain, L v It is obtained by taking the partial derivative of the rolling moment with respect to v, where φ represents the rolling angle, represents the roll input δ lat The delayed information.

[0068] In a preferred embodiment of the present invention, the state space model of the yaw channel is obtained by formula 6:

[0069]

[0070] Where ψ represents the yaw angle, represents the yaw input δ dir The delayed information.

[0071] In a preferred embodiment of the present invention, the state space model of the height channel is obtained by formula (8):

[0072]

[0073] Wherein, w represents the linear velocity of the UAV in the z-axis direction, Represents the height input δ col The delayed information.

[0074] In the present invention, the indicator for evaluating the accuracy of the state space model is the relative value of the Cramér–Rao bound and insensitivity In the present invention, there is no specific limitation on the values of the third threshold and the fourth threshold, and those skilled in the art can set them according to actual conditions. For example, the third threshold is 20%, then When the average cost function J≤100, it indicates that the state space model identification result is highly reliable and the prediction accuracy is good; the fourth threshold value is 10%, then When , it can be considered that the identified state space model has good predictive ability.

[0075] Step S104: Control the UAV to perform a test flight according to the second logarithmic swept frequency signal, use the second dipole square wave signal to perform time domain verification on the transfer function model and the state space model, and determine whether the matching error between the test flight output value and the state space model output value is not greater than a fifth threshold.

[0076] If it is not greater than the fifth threshold, the transfer function model and the state space model are reliable.

[0077] Otherwise, the parameters of the transfer function model and / or the state-space model are adjusted until the matching error is no greater than a fifth threshold.

[0078] In a preferred embodiment of the present invention, the first logarithmic frequency sweep signal and the second logarithmic frequency sweep signal are the same frequency signal. Similarly, the second dipole square wave signal and the first dipole square wave signal are the same frequency signal.

[0079] It is worth noting that frequency domain verification can easily meet the matching requirements of test data and model data. However, important evaluations such as the accuracy and robustness of the identification model and the limitations of the linear model also need to be reflected through time domain verification. The process is to input different input signals into the actual flight and the model respectively to see how close the outputs of the two are. Among them, the evaluation index can be the matching error J rms , that is, the disturbance model response time history y and the disturbance flight data y data Minimize the matching error. In the present invention, there is no specific limit on the value of the fifth threshold, and those skilled in the art can set it according to actual conditions. For example, the fifth threshold ranges from 1 to 2, then the matching error J rms If the matching error J is within 1-2, it indicates that the prediction accuracy of the model is within an acceptable range and the model is reliable. rms If it is not within 1-2, continue to adjust the model parameters to make it valid. At this point, the entire identification process is completed.

[0080] exist Figure 1 In the embodiment 1 shown,

[0081] (1) Selecting two signals during flight testing and completing the test flight in the same test can reduce the number of aircraft sorties, improve the efficiency of pilots in flight testing, reduce costs, and increase the effective closed-loop data;

[0082] (2) The frequency domain identification method can be used to decouple the data between axes in the frequency domain, and the appropriate amount of data can be selected for identification within a reasonable frequency range, which can improve the computational efficiency and make up for the shortcomings of the time domain identification method;

[0083] (3) The transfer function model and state space model are verified in the time domain, and the verification results are more accurate and the verification efficiency can be improved.

[0084] Figure 2 This is a schematic diagram of an unmanned aerial vehicle hovering test platform provided in Example 2 of the present invention. The system can be applied to a terminal or server capable of data processing, such as a cloud or local exploration server, and is mainly used to determine the collection of input and output data during the unmanned aerial vehicle test flight. The system mainly includes:

[0085] An acquisition module 201 is configured to input a first logarithmic frequency sweep signal and a first dipole square wave signal to the UAV at a hovering point, and to acquire output data of the UAV;

[0086] a transfer function module 202 configured to preprocess the output data to obtain input-output frequency response data, input the input-output frequency response data into a transfer function model, and obtain parameter values of the transfer function model based on the input-output frequency response data, a cost function not greater than a first threshold, and an error response function not greater than a second threshold;

[0087] a state-space module 203 for using parameter values in the transfer function model as input values of the state-space model and obtaining parameter values of the state-space model based on a relative value of the Cramér–Rao bound that is no greater than a third threshold and an insensitivity that is no greater than a fourth threshold;

[0088] The time domain verification module 204 is used to control the UAV to perform a test flight according to the second logarithmic swept frequency signal, perform time domain verification on the transfer function model and the state space model using the second dipole square wave signal, and determine whether the matching error between the test flight output value and the state space model output value is no greater than a fifth threshold value.

[0089] If it is not greater than the fifth threshold, the transfer function model and the state space model are reliable.

[0090] Otherwise, the parameter values of the transfer function model and / or the state-space model are adjusted until the matching error is no greater than a fifth threshold.

[0091] The UAV hovering experiment platform provided by the present invention can be used to execute the UAV hovering experiment frequency domain identification method described in the above embodiment 1. Its implementation principle and technical effects are similar and will not be repeated here.

[0092] Preferably, the transfer function module, the state space module and the time domain verification module in the unmanned aerial vehicle hovering experimental platform of the present invention can be directly in hardware, in a software module executed by a processor, or in a combination of the two.

[0093] The software modules may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such that the processor can read information from and write information to the storage medium.

[0094] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic, discrete hardware components, or any combination thereof. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors combined with a DSP core, or any other such configuration. In the alternative, the storage medium may be integral to the processor. The processor and storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In the alternative, the processor and storage medium may reside as discrete components in the user terminal.

[0095] In a third embodiment of the present invention, a real-time simulation system for an unmanned aerial vehicle is provided. The real-time simulation system executes the frequency domain identification method for the unmanned aerial vehicle hovering experiment described in the first embodiment.

[0096] In a fourth embodiment of the present invention, a computer-readable storage medium stores computer instructions, and the computer instructions are operated to execute the frequency domain identification method for the UAV hovering experiment described in the first embodiment.

[0097] In embodiment five of the present invention, a program product includes a computer program, the computer program is stored in a readable storage medium, at least one processor can read the computer program from the readable storage medium, and at least one processor executes the computer program to perform the frequency domain identification method for the unmanned aerial vehicle hovering experiment described in embodiment one.

[0098] In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0099] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0100] Experimental Example 1

[0101] To calculate the dynamic model of a 6.5kg quadrotor drone, provide input signals to the quadrotor drone so that it can fly forward at a speed of 5m / s under hovering conditions, and calculate the output data of the quadrotor drone in real time. The quadrotor drone state x = [uvwpqr φ θ ψ] and the four control inputs u = [δ lat δ lon δ dir δ col ];

[0102] Using the input and output frequency response data, the frequency error response function is obtained, such as Figure 3 shown.

[0103] (1) y=[uqa x ](a x The transfer function model of the pitch channel is inputted by the x-axis acceleration of the aircraft system, which is measured by the airborne sensor, that is,

[0104]

[0105] Get the transfer function model parameters of the pitch channel: M lon =101.9 ω lag =24 M q =-1.2 τ lon =-0.046; cost function J = 15.501.

[0106] Substitute the identified parameters into the state space model of the pitch channel, that is, in Equation 2,

[0107]

[0108] The time domain verification results of the state space model are as follows Figure 4 As shown, after calculation, the average cost function value is: J ave =24.7871, and the values of each parameter are obtained. Value and insensitivity

[0109]

[0110] Compare the input signal with the actual test flight data, J rms =0.8468, which is much smaller than the fifth threshold 1-2, indicating that the state space model is reliable.

[0111] (2) y=[vpa y ](a y The transfer function model of the roll channel is inputted by the y-axis acceleration of the aircraft system, which is measured by the airborne sensor, that is,

[0112]

[0113] Get the transfer function model parameters of the roll channel: L lat =124.672 ω lag =24 L P =-1.8 τ lat =0.0433; cost function J = 15.868.

[0114] Substitute the identified parameters into the state space model of the roll channel, that is,

[0115]

[0116] After calculation, the average cost function value is: J ave =59.8810, and the CR of each parameter is obtained i Value and insensitivity I i :

[0117]

[0118] Compare the input signal with the actual test flight data, J rms =1.76, indicating that the state space model is reliable.

[0119] (3) y=[r ψ] is input to the transfer function model of the yaw channel, that is,

[0120]

[0121] Get the transfer function model parameters of the yaw channel: N dir =12.01 N r =-0.3188 ω lag =24 τ dir =0.042; cost function J = 49.601.

[0122] Substitute the identified parameters into the state space model of the yaw channel, that is, in Equation 6,

[0123]

[0124] After calculation, the average cost function value is: J ave =45.3683, and the CR of each parameter is obtained i Value and insensitivity I i :

[0125]

[0126] Compare the input signal with the actual test flight data, J rms =0.0288, indicating that the state space model is reliable.

[0127] (4) y=[a z w] input to the transfer function model of the height channel, that is,

[0128]

[0129] Get the transfer function model parameters of the height channel: Z col =-25.7 Z w =-0.586 ω lag =24 τ col =0.034; cost function J = 11.73.

[0130] Substitute the identified parameters into the state space model of the height channel, that is, in Equation 8,

[0131]

[0132] After calculation, the average cost function value is: J ave =23.11353, and the CR of each parameter is obtained i Value and insensitivity I i :

[0133]

[0134] Compare the input signal with the actual test flight data, J rms =0.149, indicating that the state space model is reliable.

[0135] Experimental Example 2

[0136] Input a new set of input signals to the quadrotor drone to obtain the flight data detected by the corresponding sensors on the quadrotor drone. Then input the new input signals into the transfer function model and state space model obtained according to Experimental Example 1 to obtain the corresponding model data. Compare the flight data and model data. Figure 5a -d) as shown, Figure 5a ) is a comparison diagram of the transfer function model data of the roll channel of the experimental example of the present invention; Figure 5b ) is a comparison diagram of the transfer function model data of the pitch channel of the experimental example of the present invention; Figure 5c ) is a comparison diagram of the transfer function model data of the yaw channel of the experimental example of the present invention; Figure 5d ) is a comparison diagram of the transfer function model data of the height channel of the experimental example of the present invention.

[0137] The calculated matching error Jrms = 0.531, which is less than 1, further demonstrating that the model prediction is accurate. Therefore, the transfer function model and state-space model obtained in Experimental Example 1 can truly reflect the flight response of the quadrotor drone at the hovering point.

[0138] The present invention has been described in detail above with reference to specific embodiments and exemplary examples. However, these descriptions are not to be construed as limiting the present invention. Those skilled in the art will appreciate that, without departing from the spirit and scope of the present invention, various equivalent substitutions, modifications, or improvements may be made to the technical solutions and implementations of the present invention, all of which fall within the scope of the present invention.

Claims

1. A frequency domain identification method for unmanned aerial vehicle hovering experiment, characterized in that: include: Inputting a first logarithmic frequency sweep signal and a first dipole square wave signal to the unmanned aerial vehicle at a hovering point, and collecting output data of the unmanned aerial vehicle; preprocessing the output data to obtain input-output frequency response data, inputting the input-output frequency response data into a transfer function model, and obtaining parameter values of the transfer function model based on the input-output frequency response data, a cost function that is no greater than a first threshold, and an error response function that is no greater than a second threshold; Using the parameter values in the transfer function model as input values of a state-space model, and obtaining the parameter values of the state-space model based on a relative value of a Cramér–Rao bound that is no greater than a third threshold and an insensitivity that is no greater than a fourth threshold; controlling the unmanned aerial vehicle to perform a test flight according to the second logarithmic swept frequency signal, performing time domain verification on the transfer function model and the state space model using the second dipole square wave signal, and determining whether a matching error between an output value of the test flight and an output value of the state space model is no greater than a fifth threshold; If it is not greater than the fifth threshold, the transfer function model and the state space model are reliable. Otherwise, the parameter values of the transfer function model and / or the state-space model are adjusted until the matching error is no greater than the fifth threshold.

2. The frequency domain identification method for the UAV hovering experiment according to claim 1 is characterized in that: The transfer function model of the pitch channel is obtained by formula 1: Where q represents the pitch angular rate; δ lon represents the pitch input, τ lon represents the equivalent time delay of the pitch channel, M lon The pitch moment of the UAV is used to determine the lon Find the partial derivative to obtain, M q represents pitch damping, ω lag is a constant obtained by the UAV motor, and s is a fixed variable; The state space model of the pitch channel is obtained by formula 2: Among them, u represents the linear velocity of the UAV in the x-axis direction, q represents the pitch angular rate, θ represents the pitch angle, and X u The X axis force Fx of the unmanned aerial vehicle is obtained by taking the partial derivative of u, q By taking the partial derivative of Fx with respect to q, we can obtain X lon Through Fx to δ lon Find the partial derivative to obtain, M u The pitch moment of the UAV is obtained by taking the partial derivative of u, M q Pitch damping, represents the pitch input δ lon The delayed information.

3. The frequency domain identification method for the UAV hovering experiment according to claim 1 is characterized in that: The transfer function model of the roll channel is obtained by equation 3: Where p represents the roll angular rate; δ lat represents the roll input, τ lat represents the equivalent time delay of the roll channel, L lat The rolling moment lat Find the partial derivative to obtain, L P represents the roll damping; The state space model of the roll channel is obtained by equation 4: Among them, v represents the linear velocity of the UAV in the y-axis direction, Y v The y-axis force F of the UAV y Taking partial derivative of v, we get Y p By F y Taking partial derivative of p, we get Y lat By F y δ lat Find the partial derivative to obtain, L v It is obtained by taking the partial derivative of the rolling moment with respect to v, where φ represents the rolling angle, represents the roll input δ lat The delayed information.

4. The frequency domain identification method for the UAV hovering experiment according to claim 1 is characterized in that: The transfer function model of the yaw channel is obtained by formula 5: Where r represents the yaw rate, δ dir Indicates yaw input, N dir Denotes the yaw moment to yaw input δ dir Find the partial derivative, ω lead is the zero-point constant of the motor leading link, τ dir represents the equivalent time delay of the yaw channel, N r represents yaw damping; The state space model of the yaw channel is obtained by equation 6: Where ψ represents the yaw angle, represents the yaw input δ dir The delayed information.

5. The frequency domain identification method for the UAV hovering experiment according to claim 1 is characterized in that: The transfer function model of the height channel is obtained by equation 7: Among them, a z represents the z-axis acceleration of the UAV, δ col Indicates the height input, Z col Indicates the force on the z-axis of the UAV relative to the height input δ col Find the partial derivative, τ col represents the equivalent time delay of the height channel, Z w Find the partial derivative of the force on the z-axis of the UAV with respect to w; The state space model of the height channel is obtained by formula 8: Wherein, w represents the linear velocity of the UAV in the z-axis direction, Represents the height input δ col The delayed information.

6. The frequency domain identification method for UAV hovering experiment according to claim 1, characterized in that: The sweep frequency corresponding to the first logarithmic sweep signal is 0.1-10 Hz.

7. The frequency domain identification method for UAV hovering experiment according to claim 1 is characterized in that: The sweep frequency corresponding to the first dipole square wave signal is 1 Hz.

8. An unmanned aerial vehicle hovering test platform, characterized in that: include: an acquisition module, configured to input a first logarithmic frequency sweep signal and a first dipole square wave signal to the UAV at a hovering point, and acquire output data of the UAV; a transfer function module configured to preprocess the output data to obtain input-output frequency response data, input the input-output frequency response data into a transfer function model, and obtain parameter values of the transfer function model based on the input-output frequency response data, a cost function not greater than a first threshold, and an error response function not greater than a second threshold; a state-space module configured to use the parameter values in the transfer function model as input values of the state-space model and obtain the parameter values of the state-space model based on a relative value of the Cramér–Rao bound that is no greater than a third threshold and an insensitivity that is no greater than a fourth threshold; a time domain verification module, configured to control the unmanned aerial vehicle to perform a test flight according to the second logarithmic swept frequency signal, perform time domain verification on the transfer function model and the state space model using the second dipole square wave signal, and determine whether a matching error between a test flight output value and a state space model output value is no greater than a fifth threshold value; If it is not greater than the fifth threshold, the transfer function model and the state space model are reliable. Otherwise, the parameter values of the transfer function model and / or the state-space model are adjusted until the matching error is no greater than the fifth threshold.

9. The unmanned aerial vehicle hovering experimental platform according to claim 8, characterized in that: The sweep frequency corresponding to the first logarithmic sweep signal is 0.1-10 Hz.

10. A real-time simulation system for unmanned aerial vehicles, characterized in that: The real-time simulation system executes the frequency domain identification method for the unmanned aerial vehicle hovering experiment described in any one of claims 1-7.

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