A motor tension test platform with rotor and identification method

Abstract

The present invention discloses a motor tension test platform with a rotor and an identification method, comprising a static torque sensor, a tension sensor, a temperature sensor, a current sensor, a photoelectric speed sensor, a connecting foot, a ball bearing, a bare rod, a mounting platform, a vertical aluminum plate, and a single-chip computer, wherein all the components are built into the mounting platform; the present invention enlarges the size of the test bench and reinforces the entire platform through various connecting parts such as aluminum parts and connecting feet, so that the present invention can complete the tension test of small motors as well as the tension test of large motors of up to 50 inches.

Description

Technical Field

[0001] The invention relates to the fields of power electronics technology and flight control technology, and in particular to a platform for measuring motor parameters and intermediate links introduced into system identification. Background Art

[0002] With the rapid development of science and technology and the field of flight control, higher and higher requirements are being placed on motor performance and quality indicators. While the requirements for the motor itself are constantly increasing, the development of motor testing technology is also essential. Traditional test equipment and methods have a long operation time, many instruments required for observation, and data needs to be manually read, analyzed, and calculated, which to a certain extent affects the quality and accuracy of motor measurement. With the continuous improvement of the performance of motor raw materials and the continuous improvement of design methods, the performance and quality indicators of motors have also been greatly improved. Therefore, the development of motor testing is imminent.

[0003] The development of motor test systems can be divided into the following four stages:

[0004] 1. Meter type: The test instruments used in this stage are all pointer type, the data is inaccurate, only a rough assessment can be made, and the measurement range cannot be set. At the same time, it requires a high level of professionalism from the test personnel.

[0005] 2. Digital: The test instruments at this stage are mostly digital display mode, and mainly use LCD display, but the instrument functions at this time are relatively simple and the accuracy is low.

[0006] 3. Integrated: At this stage, many instrument manufacturers connected multiple single-chip microcomputers through PLC for data transmission, achieving "integration" in appearance, greatly reducing the size of the entire platform and improving test accuracy and speed.

[0007] 4. Internet of Things: At this stage, achieving integration through a single device and "integrating" data from different devices into instruments is a problem that the entire society is thinking about.

[0008] In recent years, motor testing technology has been increasingly used in the field of flight control. Taking the traditional UAV tension test bench as an example, this platform is mainly used to measure the tension of small and medium-sized motors. In addition, it can also measure the voltage, current, torque, temperature and other parameters of the motor. However, the test bench can only measure specific parameters and cannot derive the specific coefficients of the intermediate links, such as tension coefficient, torque coefficient, etc. Summary of the invention

[0009] Purpose of the invention: In order to overcome the shortcomings of the above-mentioned existing platforms, the present invention discloses an intelligent, simple, integrated motor test platform with system identification. The present invention is suitable for the occasion of UAV tension test, and has the characteristics of simple operation, strong safety, large measurement range, ideal effect and integration.

[0010] The technical solution of the present invention includes: a motor tension test platform with a rotor, including a static torque sensor 1, a tension sensor 2, a temperature sensor 3, a current sensor 4, a photoelectric speed sensor 5, a connecting foot 6, a ball bearing 7, a polished rod 8, a mounting platform 9, a vertical aluminum plate 10, and a single-chip computer 11, wherein all the components are built into the mounting platform; one side of the motor with a rotor is connected to a carbon plate and a pipe clamp, the carbon plate and the pipe clamp are connected to one side of the static torque sensor 1, the other side of the static torque sensor 1 is connected to the vertical aluminum plate 10, and the vertical aluminum plate 10 is connected to the horizontal aluminum plate through the connecting foot 6; the tension sensor 2 is connected to the polished rod 8 through a built-in thread, and then connected to the horizontal aluminum plate through a groove on the polished rod 8, and a ball bearing 7 is arranged on the lower side of the entire horizontal aluminum plate, and the ball bearing 7 is provided with bearing seats at both ends, and the bearing seats are fixed on the installation platform 9; the temperature sensor 3 is aligned with the motor base with the rotor by extending a fixing plate from the bottom plate, so as to complete the temperature monitoring of the motor in real time and set a high temperature alarm; the photoelectric speed sensor 5 is installed on the vertical carbon plate, and the vertical carbon plate is fixed on the vertical aluminum plate 10 through a connector, so that the light spot of the photoelectric speed sensor 5 is aligned with the blade of the rotor, the current sensor 4 is located between the electric regulator and the motor with the rotor, and the power cord of the motor with the rotor is passed through the current sensor 4; the single-chip computer 11 is located at the edge of the entire platform, and the single-chip computer 11 is electrically connected to the static torque sensor 1, the tension sensor 2, the temperature sensor 3, the current sensor 4, and the photoelectric speed sensor 5 respectively, and is responsible for receiving and processing all data.

[0011] Furthermore, the entire installation platform 9 is built into an aluminum frame constructed with aluminum profiles to ensure the stability and safety of the entire test platform.

[0012] Further, the following steps are included:

[0013] First, the tension T and speed ω data of the motor with rotor are obtained through the tension sensor 2 and the photoelectric speed sensor 5 respectively; by observing the formula of the tension coefficient and simplifying it, it can be obtained that the tension T and the square of the speed ω 2 Proportional to T and ω 2 Then, according to the measured T and ω 2 The coordinate points of the relationship diagram Construct the fitting function and the objective function, sort them out and find the partial derivative of the objective function to finally get the specific value of the tension coefficient;

[0014] Since there is an inertial delay link between the time when the motor electronic control generates a signal and the time when the motor with the rotor completes the command, this identification method for this link first converts the relationship between the input and output signals from the frequency domain to the time domain, then integrates the time domain relationship and organizes it into the least squares form, and finally obtains a set of estimation equations.

[0015] Furthermore, the fitting function and the objective function are:

[0016] Fitting function h(ω 2 ):h(ω 2 )=Aω 2 +B

[0017] Objective function J(A,B):

[0018] A and B are two parameters of the fitting function, T i is the pulling force, ω is the rotation speed.

[0019] Furthermore, the time domain relationship is then integrated and organized into the least squares form, and finally a set of estimation equations is obtained as follows:

[0020] The relationship between the input and output of the delay link is:

[0021]

[0022] After converting the above formula from frequency domain to time domain, we can get:

[0023]

[0024] In the formula, x(t) is the input signal, δ is the delay time, y(t) is the system output signal, τ, k are the parameters to be identified, and by integrating the above formula once, we can get the estimation equation in integral form:

[0025] y [1] (t)+τy(t)=kx [1] (t-δ)+c0,t≥δ

[0026] c0=τy(0)

[0027]

[0028] x(t-δ)=αu(t-δ),t≥δ

[0029] c0 is a non-zero constant, u(t-δ) is the step response signal, α is the amplitude, and the above formula is integrated and arranged into the least squares form:

[0030] h1(t)=A1(t)θ1

[0031] h1(t)=y(t)

[0032]

[0033] θ1=[1 αk c0]

[0034] Among them, h1(t) is the objective function, A1(t) and θ1 are vector matrices, when t = [t d+1 ,t d+2 ,…,t n ], we can get a set of estimation equations:

[0035] Γ(t)=Φ(t)θ1

[0036]

[0037] Φ(t) is a vector matrix set of n data points; d represents the number of points collected within the assumed delay time δ1, and n represents the total number of data collected, where Γ(t) and Φ2(t) are directly observable data, and θ1 is the parameter to be estimated.

[0038] A ball bearing 7 is arranged on the lower side of the entire horizontal aluminum plate, and bearing seats are arranged at both ends of the ball bearing 7. The bearing seats are fixed on the mounting platform 9. In this way, when the motor is rotating, the motor tension at different speeds can be tested and its tension coefficient can be obtained. At the same time, according to the result of the static torque sensor 1, the air anti-torque torque coefficient of the propeller motor can be obtained through system identification.

[0039] The temperature sensor 3 extends a fixed plate from the bottom plate so that the temperature sensor 3 is aligned with the motor base, and the temperature of the motor is monitored in real time and a high temperature alarm is set; the photoelectric speed sensor 5 is installed on the vertical carbon plate, and the vertical carbon plate is fixed on the vertical aluminum plate 10 through a connector, so that the light spot of the photoelectric speed sensor 5 is aligned with the blade of the rotor, so as to accurately measure the speed of the motor. Since the platform of the present invention is larger than the traditional motor test platform, it can complete the motor parameter test of the maximum 34-inch blade. The current sensor 4 is located between the electric speed controller and the motor with the rotor. The power cord of the motor with the rotor passes through the current sensor 4, so as to measure the current information during operation.

[0040] On the main control board, the intermediate lag link is measured according to the time when the single chip computer 11 signal is sent and the time when the motor reaches the target speed, and the response time of the motor is obtained. Finally, an inertial sensor is provided on the main control board to detect the amount of vibration brought to the test bench by the rotation of the motor. At the same time, the system identification of the entire test bench is completed on the main control, and the transfer function corresponding to each function and the specific parameters therein are calculated through the waveform diagram of the input and output quantities.

[0041] In summary, the present invention discloses a motor tension test platform with simple operation, strong safety, large measurement range, ideal effect and data integration.

[0042] Compared with the traditional motor test platform, the method of the present invention is unique in that:

[0043] (1) By enlarging the size of the test bench and reinforcing the entire platform through various connecting parts such as aluminum parts and connecting feet 6, the present invention can complete the tension test of small motors as well as the tension test of large motors up to 50 inches.

[0044] (2) Based on the static torque sensor 1, the air anti-torque coefficient is obtained by calculation. At the same time, the test bench has multiple sensors, realizing the "integration" of functions.

[0045] (3) Based on the measured parameters, a system identification step is introduced, so that the test bench can not only observe the results of each sensor, but also obtain the corresponding mathematical model based on the relationship between the results and the input. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Attached Figure 1 It is a schematic diagram of the basic structure of the present invention;

[0047] Attached Figure 2 It is a partial enlarged schematic diagram of the present invention;

[0048] Attached Figure 3 This is a system identification block diagram of the present invention.

[0049] exist Figure 1 Among them, 1-static torque sensor, 2-tension sensor, 3-temperature sensor, 4-current sensor, 5-photoelectric speed sensor, 6-connecting foot, 7-ball bearing, 8-light rod, 9-installation platform, 10-vertical aluminum plate, 11-single chip microcomputer. DETAILED DESCRIPTION

[0050] The present invention will be further described below with reference to the accompanying drawings and examples.

[0051] Figure 1-2 The motor test platform shown is mainly composed of the following parts: 1-static torque sensor, 2-tension sensor, 3-temperature sensor, 4-current sensor, 5-photoelectric speed sensor. The static torque sensor 1 is installed above the entire platform through the connecting foot 6 and the aluminum plate to facilitate the installation of the motor and the blades. It is connected to the entire installation platform 9 through the aluminum column to ensure the stability of the entire platform.

[0052] The photoelectric speed sensor 5 and the temperature sensor 3 are installed between the aluminum plate and the bottom plate, wherein the light spot of the photoelectric speed sensor 5 is aimed at the blade, and the probe of the temperature sensor 3 is aimed at the motor base. The tension sensor 2 is installed on the mounting platform 9 through a fixed bracket and a connecting foot 6, and the tension sensor 2 and the fixing plate of the static torque sensor 1 are simultaneously connected to the horizontal aluminum plate through a threaded light rod 8.

[0053] Figure 3The single chip microcomputer 11 used in the present invention is shown. The single chip microcomputer 11 is connected to different sensors through 485, serial port and CAN bus. The input signal is sent to the electric drive motor through the single chip microcomputer 11. Then, the hysteresis coefficient of each link is systematically identified according to the real-time updated sensor value through the least square method, including the air back torque coefficient, current coefficient, tension coefficient, and the hysteresis link from the signal generation to the expected achievement.

[0054] The present invention adopts a system identification method to establish a mathematical model between input and output, and the method adopted is mainly the least square method for identification.

[0055] Take the tensile coefficient as an example: The formula for the tensile coefficient is known as formula (1)

[0056]

[0057] Among them, ρ is the air density, R is the rotor radius, ω is the motor speed, and T is the pulling force. Since ρ, R, and π are known quantities, Q is used to replace these variables, which can be simplified to formula (2):

[0058]

[0059] From the above formula, we can see that T and ω 2 It is directly proportional to the known rotation speed ω which can be measured by the photoelectric rotation speed sensor, and the tension T which can be measured by the tension sensor. A graph of T and ω can be drawn, and then C can be obtained by fitting through the least squares method. T , let the measured point be

[0060]

[0061] Assume the fitting function is formula (3)

[0062] h(ω 2 )=Aω 2 +B (3)

[0063] Then the objective function is:

[0064]

[0065] Among them, J(A,B) is the objective function, T i is the pulling force, h i (ω 2 ) is the fitting function, and the partial derivatives of A and B are obtained respectively.

[0066]

[0067] Among them, x i With yi ω 2 The coordinate point of the coordinate system established by T, so we have

[0068]

[0069] The same applies to other coefficients, such as current coefficient and air anti-torque coefficient.

[0070] Since the process from the motor signal generation to the motor reaching the target speed is not completed immediately, there is a certain lag delay link, so the present invention also analyzes this. After the motor electric control generates a signal, there is an inertia delay link between the electric control signal being sent and the motor completing the command. The relationship between the two is formula (8):

[0071]

[0072] After converting equation (8) from the frequency domain to the time domain, we can obtain:

[0073]

[0074] In the formula, x(t) is the input signal, δ is the delay time, y(t) is the system output signal, τ,k is the parameter to be identified. By integrating formula (9) once, we can get the estimation equation in integral form:

[0075] y [1] (t)+τy(t)=kx [1] (t-δ)+c0,t≥δ (10)

[0076] c0=τy(0) (11)

[0077]

[0078] x(t-δ)=αu(t-δ),t≥δ (13)

[0079] c0 is a non-zero constant, u(t-δ) is the step response signal, α is the amplitude, and equations (11), (12), and (13) are substituted into equation (10) and then arranged into the least squares form:

[0080] h1(t)=A1(t)θ1 (14)

[0081] h1(t)=y(t)

[0082]

[0083] θ1=[1 αk c0] (15)

[0084] Among them, h1(t) is the objective function, A1(t) and θ1 are vector matrices, when t = [t d+1 ,t d+2 ,…,t n ], a set of estimation equations are obtained according to formula (14):

[0085]

[0086] d represents the number of points collected within the assumed delay time δ1, n represents the total number of data collected, where Γ(t) and Φ2(t) are directly observable data, and θ1 is the parameter to be estimated.

Claims

1. A method for identifying a motor tension test platform with a rotor, characterized in that: The platform comprises a static torque sensor (1), a tension sensor (2), a temperature sensor (3), a current sensor (4), a photoelectric speed sensor (5), a connecting foot (6), a ball bearing (7), a polished rod (8), a mounting platform (9), a vertical aluminum plate (10), and a single-chip computer (11), wherein all the components are built into the mounting platform; One side of the motor with the rotor is connected to a carbon plate and a pipe clamp, the carbon plate and the pipe clamp are connected to one side of a static torque sensor (1), the other side of the static torque sensor (1) is connected to a vertical aluminum plate (10), and the vertical aluminum plate (10) is connected to a horizontal aluminum plate via a connecting foot piece (6); The tension sensor (2) is connected to the polished rod (8) through an internal thread, and then connected to the horizontal aluminum plate through a groove on the polished rod (8). A ball bearing (7) is arranged on the lower side of the entire horizontal aluminum plate. Bearing seats are arranged at both ends of the ball bearing (7), and the bearing seats are fixed on the mounting platform (9). The temperature sensor (3) is configured to align the temperature sensor (3) with the motor base with the rotor by extending a fixing plate from the bottom plate, so as to monitor the temperature of the motor in real time and set a high temperature alarm; the photoelectric speed sensor (5) is mounted on the vertical carbon plate, and the vertical carbon plate is fixed on the vertical aluminum plate (10) through a connecting piece, so that the light spot of the photoelectric speed sensor (5) is aligned with the blade of the rotor; the current sensor (4) is located between the electric regulator and the motor with the rotor, and the power line of the motor with the rotor passes through the current sensor (4); The single chip microcomputer (11) is located at the edge of the entire platform. The single chip microcomputer (11) is electrically connected to the static torque sensor (1), the tension sensor (2), the temperature sensor (3), the current sensor (4), and the photoelectric rotation speed sensor (5) respectively, and is responsible for receiving and processing all data; The method comprises the following steps: First, the tension T and the speed ω data of the motor with the rotor are obtained respectively through the tension sensor (2) and the photoelectric speed sensor (5); by observing the formula of the tension coefficient and simplifying it, it can be obtained that the tension T and the square of the speed ω 2 Proportional to T and ω 2 Then, according to the measured T and ω 2 The coordinate points of the relationship diagram Construct the fitting function and the objective function, sort them out and find the partial derivative of the objective function to finally get the specific value of the tension coefficient; Since there is an inertial delay link between the time when the motor electronic control generates a signal and the time when the motor with the rotor completes the command, this identification method for this link first converts the relationship between the input and output signals from the frequency domain to the time domain, then integrates the time domain relationship and organizes it into the least squares form, and finally obtains a set of estimation equations.

2. The method according to claim 1, characterized in that The entire installation platform (9) is built into an aluminum frame constructed with aluminum profiles to ensure the stability and safety of the entire test platform.

3. The method according to claim 1, characterized in that The fitting function and objective function are: Fitting function h(ω 2 ):h(ω 2 )=Aω 2 +B Objective function J(A,B): A and B are two parameters of the fitting function, T i is the pulling force, ω is the rotation speed.

4. The method according to claim 1, characterized in that: Then the time domain relationship is integrated and sorted into the least squares form, and finally a set of estimation equations is obtained as follows: The relationship between the input and output of the delay link is: After converting the above formula from frequency domain to time domain, we can get: In the formula, x(t) is the input signal, δ is the delay time, y(t) is the system output signal, τ, k are the parameters to be identified, and by integrating the above formula once, we can get the estimation equation in integral form: y [1] (t)+τy(t)=kx [1] (t-δ)+c0,t≥δ c0=τy(0) x(t-δ)=αu(t-δ),t≥δ c0 is a non-zero constant, u(t-δ) is the step response signal, α is the amplitude, and the above formula is integrated and arranged into the least squares form: h1(t)=A1(t)θ1 h1(t)=y(t) θ1=[1 αk c0] Among them, h1(t) is the objective function, A1(t) and θ1 are vector matrices, when t = [t d+1 ,t d+2 ,…,t n ], we can get a set of estimation equations: Γ(t)=Φ(t)θ1 Φ(t) is a vector matrix set of n data points; d represents the number of points collected within the assumed delay time δ1, and n represents the total number of data collected, where Γ(t) and Φ2(t) are directly observable data, and θ1 is the parameter to be estimated.

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