Method and system for optimizing rack torque tension of unmanned aerial vehicle power system

Abstract

The invention relates to an unmanned aerial vehicle power system rack torque tension optimization method and system, and relates to the field of unmanned aerial vehicle testing, and the method comprises the steps: carrying out the testing processing according to a plurality of preset testing conditions, and obtaining a plurality of tension coefficients and a plurality of torque coefficients; performing test stability analysis according to a plurality of preset test conditions to obtain a plurality of test stability parameters; testing to obtain a first test tensile force, a first test torque and a first test rotating speed of the unmanned aerial vehicle under the extreme test condition, and calculating to obtain a second estimated tensile force and a second estimated torque according to the second estimated rotating speed, the plurality of test stability parameters, the plurality of tensile force coefficients and the plurality of torque coefficients; and based on the control parameters of the unmanned aerial vehicle, calculating to obtain a third estimated tension and a third estimated torque, and combining the first test tension, the first test torque, the second estimated tension and the second estimated torque to calculate to obtain an optimized estimated tension and an optimized estimated torque. The technical effect of improving the accuracy of the tension torque test of the unmanned aerial vehicle is achieved.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle testing, and in particular to a method and system for optimizing the torque and tension of a test bench of a power system of an unmanned aerial vehicle. Background Art

[0002] With the promotion of low-altitude economic industries such as drone logistics, the promotion of drones is developing towards large loads. The larger the drone blades, the greater the tension and torque generated. When testing drones, the test bench structure vibrates due to the large tension and torque, resulting in large measurement errors of the torque sensor under large tension conditions, resulting in inaccurate tension and torque measurements of large-load drones, and the inability to obtain accurate data. Summary of the invention

[0003] The present invention aims to solve the technical problem of inaccurate tension and torque testing of large-load UAVs in the prior art, and provides a method and system for optimizing the torque and tension of a UAV power system bench.

[0004] The technical solution of the present invention to solve the above technical problems is as follows:

[0005] In a first aspect, the present invention provides a method for optimizing the torque and tension of a UAV power system test bench, comprising:

[0006] According to multiple preset test conditions, the UAV is tested using a test bench to obtain multiple tension coefficients and multiple torque coefficients;

[0007] Performing a test stability analysis according to the plurality of preset test conditions to obtain a plurality of test stability parameters;

[0008] The test obtains a first test tension, a first test torque and a first test speed of the UAV under extreme test conditions, and calculates a second estimated tension and a second estimated torque according to a second estimated speed, a plurality of test stability parameters, a plurality of tension coefficients and a plurality of torque coefficients;

[0009] Based on the control parameters of the drone, a third estimated tension and a third estimated torque are calculated, and the optimized estimated tension and the optimized estimated torque are calculated by combining the first test tension, the first test torque, the second estimated tension, the second estimated torque, the third estimated tension and the third estimated torque.

[0010] In a second aspect, the present invention provides a UAV power system test bench torque and tension optimization system, comprising:

[0011] A multi-condition test module is used to test the UAV using a test bench according to multiple preset test conditions, and process multiple tension coefficients and multiple torque coefficients;

[0012] A test stability analysis module, used to perform test stability analysis according to the plurality of preset test conditions to obtain a plurality of test stability parameters;

[0013] An extreme test module, used to test and obtain a first test tension, a first test torque and a first test speed of the UAV under extreme test conditions, and calculate and obtain a second estimated tension and a second estimated torque according to a second estimated speed, a plurality of test stability parameters, a plurality of tension coefficients and a plurality of torque coefficients;

[0014] The test optimization calculation module is used to calculate a third estimated tension and a third estimated torque based on the control parameters of the UAV, and to calculate an optimized estimated tension and an optimized estimated torque by combining the first test tension, the first test torque, the second estimated tension, the second estimated torque, the third estimated tension and the third estimated torque.

[0015] In a third aspect, the present invention provides an electronic device, including:

[0016] Memory for storing computer software programs;

[0017] The processor is used to read and execute the computer software program, thereby implementing the UAV power system test bench torque tension optimization method described in the first aspect.

[0018] In a fourth aspect, the present invention provides a computer-readable storage medium, in which a computer software program is stored. When the computer program is executed by a processor, the method for optimizing the torque and tension of the UAV power system test bench as described in the first aspect is implemented.

[0019] The beneficial effects of the present invention are as follows: a tension and torque optimization method for a UAV power system bench is proposed, the UAV is tested by multiple preset test conditions, multiple tension coefficients and torque coefficients are collected and processed, and the test stability analysis is performed in combination with historical test data, and multiple test stability parameters are calculated to ensure the reliability of the test results. In the test, by measuring the tension, torque and speed of the UAV under extreme working conditions, combined with the test stability parameters, tension coefficient and torque coefficient, the second estimated tension and the second estimated torque are further calculated. In addition, based on the control parameters of the UAV, the third estimated tension and the third estimated torque are calculated, and combined with the tension and torque of each stage, the optimized estimated tension and estimated torque are finally calculated. This solution effectively solves the measurement error problem caused by vibration during the test of a large-load UAV, greatly improves the test accuracy and stability, and provides high-quality test data support for the performance optimization and design improvement of the UAV, laying a technical foundation for the development of the low-altitude economic industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1A schematic diagram of the process flow of the torque and tension optimization method for the UAV power system bench provided by the present invention;

[0021] Figure 2 A schematic diagram of the structure of the torque and tension optimization of the UAV power system provided by the present invention;

[0022] Figure 3 A schematic diagram of the structure of an electronic device provided by the present invention;

[0023] Figure 4 A schematic diagram of the structure of a computer-readable storage medium provided by the present invention.

[0024] In the accompanying drawings, the components represented by the reference numerals are described as follows:

[0025] Multi-condition testing module 11, testing stability analysis module 12, extreme testing module 13, testing optimization calculation module 14, electronic device 500, memory 510, processor 520, first computer program 511, computer-readable storage medium 600, second computer program 611. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0027] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0028] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or explanation". Any embodiment described as "for example" in the present invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present invention.

[0029] Embodiment 1:

[0030] like Figure 1 As shown, an embodiment of the present invention provides a method for optimizing the torque and tension of a UAV power system test bench, comprising:

[0031] S10: According to a plurality of preset test conditions, the UAV is tested using a test bench to obtain a plurality of tension coefficients and a plurality of torque coefficients;

[0032] In the embodiment of the present application, when the UAV is tested under high tension in the test bench, the test bench structure is affected by the high tension and high torque and vibrates, resulting in a large measurement error of the torque sensor under high tension conditions, affecting the test accuracy. When the UAV is tested under medium tension conditions in the test bench, the test bench structure is stable and the test accuracy is high. Therefore, the test bench is used to test the UAV under medium tension conditions, and based on relatively accurate tests, multiple tension coefficients and multiple torque coefficients are obtained through processing and calibration, so as to perform subsequent accurate calculations of large tension and large torque.

[0033] Step S10 in the method provided in the embodiment of the present application includes:

[0034] Under the first preset test condition, the UAV is tested using a test bench to obtain a first condition test tension, a first condition test torque and a first condition test speed, and a first tension coefficient and a first torque coefficient are obtained by processing and calculating;

[0035] Continue to test the drone according to multiple preset test conditions to obtain multiple tension coefficients and multiple torque coefficients.

[0036] The embodiment of the present application uses a phased, multi-condition testing method to accurately measure and optimize the tension and torque of the UAV power system test bench.

[0037] Among them, firstly, under the first preset test condition (for example, the test condition under the medium tension condition, under which the test bench structure is relatively stable and the vibration effect is small), the test bench is used to test the drone to obtain the first condition test tension (for example, 50N), the first condition test torque (for example, 20Nm) and the first condition test speed (for example, 3000rpm).

[0038] Based on the first condition test tension, the first condition test torque and the first condition test speed, the first tension coefficient and the first torque coefficient are processed and calculated. The tension coefficient and the torque coefficient are proportional factors reflecting the relationship between the tension and torque and the square of the speed, and are used to describe the change law of the tension and torque of the UAV at different speeds.

[0039] In the embodiment of the present application, under the first preset test condition, the test bench is used to test the drone, and the first condition test tension, the first condition test torque and the first condition test speed are obtained, and the first tension coefficient and the first torque coefficient are obtained by processing and calculating, including:

[0040] Under the first preset test condition, the UAV is tested using a test bench to obtain a first condition test tension, a first condition test torque, and a first condition test speed;

[0041] According to the first condition test tension, the first condition test torque and the first condition test speed, the first tension coefficient and the first torque coefficient are calculated and obtained as follows:

[0042] G=K G *V 2 ;

[0043] T=K T *V 2 ;

[0044] Among them, G is the test tension, T is the test torque, V is the test speed, K is the test speed G is the tension coefficient, K T is the torque coefficient.

[0045] In the embodiment of the present application, under the first preset test condition (for example, a medium tension condition, such as a tension of 50N, a test bench structure is stable, and the vibration is small), the test bench is used to test the UAV. During the test, key parameters such as the first condition test tension (such as the measured tension is 50N), the first condition test torque (such as the measured torque is 20Nm) and the first condition test speed (such as the measured speed is 3000rpm) are collected by precision sensors. The first condition test tension, the first condition test torque and the first condition test speed reflect the power performance of the UAV under specific working conditions and are the basis for subsequent calculations.

[0046] According to the first condition test tension, the first condition test torque and the first condition test speed obtained in the test, the first tension coefficient and the first torque coefficient are calculated and obtained as follows:

[0047] G=K G *V 2 ;

[0048] T=K T *V 2 ;

[0049] Where G is the test tension, such as 50N, T is the test torque, such as 20Nm, V is the test speed, such as 3000rpm, K G is the tensile coefficient, for example, the calculated value is 5.56×10 -6 , K T is the torque coefficient, for example, it is calculated as 2.22×10 -6 .

[0050] Furthermore, the UAV is tested under other multiple preset test conditions, and multiple tension coefficients and multiple torque coefficients are obtained by calculating and processing based on the test data according to the above formula. The testing and calculation methods are the same as the processing steps of the first tension coefficient and the first torque coefficient.

[0051] Exemplarily, multiple preset test conditions are all test conditions near the medium tension of the drone. For example, under the five preset test conditions of 40N, 45N, 50N, 55N, and 60N, the drone is tested for conditional test tension, conditional test torque, and conditional test speed, and multiple tension coefficients and multiple torque coefficients are calculated and obtained.

[0052] The tension coefficient and torque coefficient obtained by the above calculation can accurately reflect the dynamic characteristics of the UAV under the test conditions, and provide important parameter support for the subsequent optimization calculation of large tension and large torque conditions. The above steps effectively utilize the high-precision data of the test bench under medium tension conditions, avoid the influence of vibration on measurement accuracy, and significantly improve the reliability of the overall test and calculation.

[0053] S20: performing a test stability analysis according to the plurality of preset test conditions to obtain a plurality of test stability parameters;

[0054] In the embodiment of the present application, multiple preset test conditions are test conditions with less vibration of the test bench structure and more accurate test conditions, but there are also certain errors. Therefore, a test stability analysis is performed on multiple preset test conditions to evaluate the consistency and fluctuation of the test results of the drone in multiple tests under different preset test conditions, thereby improving the reliability of the data. In this way, multiple test stability parameters of multiple preset test conditions are obtained as the basis for subsequent accurate calculation of the test tension and torque of the drone under large tension.

[0055] Step S20 in the method provided in the embodiment of the present application includes:

[0056] According to historical test data of similar UAVs under the first preset test condition, obtaining a historical first set of tension coefficients and a historical first set of torque coefficients;

[0057] Performing test stability calculation and analysis according to the historical first tension coefficient set and the historical first torque coefficient set to obtain a first test stability parameter;

[0058] According to the historical test data of the plurality of preset test conditions, a test stability calculation and analysis is performed to obtain a plurality of test stability parameters.

[0059] In the embodiment of the present application, in order to ensure the stability of the UAV power system bench test and the accuracy of the data, the stability of the UAV test data under each preset test condition is evaluated, thereby optimizing the measurement accuracy of tension and torque.

[0060] Based on the first preset test condition, historical test data of similar drones under this condition are collected. These historical data usually come from past experimental records or standard test data of similar models of drones. Specifically, the historical test data includes the first tension coefficient set and the first torque coefficient set obtained in multiple tests. The first tension coefficient set and the first torque coefficient set reflect the dynamic performance of the drone under the first preset test condition and are the basis for stability analysis.

[0061] According to the historical first tension coefficient set and the historical first torque coefficient set, a test stability calculation and analysis is performed, for example, the average fluctuation amplitude of the historical first tension coefficient and the historical first torque coefficient in the historical first tension coefficient set and the historical first torque coefficient set is analyzed to obtain the first test stability parameter.

[0062] In the embodiment of the present application, a test stability calculation and analysis is performed according to the historical first tension coefficient set and the historical first torque coefficient set to obtain a first test stability parameter, including:

[0063] Randomly selecting multiple groups of historical first tension coefficients from the historical first tension coefficient set, calculating the difference amplitude, and obtaining multiple historical first tension difference amplitudes;

[0064] Calculating an average of the plurality of historical first tension difference amplitudes, and subtracting the average from 1 to obtain a first tension test stability parameter;

[0065] Randomly selecting multiple groups of historical first torque coefficients from the historical first torque coefficient set, calculating the difference amplitude, and obtaining multiple historical first torque difference amplitudes;

[0066] Calculating an average of the plurality of historical first torque difference amplitudes, and subtracting the average from 1 to obtain a first torque test stability parameter;

[0067] The average of the first tension test stability parameter and the first torque test stability parameter is calculated to obtain a first test stability parameter.

[0068] In the embodiment of the present application, first, multiple groups of historical first tension coefficients are randomly selected from the historical first tension coefficient set, and each group of historical first tension coefficients includes two randomly selected historical first tension coefficients.

[0069] Furthermore, the fluctuation of each group of historical first tension coefficients is evaluated by calculating the difference amplitude between them. For example, the absolute value of the difference between two historical first tension coefficients is calculated, and the ratio of the absolute value to the larger historical first tension coefficient of the two historical first tension coefficients is calculated as the historical first tension difference amplitude. In this way, according to multiple groups of historical first tension coefficients, the difference amplitude of each group of historical first tension coefficients is calculated to obtain multiple historical first tension difference amplitudes.

[0070] For example, the two historical first tension coefficients are 5.56×10 -6 and 5.70×10 -6 The calculated first tension difference in history is 0.14×10 -6 Compared with 5.70×10 -6 , which is 2.5%.

[0071] Furthermore, the mean of multiple historical first tension difference amplitudes is calculated as the average historical first tension difference amplitude, which reflects the average difference amplitude within the historical first tension coefficient set. The first tension test stability parameter is obtained by subtracting 1 from the mean. The smaller the tension difference amplitude, the more stable the test data of the same type of drone under the first preset test conditions, and the larger the first tension test stability parameter.

[0072] Furthermore, in the same manner, multiple groups of historical first torque coefficients are randomly selected from the historical first torque coefficient set, and the difference amplitude is calculated to obtain multiple historical first torque difference amplitudes. Then, the average of the multiple historical first torque difference amplitudes is calculated, and the first torque test stability parameter is obtained by subtracting the average from 1.

[0073] Furthermore, in order to obtain a comprehensive test stability result, the first tension test stability parameter and the first torque test stability parameter are comprehensively calculated. Specifically, the average of the two is calculated to obtain the average stability parameter of the tension and torque test of the drone under the first preset test condition, that is, the first test stability parameter.

[0074] Based on the same method as in the foregoing content, test stability calculation and analysis is performed according to historical test data of other multiple preset test conditions to obtain multiple test stability parameters.

[0075] The embodiment of the present application performs test stability calculation and analysis based on historical test data of multiple preset test conditions to obtain multiple test stability parameters, providing a unified standard for subsequent optimization calculations, helping to further improve the accuracy and reliability of the UAV power system under different test conditions. Through these steps, the stability of tension and torque during the test process can be effectively evaluated, thereby providing more accurate and reliable data support for subsequent optimization calculations.

[0076] S30: testing and obtaining a first test tension, a first test torque and a first test speed of the drone under extreme test conditions, and calculating and obtaining a second estimated tension and a second estimated torque according to a second estimated speed, a plurality of test stability parameters, a plurality of tension coefficients and a plurality of torque coefficients;

[0077] In the embodiment of the present application, the extreme test condition is the test condition of maximum tension, under which the vibration of the test bench structure is the most serious and the test error is the largest. In order to obtain the tension and torque of the drone under extreme test conditions (i.e., the test condition of maximum tension), a method of fitting multiple data sources is used to improve the accuracy of the test calculation.

[0078] First, the UAV is tested under the test condition of maximum tension to obtain the performance of the UAV under these conditions. The specific test obtains the tension, torque and speed of the UAV, and obtains the first test tension, first test torque and first test speed. The first test tension, first test torque and first test speed are parameters obtained by direct testing, and the error is large. The first test speed is recorded as data and does not participate in subsequent calculations.

[0079] Furthermore, based on the second estimated speed, in combination with the aforementioned multiple test stability parameters, multiple tension coefficients and multiple torque coefficients, the tension and torque of the drone under extreme test conditions are estimated and calculated according to the multiple tension coefficients and multiple torque coefficients under multiple preset test conditions, to obtain the second estimated tension and the second estimated torque. The second estimated tension and the second estimated torque are calculated based on the multiple test stability parameters, multiple tension coefficients and multiple torque coefficients under multiple relatively accurate preset test conditions, and are relatively accurate. The second estimated speed is the speed of the motor under extreme test conditions estimated based on the motor control parameters. Since the error of the first test speed is relatively large, the second estimated speed is used for calculation.

[0080] Step S30 in the method provided in the embodiment of the present application includes:

[0081] assigning a plurality of condition weights according to the magnitudes of the plurality of test stability parameters;

[0082] According to the second estimated rotation speed, multiple condition weights, multiple pulling force coefficients and multiple torque coefficients, a second estimated pulling force and a second estimated torque are calculated and obtained as follows:

[0083]

[0084] in, is the second estimated tension, M is the number of multiple preset test conditions, and w i is the condition weight of the i-th preset test condition, is the tension coefficient of the i-th preset test condition, V D2 is the second estimated speed, is the second estimated torque, is the torque coefficient of the i-th preset test condition.

[0085] In the embodiment of the present application, the weight of each preset test condition is determined by performing test stability analysis on multiple preset test conditions to obtain multiple test stability parameters, and the larger the test stability parameter, the greater the condition weight. The test stability parameter is an indicator to measure the stability of the tension and torque data under each test condition.

[0086] Exemplarily, the ratio of each test stability parameter to the sum of multiple test stability parameters is calculated, and the ratio is used as the conditional weight, so that multiple conditional weights are obtained by allocation calculation.

[0087] According to the second estimated rotation speed, multiple condition weights, multiple pulling force coefficients and multiple torque coefficients, a second estimated pulling force and a second estimated torque are calculated and obtained as follows:

[0088]

[0089] in, is the second estimated tension, M is the number of multiple preset test conditions, and w i is the condition weight of the i-th preset test condition, multiple preset test conditions correspond to multiple test stability parameters, and multiple test stability parameters correspond to multiple condition weights, is the tensile coefficient calculated under the i-th preset test condition, V D2 is the second estimated speed, is the second estimated torque, is the torque coefficient of the i-th preset test condition.

[0090] The second estimated speed is the speed estimated by the motor position sensor. In practical applications, in order to save costs, the speed can also be estimated using an algorithm without a position sensor. As shown in the following formula:

[0091]

[0092] Among them, dθ is the angle difference between the front and rear of the motor, and dt is the control period of the motor.

[0093] In an embodiment of the present application, the second estimated tension and the second estimated torque are calculated based on the estimated second estimated rotational speed and the accurate tension coefficient and torque coefficient under multiple test conditions. This process combines the test stability parameters, tension coefficient and torque coefficient, and can effectively estimate the performance of the drone under extreme conditions.

[0094] By analyzing multiple preset test conditions and assigning condition weights according to the test stability parameters, the second estimated pulling force and second estimated torque of the UAV can be accurately calculated according to the second estimated speed. This process takes into account the pulling force coefficient and torque coefficient calculated by the UAV under more accurate test conditions, and the weighted calculation combined with the test stability parameters is more accurate, which improves the accuracy of the UAV large pulling force and torque test.

[0095] S40: Based on the control parameters of the drone, a third estimated tension and a third estimated torque are calculated, and the optimized estimated tension and the optimized estimated torque are calculated by combining the first test tension, the first test torque, the second estimated tension, the second estimated torque, the third estimated tension and the third estimated torque.

[0096] In the embodiment of the present application, the control parameters of the drone include the control strategy of the motor (such as FOC vector control), which is generally controlled by FOC vector position sensorless control. Taking the surface-mounted motor as an example, the motor current (especially the q-axis current), the number of pole pairs of the motor, the motor parameters, etc. directly affect the pulling force and torque output of the drone. Based on the control parameters of the drone, the third estimated pulling force and the third estimated torque are calculated and obtained, and the third estimated pulling force and the third estimated torque are calculated based on the FOC vector position sensorless control parameters of the drone.

[0097] Then, based on the first test tension and the first test torque obtained by direct testing, the second estimated tension and the second estimated torque calculated based on multiple tension coefficients and multiple torque coefficients under multiple preset test conditions, and the third estimated tension and the third estimated torque calculated based on the control parameters of the UAV, the three are combined to calculate the optimized estimated tension and the optimized estimated torque, which are used as the final optimized calculated tension and torque of the UAV under extreme test conditions. By integrating multiple calculation and testing methods, the test accuracy is effectively improved.

[0098] Step S40 in the method provided in the embodiment of the present application includes:

[0099] Based on the FOC vector position sensorless control parameters of the UAV, a third estimated pulling force and a third estimated torque are calculated and obtained as follows:

[0100]

[0101] in, is the third estimated torque, p is the number of motor pole pairs, phif is the motor parameter, iq is the q-axis current, V D3 is the third estimated speed, Estimate the pulling force for the third;

[0102] The first test tension, the second estimated tension and the third estimated tension are weighted and calculated to obtain an optimized estimated tension, and the first test torque, the second estimated torque and the third estimated torque are weighted and calculated to obtain an optimized estimated torque.

[0103] In the embodiment of the present application, the control parameters of the drone, such as FOC (Field Oriented Control) vector position sensorless control technology, are used to accurately adjust the flight state by controlling the current of the motor. Based on these control parameters, the third estimated tension and the third estimated torque are calculated. The key control parameters include the number of pole pairs of the motor, the motor parameter φ f And the iq axis current in the control signal.

[0104] Based on the control principle of the UAV, the third estimated pulling force is first calculated as follows:

[0105]

[0106] in, is the third estimated torque, p is the number of motor pole pairs, phif is the motor parameter, specifically the magnetic field angle between the stator and rotor of the motor, and iq is the q-axis current of the motor.

[0107] Then, based on the third estimated torque and in combination with the formula in the above content, the third estimated speed can be calculated as follows:

[0108]

[0109] Among them, V D3 is the third estimated speed, is the speed of the UAV under extreme test conditions obtained by substituting the third estimated torque calculated based on the UAV control parameters into the estimated calculation in combination with multiple condition weights and multiple torque coefficients.

[0110] Optionally, the third estimated speed may also be a speed estimated by a motor position sensor, and the third estimated speed may also be estimated using a position sensor-free algorithm, as shown in the following formula:

[0111]

[0112] Among them, dθ is the angle difference between the front and rear of the motor, and dt is the control period of the motor.

[0113] Further, based on the third estimated speed and in combination with the formula in the above content, the third estimated torque can be calculated as follows:

[0114]

[0115] in, The third estimated pulling force is obtained by substituting the third estimated rotation speed, a plurality of condition weights, and a plurality of pulling force coefficients into the estimation calculation.

[0116] After directly testing to obtain a first test tension and a first test torque, calculating a second estimated tension and a second estimated torque based on multiple tension coefficients and multiple torque coefficients under multiple preset test conditions, and calculating a third estimated tension and a third estimated torque based on the control parameters of the UAV, the three are combined to calculate a comprehensive and relatively accurate tension and torque of the UAV under extreme test conditions.

[0117] The first test tension, the second estimated tension and the third estimated tension are weighted and calculated to obtain the optimized estimated tension, and the first test torque, the second estimated torque and the third estimated torque are weighted and calculated to obtain the optimized estimated torque.

[0118] The weights of the first test tension, the second estimated tension and the third estimated tension as well as the first test torque, the second estimated torque and the third estimated torque can be configured based on the accuracy of the three by those skilled in the art, for example, 0.3, 0.4 and 0.3.

[0119] Optionally, the average of the first test tension, the second estimated tension and the third estimated tension may be calculated as the optimized estimated tension, and the average of the first test torque, the second estimated torque and the third estimated torque may be calculated to obtain the optimized estimated torque.

[0120] By calculating the third estimated tension and the third estimated torque based on the FOC vector position sensorless control parameters, and combining the first and second estimated tension and torque, the optimized estimated tension and optimized estimated torque are obtained by using the weighted calculation method, thereby improving the accuracy of the tension and torque test under large load of the UAV.

[0121] The method for optimizing the torque and tension of the UAV power system provided by the embodiment of the present invention has at least the following technical effects:

[0122] The embodiment of the present invention tests the UAV through multiple preset test conditions, collects and processes multiple tension coefficients and torque coefficients, and performs test stability analysis in combination with historical test data, calculates multiple test stability parameters, and ensures the reliability of the test results. In the test, by measuring the tension, torque and speed of the UAV under extreme working conditions, combined with the test stability parameters, tension coefficient and torque coefficient, the second estimated tension and the second estimated torque are further calculated. In addition, based on the control parameters of the UAV, the third estimated tension and the third estimated torque are calculated, and combined with the tension and torque of each stage, the optimized estimated tension and estimated torque are finally calculated. This solution effectively solves the measurement error problem caused by vibration during the test of large-load UAVs, greatly improves the test accuracy and stability, and provides high-quality test data support for the performance optimization and design improvement of UAVs, laying a technical foundation for the development of the low-altitude economic industry.

[0123] Embodiment 2:

[0124] like Figure 2 As shown, based on the same inventive concept of the UAV power system bench torque and tension optimization method provided in Example 1, the embodiment of the present invention further provides a UAV power system bench torque and tension optimization system. The interpretation and limitation of the UAV power system bench torque and tension optimization method in Example 1 are also applicable to the UAV power system bench torque and tension optimization system. The system includes:

[0125] The multi-condition test module 11 is used to test the UAV using a test bench according to multiple preset test conditions, and process and obtain multiple tension coefficients and multiple torque coefficients;

[0126] A test stability analysis module 12, configured to perform a test stability analysis according to the plurality of preset test conditions to obtain a plurality of test stability parameters;

[0127] The extreme test module 13 is used to test and obtain a first test tension, a first test torque and a first test speed of the UAV under extreme test conditions, and calculate and obtain a second estimated tension and a second estimated torque according to a second estimated speed, a plurality of test stability parameters, a plurality of tension coefficients and a plurality of torque coefficients;

[0128] The test optimization calculation module 14 is used to calculate a third estimated tension and a third estimated torque based on the control parameters of the drone, and to calculate an optimized estimated tension and an optimized estimated torque by combining the first test tension, the first test torque, the second estimated tension, the second estimated torque, the third estimated tension and the third estimated torque.

[0129] The multi-condition test module 11 is also used for:

[0130] Under the first preset test condition, the UAV is tested using a test bench to obtain a first condition test tension, a first condition test torque and a first condition test speed, and a first tension coefficient and a first torque coefficient are obtained by processing and calculating;

[0131] Continue to test the drone according to multiple preset test conditions to obtain multiple tension coefficients and multiple torque coefficients.

[0132] The multi-condition test module 11 is also used for:

[0133] Under the first preset test condition, the UAV is tested using a test bench to obtain a first condition test tension, a first condition test torque, and a first condition test speed;

[0134] According to the first condition test tension, the first condition test torque and the first condition test speed, the first tension coefficient and the first torque coefficient are calculated and obtained as follows:

[0135] G=K G *V 2 ;

[0136] T=K T *V 2 ;

[0137] Among them, G is the test tension, T is the test torque, V is the test speed, K is the test speed G is the tension coefficient, K T is the torque coefficient.

[0138] The test stability analysis module 12 is also used for:

[0139] According to historical test data of similar UAVs under the first preset test condition, obtaining a historical first set of tension coefficients and a historical first set of torque coefficients;

[0140] Performing test stability calculation and analysis according to the historical first tension coefficient set and the historical first torque coefficient set to obtain a first test stability parameter;

[0141] According to the historical test data of the plurality of preset test conditions, a test stability calculation and analysis is performed to obtain a plurality of test stability parameters.

[0142] The test stability analysis module 12 is also used for:

[0143] randomly selecting a plurality of groups of historical first tension coefficients from the set of historical first tension coefficients, calculating the difference amplitudes, and obtaining a plurality of historical first tension difference amplitudes;

[0144] Calculating an average of the plurality of historical first tension difference amplitudes, and subtracting the average from 1 to obtain a first tension test stability parameter;

[0145] Randomly selecting multiple groups of historical first torque coefficients from the historical first torque coefficient set, calculating the difference amplitude, and obtaining multiple historical first torque difference amplitudes;

[0146] Calculating an average of the plurality of historical first torque difference amplitudes, and subtracting the average from 1 to obtain a first torque test stability parameter;

[0147] The average of the first tension test stability parameter and the first torque test stability parameter is calculated to obtain a first test stability parameter.

[0148] The extreme test module 13 is also used for:

[0149] assigning a plurality of condition weights according to the magnitudes of the plurality of test stability parameters;

[0150] According to the second estimated rotation speed, multiple condition weights, multiple pulling force coefficients and multiple torque coefficients, a second estimated pulling force and a second estimated torque are calculated and obtained as follows:

[0151]

[0152] in, is the second estimated tension, M is the number of multiple preset test conditions, and w i is the condition weight of the i-th preset test condition, is the tension coefficient of the i-th preset test condition, V D2 is the second estimated speed, is the second estimated torque, is the torque coefficient of the i-th preset test condition.

[0153] The test optimization calculation module 14 is also used for:

[0154] Based on the FOC vector position sensorless control parameters of the UAV, a third estimated pulling force and a third estimated torque are calculated and obtained as follows:

[0155]

[0156] in, is the third estimated torque, p is the number of motor pole pairs, phif is the motor parameter, iq is the q-axis current, V D3 is the third estimated speed, Estimate the pulling force for the third;

[0157] The first test tension, the second estimated tension and the third estimated tension are weighted and calculated to obtain an optimized estimated tension, and the first test torque, the second estimated torque and the third estimated torque are weighted and calculated to obtain an optimized estimated torque.

[0158] Embodiment three:

[0159] See also Figure 3 , Figure 3 Schematic diagram of an electronic device provided by an embodiment of the present invention. Figure 3 As shown, an embodiment of the present invention provides an electronic device 500, including a memory 510, a processor 520, and a first computer program 511 stored in the memory 510 and executable on the processor 520. When the processor 520 executes the first computer program 511, a method for optimizing the torque and tension of a UAV power system test bench is implemented.

[0160] Embodiment 4:

[0161] See also Figure 4 , Figure 4 A schematic diagram of an embodiment of a computer-readable storage medium provided in an embodiment of the present invention. Figure 4 As shown, this embodiment provides a computer-readable storage medium 600, on which a second computer program 611 is stored. When the second computer program 611 is executed by a processor, the following steps are implemented: a method for optimizing the torque and tension of a UAV power system test bench.

[0162] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0163] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0164] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0165] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0166] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0167] Although preferred embodiments of the present invention have been described, additional changes and modifications may occur to these embodiments once those skilled in the art understand the basic inventive concepts.

[0168] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention belong to the scope of the present invention and its equivalent technologies, the present invention is also intended to include these changes and variations.

Claims

1. A method for optimizing the torque and tension of a UAV power system bench, characterized in that: The method comprises: According to multiple preset test conditions, the UAV is tested using a test bench to obtain multiple tension coefficients and multiple torque coefficients; Performing a test stability analysis according to the plurality of preset test conditions to obtain a plurality of test stability parameters; The test obtains a first test tension, a first test torque and a first test speed of the UAV under extreme test conditions, and calculates a second estimated tension and a second estimated torque according to a second estimated speed, a plurality of test stability parameters, a plurality of tension coefficients and a plurality of torque coefficients; Based on the control parameters of the drone, a third estimated tension and a third estimated torque are calculated, and the optimized estimated tension and the optimized estimated torque are calculated by combining the first test tension, the first test torque, the second estimated tension, the second estimated torque, the third estimated tension and the third estimated torque.

2. The method for optimizing the torque and tension of the UAV power system according to claim 1, characterized in that: The test bench is used to test the drone according to multiple preset test conditions to obtain multiple tension coefficients and multiple torque coefficients, including: Under the first preset test condition, the UAV is tested using a test bench to obtain a first condition test tension, a first condition test torque and a first condition test speed, and a first tension coefficient and a first torque coefficient are obtained by processing and calculating; Continue to test the drone according to multiple preset test conditions to obtain multiple tension coefficients and multiple torque coefficients.

3. The method for optimizing the torque and tension of the UAV power system according to claim 2, characterized in that: Under the first preset test condition, the UAV is tested using a test bench to obtain a first condition test tension, a first condition test torque and a first condition test speed, and a first tension coefficient and a first torque coefficient are obtained by processing and calculating, including: Under the first preset test condition, the UAV is tested using a test bench to obtain a first condition test tension, a first condition test torque, and a first condition test speed; According to the first condition test tension, the first condition test torque and the first condition test speed, the first tension coefficient and the first torque coefficient are calculated and obtained as follows: G=K G *V 2 ; T=K T *V 2 ; Among them, G is the test tension, T is the test torque, V is the test speed, K is the test speed G is the tension coefficient, K T is the torque coefficient.

4. The method for optimizing the torque and tension of the UAV power system according to claim 1, characterized in that: According to the plurality of preset test conditions, a test stability analysis is performed to obtain a plurality of test stability parameters, including: According to historical test data of similar UAVs under the first preset test condition, obtaining a historical first set of tension coefficients and a historical first set of torque coefficients; Performing test stability calculation and analysis according to the historical first tension coefficient set and the historical first torque coefficient set to obtain a first test stability parameter; According to the historical test data of the plurality of preset test conditions, a test stability calculation and analysis is performed to obtain a plurality of test stability parameters.

5. The method for optimizing the torque and tension of the UAV power system according to claim 4, characterized in that: According to the first historical tension coefficient set and the first historical torque coefficient set, a test stability calculation and analysis is performed to obtain a first test stability parameter, including: Randomly selecting multiple groups of historical first tension coefficients from the historical first tension coefficient set, calculating the difference amplitude, and obtaining multiple historical first tension difference amplitudes; Calculating an average of the plurality of historical first tension difference amplitudes, and subtracting the average from 1 to obtain a first tension test stability parameter; Randomly selecting multiple groups of historical first torque coefficients from the historical first torque coefficient set, calculating the difference amplitude, and obtaining multiple historical first torque difference amplitudes; Calculating an average of the plurality of historical first torque difference amplitudes, and subtracting the average from 1 to obtain a first torque test stability parameter; The average of the first tension test stability parameter and the first torque test stability parameter is calculated to obtain a first test stability parameter.

6. The method for optimizing the torque and tension of the UAV power system according to claim 1, characterized in that: Calculating a second estimated pulling force and a second estimated torque according to a second estimated rotation speed, a plurality of test stability parameters, a plurality of pulling force coefficients, and a plurality of torque coefficients includes: assigning a plurality of condition weights according to the magnitudes of the plurality of test stability parameters; According to the second estimated rotation speed, multiple condition weights, multiple pulling force coefficients and multiple torque coefficients, a second estimated pulling force and a second estimated torque are calculated and obtained as follows: in, is the second estimated tension, M is the number of multiple preset test conditions, and w i is the condition weight of the i-th preset test condition, is the tension coefficient of the i-th preset test condition, V D2 is the second estimated speed, is the second estimated torque, is the torque coefficient of the i-th preset test condition.

7. The method for optimizing the torque and tension of the UAV power system according to claim 6, characterized in that: Based on the control parameters of the drone, a third estimated tension and a third estimated torque are calculated, and the optimized estimated tension and the optimized estimated torque are calculated by combining the first test tension, the first test torque, the second estimated tension and the second estimated torque, including: Based on the FOC vector position sensorless control parameters of the UAV, a third estimated pulling force and a third estimated torque are calculated and obtained as follows: in, is the third estimated torque, p is the number of motor pole pairs, phif is the motor parameter, iq is the q-axis current, V D3 is the third estimated speed, Estimate the pulling force for the third; The first test tension, the second estimated tension and the third estimated tension are weighted and calculated to obtain an optimized estimated tension, and the first test torque, the second estimated torque and the third estimated torque are weighted and calculated to obtain an optimized estimated torque.

8. A UAV power system test bench torque and tension optimization system, characterized in that: The system is used to execute the UAV power system bench torque and tension optimization method according to any one of claims 1 to 7, and the system comprises: A multi-condition test module is used to test the UAV using a test bench according to multiple preset test conditions, and process multiple tension coefficients and multiple torque coefficients; A test stability analysis module, used to perform test stability analysis according to the plurality of preset test conditions to obtain a plurality of test stability parameters; An extreme test module, used to test and obtain a first test tension, a first test torque and a first test speed of the UAV under extreme test conditions, and calculate and obtain a second estimated tension and a second estimated torque according to a second estimated speed, a plurality of test stability parameters, a plurality of tension coefficients and a plurality of torque coefficients; The test optimization calculation module is used to calculate a third estimated tension and a third estimated torque based on the control parameters of the UAV, and to calculate an optimized estimated tension and an optimized estimated torque by combining the first test tension, the first test torque, the second estimated tension, the second estimated torque, the third estimated tension and the third estimated torque.

9. An electronic device, characterized in that: include: Memory for storing computer software programs; A processor is used to read and execute the computer program, thereby implementing the UAV power system test bench torque tension optimization method described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer software program, and when the computer program is executed by the processor, the method for optimizing the torque and tension of the UAV power system test bench as described in any one of claims 1 to 7 is implemented.

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