Performance testing device and method for heat dissipation module for unmanned aerial vehicle motor controller
By using a multimodal sensing test adjustment unit and PID control algorithm, the problem of inaccurate simulation in the test of the heat dissipation module of the UAV motor controller was solved, realizing efficient and safe heat dissipation module testing and improving the stability and reliability of the test system.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-26
- Publication Date
- 2026-04-14
AI Technical Summary
Existing testing methods for the heat dissipation module of UAV motor controllers cannot accurately simulate the heat dissipation conditions of high-altitude, high-speed flight, and lack intelligent sensing and adjustment functions, resulting in inaccurate test results and safety risks.
By employing a multimodal sensing test and adjustment unit and a PID control algorithm, combined with wind speed, wind pressure, and thermal imaging sensors, and coordinating the control priorities of temperature and wind speed through a fuzzy logic rule base, dynamic adjustment of temperature and wind speed is achieved to simulate the heat dissipation conditions of a UAV motor controller.
It enables accurate simulation and efficient testing of the heat dissipation module of the UAV motor controller, improves the reliability and safety of testing, reduces the difficulty of operation and the probability of error, and enhances the stability and robustness of the testing system.
Smart Images
Figure CN121857628A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of heat dissipation for drone motor controllers, and specifically to a device and method for testing the performance of a heat dissipation module for drone motor controllers. Background Technology
[0002] With the continuous development of aerospace technology, the performance and functionality of UAV motor controllers are constantly improving, and the power density of their internal electronic devices and mechanical components is also continuously increasing. This leads to a significant increase in the heat generated by UAV motor controllers during operation, making heat dissipation a crucial factor affecting their performance and reliability. Therefore, accurate testing and evaluation of the heat dissipation modules used in UAV motor controllers are particularly important. Traditional testing methods for UAV motor controller heat dissipation modules are typically conducted in actual flight environments, which is not only costly but also carries certain safety risks. Furthermore, the complexity and uncertainty of actual flight environments make it difficult for test results to accurately reflect the performance of the heat dissipation system. To address these issues, ground-based simulation testing devices have emerged. However, existing ground-based testing devices cannot accurately simulate the heat dissipation conditions of UAVs during high-altitude, high-speed flight, especially regarding the dynamic changes in temperature and wind speed, resulting in significant deviations between test results and actual flight conditions. Moreover, control systems typically use single-parameter adjustments, failing to consider the synergistic effects of temperature and wind speed simultaneously, making precise control of the heat dissipation system difficult. In addition, existing devices mostly rely on manual operation and adjustment, lacking intelligent sensing and adjustment functions, and cannot respond to dynamic changes in the heat dissipation system in real time. Therefore, a high-precision, low-cost, and stable testing method is needed to overcome the shortcomings of existing testing methods. Summary of the Invention
[0003] The technical problem solved by this invention is: In view of the above-mentioned problem, this invention proposes a heat dissipation module performance testing device and method for UAV motor controller, which can accurately simulate the heat dissipation conditions of UAV during high-altitude and high-speed flight, and realize dynamic adjustment of temperature and wind speed through multimodal perception and PID control algorithm, thereby improving the reliability and safety of the testing device.
[0004] To achieve the above objectives, the present invention adopts the following technical solution: a heat dissipation module performance testing device for a drone motor controller, comprising: a sample positioning unit and a multimodal sensing test adjustment unit; The sample positioning unit is used to install and fix the heat dissipation module to be tested. The heat dissipation module under test includes fins, heat pipes, and a thermally conductive aluminum plate. The fins are subjected to forced air cooling generated by the drone's flight. The heat pipes are the thermally conductive elements in the heat dissipation module. The thermally conductive aluminum plate is connected to the heating module. The multimodal sensing test and adjustment unit includes a wind speed sensor, a wind pressure sensor, a thermal imaging sensor, a fan module, a heating module, a heat dissipation module, and a multi-physical quantity coupling adjustment system; The heating module simulates the heat generation power of the drone motor controller. The heating module is heated by a programmable power supply, and the heat is conducted through the heat dissipation module to simulate the heat source conduction of the drone motor controller's heat dissipation module. The fan module adjusts the output air speed to simulate the heat dissipation of an aircraft's cooling system. An anemometer and a wind pressure sensor measure the wind speed and wind pressure in the duct, respectively; a thermal imaging sensor measures the surface temperature of the heat dissipation module; and the wind speed, wind pressure, and temperature are fed back to a multi-physical quantity coupled regulation system. The multi-physical quantity coupled regulation system dynamically adjusts the power of the heating module and the fan speed in the fan module through a PID control algorithm, thereby achieving closed-loop control of the temperature field and the wind speed field.
[0005] Furthermore, the multi-physical quantity coupled regulation system coordinates the control priorities of temperature and wind speed through a fuzzy logic rule base, and when the temperature error... When the wind speed error exceeds the set threshold, the heating module power is adjusted first; when the wind speed error exceeds the set threshold, the heating module power is adjusted first. When the speed exceeds the set threshold, the fan speed will be adjusted first, including: S1. The wind speed is inferred from the dynamic pressure formula of Bernoulli's equation and the wind pressure data in the duct measured by the wind pressure sensor. When the error between the wind speed inferred from the wind pressure sensor measurement data and the wind speed measured by the wind speed sensor is less than or equal to the set threshold a, the wind speed sensor measurement data is considered accurate. When the error between the wind speed inferred from the wind pressure sensor measurement data and the wind speed measured by the wind speed sensor is greater than the set threshold a, the PID control algorithm is used to correct the wind speed measurement error. S2, Generate comprehensive temperature value and filtered wind speed value ; S3. Establish a fuzzy logic rule base and define temperature error. and wind speed error The fuzzy set is used to convert sensor data into fuzzy variables through membership functions; S4. Generate control instructions based on the fuzzy logic rule base. and Then increase the heating power and increase the fan speed; if and If so, maintain the current control value; and Then reduce the heating power and lower the fan speed; S5. Based on the comprehensive temperature value Calculate temperature error The PID control algorithm calculates the power adjustment of the heating module and adjusts the power of the heating module to bring the temperature closer to the set value; based on the filtered wind speed value... Calculate wind speed error The fan speed adjustment is calculated by using a PID control algorithm, and the fan speed is adjusted to make the wind speed approach the set value.
[0006] Furthermore, the multi-physical quantity coupled regulation system employs a first-order PID control algorithm, including: Temperature and wind speed are decoupled for control. The temperature PID output controls the heating module power, and the wind speed PID output controls the fan speed. The PID control algorithm is as follows: , , In the formula, The sampling time interval; and Representing the first k The temperature and fan speed output of the time controller; and These are the proportional gains of the temperature controller and the fan speed controller, respectively. and These are the integral gains of the temperature controller and the fan speed controller, respectively. and These are the differential gains of the temperature controller and the fan speed controller, respectively. and It is the first k The error in temperature and wind speed at any given time; and It is the first i Errors in temperature and wind speed at any given time.
[0007] Furthermore, the method for correcting wind speed measurement errors using a PID control algorithm is as follows: , in, This is the wind pressure compensation coefficient, used to correct wind speed measurement errors; For the first k The wind speed output of the time controller; The proportional gain of the wind speed controller; For the first k Wind speed error at any given time; This is the integral gain of the wind speed controller; For the first i Wind speed error at any given time; The differential gain of the wind speed controller; This is the set value for the wind pressure sensor. These are the measured values from the wind pressure sensor.
[0008] Furthermore, a weighted average method was used to analyze the overall temperature of the thermal imaging sensor. and local temperature Weighted values are then used to generate a composite temperature value: , In the formula, , These are the weighting coefficients.
[0009] Furthermore, the measurement data from the wind speed sensor... Perform Kalman filtering to obtain the filtered wind speed value. :
[0010] In the formula, K is the Kalman gain. This is the predicted data from the wind speed sensor.
[0011] Furthermore, the establishment of the fuzzy logic rule base includes: , , In the formula, , These represent temperature error and wind speed error, respectively. , These represent the membership functions based on temperature error and wind speed error, respectively.
[0012] Furthermore, after the heat dissipation module for the UAV motor controller is installed, the positioning device in the sample positioning unit is activated, causing the positioning device to move to the same central axis plane as the fan module, and the position is calibrated by a laser rangefinder.
[0013] The test method for the above-mentioned performance testing device for a heat dissipation module of a UAV motor controller is characterized by comprising: S1: Install the heat dissipation module to be tested in the sample positioning unit and obtain the spatial position of the heat dissipation module fins; S2: Adjust the spatial position of the heat dissipation module under test so that the air cooling generated by the fan module only acts on the fins; S3: Determine the power of the heating module and turn on the programmable power supply of the heating module and the fan module; S4: Monitor the wind speed sensor, wind pressure sensor, and thermal imaging sensor to collect dynamic data in real time during the test process, and record the power data of the heating module.
[0014] Furthermore, the aforementioned testing method coordinates the control priorities of temperature and wind speed through a fuzzy logic rule base, and when the temperature error... When the wind speed error exceeds the set threshold, the heating module power is adjusted first; when the wind speed error exceeds the set threshold, the heating module power is adjusted first. When the speed exceeds the set threshold, the fan speed will be adjusted first.
[0015] Compared with the prior art, the present invention has the following advantages: 1) This invention, through the coordinated operation of a heating module and a fan, can accurately simulate the heat dissipation conditions of a drone during high-altitude, high-speed flight. The heating module uses a programmable power supply to simulate the heat source conditions of the drone's motor controller; the fan adjusts its speed to simulate the drone's heat dissipation environment. This simulation method can highly replicate the heat dissipation scenario in actual flight, providing more realistic conditions for testing the heat dissipation system.
[0016] 2) The multimodal sensing test and adjustment system of this invention integrates a thermal imaging sensor and a wind speed sensor, enabling real-time monitoring of the surface temperature distribution of the heat dissipation system and the airflow velocity within the duct. Through a PID control algorithm, the system can dynamically adjust the power of the heating module and the fan speed, achieving closed-loop control of the temperature and wind speed fields. This multimodal sensing and dynamic adjustment mechanism effectively addresses various changes that may occur during testing, ensuring the stability and consistency of the test.
[0017] 3) This invention coordinates the control priorities of temperature and wind speed through a fuzzy logic rule base, enabling dynamic adjustment of control parameters based on actual operating conditions and improving system response speed and control accuracy. A weighted average method is used to weight the overall and local temperatures of the thermal imaging sensor, and Kalman filtering is applied to the wind speed sensor data to eliminate noise and interference. This data fusion and filtering mechanism improves the accuracy and reliability of measurement data, providing a more reliable input for the control algorithm.
[0018] 4) The heating module, heat dissipation module, fan, thermal imaging sensor, wind speed sensor, and industrial control computer of this invention are all connected to the motion controller, achieving a high degree of integration and automation. This integrated and automated design not only improves testing efficiency but also reduces manual intervention, lowers operational difficulty, and reduces the probability of errors.
[0019] 5) This invention, by introducing a wind pressure sensor, can monitor changes in airflow pressure in real time. Combined with wind speed and temperature data, it can comprehensively evaluate the dynamic response of the heat dissipation system in high-altitude, high-speed environments. The wind pressure data can be used to optimize the fan control logic, avoid the decrease in simulation reliability caused by inaccurate airflow speed measurement, and improve the overall stability and robustness of the test system. Attached Figure Description
[0020] Figure 1 This is a control method for a heat dissipation module performance testing device for drone motor controllers. Detailed Implementation
[0021] To better understand the present invention, further description is provided below with reference to the accompanying drawings; however, the embodiments of the present invention are not limited thereto. Furthermore, all other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.
[0022] like Figure 1 As shown, a method for testing the performance of a heat dissipation module for a drone motor controller is provided. The method is used to test the performance indicators of the heat dissipation module under test, which includes fins, heat pipes, and a thermally conductive aluminum plate.
[0023] A heat dissipation module testing device is constructed, and a fixed connection is established between the heat dissipation module testing device and the heat dissipation module under test; the heat dissipation module testing device for the UAV motor controller includes a sample positioning unit and a multimodal sensing test adjustment unit; the sample positioning unit is used to determine the position of the heat dissipation module under test in the testing device.
[0024] Using the heat dissipation module as the test object, a forced air-cooling environment suitable for UAVs is generated by the multimodal sensing test adjustment unit in the heat dissipation module test device. The power consumption of the three heat-generating devices in the controller is simulated by the output power of the programmable power supply connected to the heating module.
[0025] The fins in the heat dissipation module under test directly bear the forced air cooling generated by the drone's flight. The heat pipe is the main heat-conducting element in the heat dissipation module, and the heat-conducting aluminum plate is directly connected to the end face of the heat-generating device in the controller.
[0026] The multimodal sensing and control unit includes a wind speed sensor, a wind pressure sensor, a thermal imaging sensor, and a multi-physical quantity coupling control system. The wind speed sensor and the wind pressure sensor measure the wind speed and wind pressure in the air duct, respectively. The thermal imaging sensor measures the temperature of the heat dissipation module. The three physical quantities of wind speed, wind pressure, and temperature are fed back to the multi-physical quantity coupling control system.
[0027] Includes the following steps: S1: Install the heat dissipation module to be tested into the sample positioning unit of the heat dissipation module testing device, and obtain the spatial position of the heat dissipation module fins; S2: Automatically adjust the spatial position of the heat dissipation module under test so that the forced air cooling generated by the multimodal sensing test adjustment unit only acts on the fins; S3: Determine the power of the heating module and turn on the programmable power supply of the heating module and the fan module in the multimodal sensing test and adjustment unit; S4: Monitor the dynamic data collected in real time by the wind speed sensor, wind pressure sensor, and thermal imaging sensor in the multimodal sensing test adjustment unit during the test process, and record the power data of the heating module; The heating module is heated by a programmable power supply, and the heat is transferred to the heat dissipation module to simulate the heat generation and heat dissipation of the motor controller of the UAV; the fan module starts the fan, and the output wind speed of the fan is adjusted to simulate the heat dissipation of the aircraft's heat dissipation system.
[0028] The multimodal sensing and control unit uses data fusion from thermal imaging and wind speed sensors, employing a PID control algorithm to dynamically adjust the heating module power and fan speed, achieving closed-loop control of the temperature and wind speed fields. The system coordinates the control priorities of temperature and wind speed through a fuzzy logic rule base. When the temperature error exceeds a set threshold, the heating module power is adjusted first; when the wind speed error exceeds a set threshold, the fan speed is adjusted first. The process includes the following steps: S1: The overall temperature field of the surface of the heat dissipation system is monitored in real time by a thermal imaging sensor, and the local temperature is measured at key points; the airflow velocity in the duct is measured by a wind speed sensor.
[0029] S2: According to Bernoulli's equation and dynamic pressure formula: , In the formula For dynamic pressure, ρ air density, V Let be the wind speed. The wind speed can be deduced from the above formula: , The wind pressure inside the duct is measured by a wind pressure sensor. The accuracy of the wind speed sensor data is judged by inversely calculating the wind speed verification formula. If the error is less than 5%, the wind speed sensor data is considered accurate; if the error exceeds 5%, the PID control algorithm is corrected as follows: , In the formula, α is the wind pressure compensation coefficient, which is used to correct the wind speed measurement error. This is the set value for the wind pressure sensor. These are the measured values from the wind pressure sensor.
[0030] S3: A weighted average method is used to weight the overall temperature and local temperature of the thermal imaging sensor to generate a comprehensive temperature value. , In the formula , The weighting coefficient is dynamically adjusted based on sensor accuracy and operating conditions.
[0031] Kalman filtering is applied to the wind speed sensor to eliminate noise and interference: , In the formula, K is the Kalman gain, which is updated in real time based on sensor error and system dynamic characteristics. This is the predicted data from the wind speed sensor.
[0032] S4: Establish a fuzzy logic rule base and define temperature error. and wind speed error A fuzzy set is used to convert sensor data into fuzzy variables through membership functions: , , In the formula , These represent temperature error and wind speed error, respectively. , These represent the membership functions based on temperature error and wind speed error, respectively.
[0033] Control commands are generated based on a rule base. "High temperature" and For "low wind speed", increase the heating power and increase the fan speed. It is "medium temperature" and If the wind speed is "medium", then maintain the current control level. "Low temperature" and For "high wind speed", the heating power is reduced and the fan speed is lowered.
[0034] S5: Based on the comprehensive temperature value The temperature error is calculated, and the power adjustment of the heating module is determined using a PID control algorithm. The power of the heating module is adjusted to bring the temperature closer to the set value. The filter fan speed value is also considered. Calculate wind speed error The fan speed adjustment is calculated by using a PID control algorithm, and the fan speed is adjusted to make the wind speed approach the set value.
[0035] The multimodal sensing test control unit employs a first-order PID control algorithm. The transfer function of the first-order system is expressed as: ， In the formula y ( t ) is the system output. u ( t ) is system input, K It is the system gain. τ It is the system's time constant.
[0036] The PID control algorithm for a first-order system is expressed as follows: ， In the formula, u ( t ) is the output of the controller. e ( t This represents the error between the set value and the actual value. It is a proportional gain, which can adjust the immediate response to the error; It is the integral gain, used to eliminate steady-state error; It is the differential gain, used to predict the trend of error changes.
[0037] Based on the actual discretization sampling, the PID control algorithm is further modified as follows: ， In the formula It is the controller output at time k. It is the error at time k, Δ t It is the sampling time interval.
[0038] Temperature and wind speed are decoupled for control. The temperature PID output controls the heating module power, and the wind speed PID output controls the fan speed. The PID control algorithm is further modified as follows: , , In the formula, and These represent the controller's temperature and wind speed outputs at time k, respectively. and These are the proportional gains of the temperature controller and the fan speed controller, respectively. and These are the integral gains of the temperature controller and the fan speed controller, respectively. and These are the differential gains of the temperature controller and the fan speed controller, respectively. and It is the first k The error in temperature and wind speed at any given time; and It is the first i Errors in temperature and wind speed at any given time.
[0039] The parameters of the PID controller are tuned using a trial-and-error method. Initial setup. , , For smaller values, , , Gradually increase Until the system responds quickly and without significant oscillations, an integral term is introduced. To eliminate steady-state error and avoid oscillations caused by integral saturation, a differential term is introduced. Easy-to-control overshoot improves system stability.
[0040] For the multimodal sensing test and adjustment unit, after the UAV motor controller heat dissipation system operating condition simulation device is started, the thermal imaging sensor and wind speed sensor begin operation. The thermal imaging sensor monitors the surface temperature distribution of the heat dissipation system in real time at a frequency of 10 frames per second, and selects key points to measure local temperature. The wind speed sensor measures the airflow velocity in the duct at a frequency of 5 times per second. The wind pressure sensor measures the airflow pressure in the duct at a frequency of 5 times per second. When the error between the wind speed verified by the wind pressure sensor and the wind speed detected by the wind speed sensor exceeds 5%, a corrected PID control system is adopted, and the α parameter is manually input to correct the wind speed measurement error. The measurement results are processed in real time by the industrial control computer to generate a comprehensive temperature value. and filtered wind speed value The system dynamically adjusts the heating module power and fan speed through a PID control algorithm to achieve closed-loop control of the temperature and wind speed fields; when the temperature error... When the wind speed error exceeds the set threshold, the heating module power is adjusted first; when the wind speed error exceeds the set threshold, the heating module power is adjusted first. At the same time, the fan speed is adjusted first; the measurement results are synchronized in the test and adjustment system.
[0041] The parts of this invention not described in detail are well-known to those skilled in the art.
Claims
1. A performance testing device for a heat dissipation module used in a drone motor controller, characterized in that, include: Sample positioning unit and multimodal sensing test adjustment unit; The sample positioning unit is used to install and fix the heat dissipation module to be tested. The heat dissipation module under test includes fins, heat pipes, and a thermally conductive aluminum plate. The fins are subjected to forced air cooling generated by the drone's flight. The heat pipes are the thermally conductive elements in the heat dissipation module. The thermally conductive aluminum plate is connected to the heating module. The multimodal sensing test and adjustment unit includes a wind speed sensor, a wind pressure sensor, a thermal imaging sensor, a fan module, a heating module, a heat dissipation module, and a multi-physical quantity coupling adjustment system; The heating module simulates the heat generation power of the drone motor controller. The heating module is heated by a programmable power supply, and the heat is conducted through the heat dissipation module to simulate the heat source conduction of the drone motor controller's heat dissipation module. The fan module adjusts the output air speed to simulate the heat dissipation of an aircraft's cooling system. An anemometer and a wind pressure sensor measure the wind speed and wind pressure in the duct, respectively; a thermal imaging sensor measures the surface temperature of the heat dissipation module; and the wind speed, wind pressure, and temperature are fed back to a multi-physical quantity coupled regulation system. The multi-physical quantity coupled regulation system dynamically adjusts the power of the heating module and the fan speed in the fan module through a PID control algorithm, thereby achieving closed-loop control of the temperature field and the wind speed field.
2. The performance testing device for a heat dissipation module for a drone motor controller according to claim 1, characterized in that, The multi-physical quantity coupled regulation system coordinates the control priorities of temperature and wind speed through a fuzzy logic rule base, and when temperature error occurs... When the wind speed error exceeds the set threshold, the heating module power is adjusted first; when the wind speed error exceeds the set threshold, the heating module power is adjusted first. When the speed exceeds the set threshold, the fan speed will be adjusted first, including: S1. The wind speed is inferred from the dynamic pressure formula of Bernoulli's equation and the wind pressure data in the duct measured by the wind pressure sensor. When the error between the wind speed inferred from the wind pressure sensor measurement data and the wind speed measured by the wind speed sensor is less than or equal to the set threshold a, the wind speed sensor measurement data is considered accurate. When the error between the wind speed inferred from the wind pressure sensor measurement data and the wind speed measured by the wind speed sensor is greater than the set threshold a, the PID control algorithm is used to correct the wind speed measurement error. S2, Generate comprehensive temperature value and filtered wind speed value ; S3. Establish a fuzzy logic rule base and define temperature error. and wind speed error The fuzzy set is used to convert sensor data into fuzzy variables through membership functions; S4. Generate control instructions based on the fuzzy logic rule base. and Then increase the heating power and increase the fan speed; if and If so, maintain the current control value; and Then reduce the heating power and lower the fan speed; S5. Based on the comprehensive temperature value Calculate temperature error The PID control algorithm calculates the power adjustment of the heating module and adjusts the power of the heating module to bring the temperature closer to the set value; based on the filtered wind speed value... Calculate wind speed error The fan speed adjustment is calculated by using a PID control algorithm, and the fan speed is adjusted to make the wind speed approach the set value.
3. The performance testing device for a heat dissipation module for a drone motor controller according to claim 2, characterized in that, The multi-physical quantity coupled regulation system adopts a first-order PID control algorithm, including: Temperature and wind speed are decoupled for control. The temperature PID output controls the heating module power, and the wind speed PID output controls the fan speed. The PID control algorithm is as follows: ， ， In the formula, The sampling time interval; and Representing the first k The temperature and fan speed output of the time controller; and These are the proportional gains of the temperature controller and the fan speed controller, respectively. and These are the integral gains of the temperature controller and the fan speed controller, respectively. and These are the differential gains of the temperature controller and the fan speed controller, respectively. and It is the first k The error in temperature and wind speed at any given time; and It is the first i Errors in temperature and wind speed at any given time.
4. The performance testing device for a heat dissipation module for a drone motor controller according to claim 3, characterized in that, The method for correcting wind speed measurement error using PID control algorithm is as follows: ， in, This is the wind pressure compensation coefficient, used to correct wind speed measurement errors; For the first k The wind speed output of the time controller; The proportional gain of the wind speed controller; For the first k Wind speed error at any given time; This is the integral gain of the wind speed controller; For the first i Wind speed error at any given time; The differential gain of the wind speed controller; This is the set value for the wind pressure sensor. These are the measured values from the wind pressure sensor.
5. The performance testing device for a heat dissipation module for a drone motor controller according to claim 4, characterized in that, The overall temperature of the thermal imaging sensor was determined using a weighted average method. and local temperature Weighted values are then used to generate a composite temperature value: ， In the formula, , These are the weighting coefficients.
6. The performance testing device for a heat dissipation module for a drone motor controller according to claim 5, characterized in that, Measurement data from the wind speed sensor Perform Kalman filtering to obtain the filtered wind speed value. : In the formula, K is the Kalman gain. This is the predicted data from the wind speed sensor.
7. The performance testing device for a heat dissipation module for a drone motor controller according to claim 6, characterized in that, The establishment of the fuzzy logic rule base includes: ， ， In the formula, , These represent temperature error and wind speed error, respectively. , These represent the membership functions based on temperature error and wind speed error, respectively.
8. The performance testing device for a heat dissipation module for a drone motor controller according to claim 7, characterized in that, After the heat dissipation module for the UAV motor controller is installed, the positioning device in the sample positioning unit is activated, causing the positioning device to move to the same central axis plane as the fan module, and the position is calibrated by a laser rangefinder.
9. The test method for a heat dissipation module performance testing device for a UAV motor controller according to any one of claims 1 to 8, characterized in that, include: S1: Install the heat dissipation module to be tested in the sample positioning unit and obtain the spatial position of the heat dissipation module fins; S2: Adjust the spatial position of the heat dissipation module under test so that the air cooling generated by the fan module only acts on the fins; S3: Determine the power of the heating module and turn on the programmable power supply of the heating module and the fan module; S4: Monitor the wind speed sensor, wind pressure sensor, and thermal imaging sensor to collect dynamic data in real time during the test process, and record the power data of the heating module.
10. The test method according to claim 9, characterized in that, The control priorities of temperature and wind speed are coordinated through a fuzzy logic rule base, and when temperature error occurs... When the wind speed error exceeds the set threshold, the heating module power is adjusted first; when the wind speed error exceeds the set threshold, the heating module power is adjusted first. When the speed exceeds the set threshold, the fan speed will be adjusted first.
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