Sensorless control temperature identification method for unmanned aerial vehicle power system

Abstract

The invention discloses an unmanned aerial vehicle power system sensorless control temperature identification method, and belongs to the technical field of motor control. The method aims at solving the problem that temperature identification in a permanent magnet synchronous motor driving system of an unmanned aerial vehicle depends on rotor position information, and closed-loop coupling contradiction exists between the temperature identification and rotor position estimation needed by sensorless control. Comprising the following steps: establishing a permanent magnet synchronous motor complex vector model based on extended back electromotive force in an estimated synchronous rotation gamma-delta reference system; establishing a full-order state sliding mode observer based on a complex vector by taking the stator current and the extended counter electromotive force as state variables; performing forward Euler discretization to obtain a full-order state sliding mode observer in a discrete domain, and observing a stator current estimated value and an extended back electromotive force estimated value; obtaining a rotor observation position and a rotor observation rotating speed; and designing an affine projection algorithm, identifying the stator resistance in real time in combination with a segmented regularization factor strategy, and estimating based on the stator resistance obtained by identification to obtain a motor temperature estimation value. The method is used for motor temperature identification.

Description

Technical Field

[0001] The present invention relates to a temperature identification method for sensorless control of an unmanned aerial vehicle (UAV) power system, and belongs to the technical field of motor control. Background Art

[0002] The permanent magnet synchronous motor (PMSM) has become the core drive unit of the UAV power system due to its high efficiency, high power density, and excellent dynamic performance. With the wide application of UAVs in fields such as logistics and inspection. However, the stringent lightweight requirements and complex working conditions (such as high altitude and extreme temperature changes) of UAVs pose higher challenges to the reliability of the PMSM control system.

[0003] The precise control of a PMSM lies in the real-time and efficient acquisition of rotor position data and operating speed information. However, traditional PMSM control relies on mechanical position sensors, but these sensors have problems such as large volume, high cost, and susceptibility to environmental interference, making it difficult to meet the lightweight and high-reliability requirements of UAVs. Therefore, the "plug-and-play" sensorless control technology is more favored. The research on sensorless control can be divided into two types according to the operating speed of the motor: low-speed and medium-high-speed. Low-speed sensorless control of PMSMs usually adopts the signal injection method, where the rotor position error is obtained by processing the response signal of the injected high-frequency signal, and then the rotor position is obtained through links such as a phase-locked loop. Medium-high-speed sensorless control of PMSMs usually adopts the model method, where the rotor pole position information is estimated through the back electromotive force or flux linkage model of the fundamental frequency excitation. Among them, the sliding mode observer method has attracted much attention due to its high precision and strong robustness.

[0004] However, in the actual operation of UAVs, especially during heavy-load transportation or inspection in high-temperature environments, the PMSM often faces severe temperature rise. Excessive temperature may cause demagnetization of permanent magnets and insulation damage of stator windings, and even lead to the failure of the motor system, seriously affecting its reliability and service life. Therefore, for the sensorless PMSM drive system in UAVs, online temperature monitoring is of great significance for the safe operation of the system. Traditional temperature monitoring relies on built-in temperature sensors (such as thermocouples and thermistors), but they have limitations such as high hardware cost, space constraints, and slow dynamic response. For this reason, the online temperature identification method without temperature sensors has become a research trend, and the existing technologies are mainly divided into two categories: the method based on the thermal model and the method based on stator resistance identification.

[0005] Although progress has been made in sensorless control and temperature monitoring technologies, the following key problems still exist in the permanent magnet synchronous motor drive system of unmanned aerial vehicles: the traditional reduced-order sliding mode observer ignores the current dynamic characteristics, resulting in an increase in position estimation error during high-speed or load mutation; the temperature identification method relies on rotor position information, while sensorless control itself needs to estimate the rotor position, forming a closed-loop coupling contradiction with sensorless control, and the resistance identification algorithm with fixed parameters has a poor convergence speed and a large steady-state error during speed mutation.

[0006] Therefore, in order to improve the operation reliability and thermal safety of the sensorless control system of permanent magnet synchronous motors for unmanned aerial vehicles, it is of great theoretical significance and application value to study the sensorless control technology of full-order state sliding mode observers with online temperature identification. Summary of the Invention

[0007] Aiming at the problem that the temperature identification in the permanent magnet synchronous motor drive system of unmanned aerial vehicles depends on rotor position information and there is a closed-loop coupling contradiction with the need to estimate the rotor position in sensorless control itself, the present invention provides a sensorless control temperature identification method for the power system of unmanned aerial vehicles.

[0008] A sensorless control temperature identification method for the power system of unmanned aerial vehicles according to the present invention includes: Based on the stator voltage equation and flux linkage equation of the permanent magnet synchronous motor, a permanent magnet synchronous motor model based on the extended back electromotive force in the d-q coordinate system is obtained; an estimated synchronous rotating γ-δ reference frame is established, and the permanent magnet synchronous motor model based on the extended back electromotive force in the d-q coordinate system is transformed into the estimated synchronous rotating γ-δ reference frame to obtain a complex vector model of the permanent magnet synchronous motor based on the extended back electromotive force in the estimated synchronous rotating γ-δ reference frame; Based on the complex vector model of the permanent magnet synchronous motor, a state equation with stator current and extended back electromotive force as state variables in the estimated synchronous rotating γ-δ reference frame is obtained, and a full-order state sliding mode observer based on complex vectors is established based on the state equation; The full-order state sliding mode observer based on complex vectors is discretized by forward Euler to obtain a full-order state sliding mode observer in the discrete domain, which is used to observe the estimated value of stator current and the estimated value of extended back electromotive force; then, based on the estimated value of stator current and the estimated value of extended back electromotive force, a normalized orthogonal phase-locked loop is used to obtain the observed rotor position and the observed rotor speed; An affine projection algorithm is designed based on the estimated value of stator current and the estimated value of extended back electromotive force; the affine projection algorithm combines a piecewise regularization factor strategy to identify the stator resistance in real time, and then the motor temperature estimate value is estimated based on the identified stator resistance.

[0009] Advantages of the present invention: By integrating the dynamic characteristics of the full-order state sliding mode observer and the real-time temperature identification technology based on the affine projection algorithm, the method of the present invention improves the dynamic observation accuracy of the rotor position, realizes the decoupled estimation of temperature within the full speed range, improves the convergence and accuracy of identification, can effectively avoid the risk of permanent magnet demagnetization caused by model mismatch and parameter drift in the traditional scheme, and at the same time reduces the hardware cost and improves the system lightweight level, providing technical support for the long-term reliable operation of the UAV in extreme environments.

[0010] In the estimated synchronous rotating γ-δ reference frame of the present invention, based on the extended back electromotive force model, a full-order sliding mode observer with stator current and extended back electromotive force as state variables is established. Then, by using the motor model based on the extended back electromotive force in the estimated synchronous rotating γ-δ reference frame to design the affine projection algorithm, the dependence on the rotor position is avoided; the resistance is identified in real time by combining the piecewise regularization factor strategy, balancing the convergence speed and steady-state accuracy. The method of the present invention is used for the sensorless control system of the permanent magnet synchronous motor for UAVs, and can achieve good steady-state tracking performance and high estimation accuracy during both steady-state operation and acceleration / deceleration operation, and shows good convergence and small steady-state error during the acceleration / deceleration operation process, effectively improving the convergence speed and accuracy of identification, and realizing the high-precision, safe and stable operation of the UAV permanent magnet synchronous motor under sensorless conditions.

[0011] Under the condition of ensuring the lightweight and reliability of the UAV drive system, the method of the present invention significantly improves the position estimation accuracy and temperature monitoring real-time performance through the full-order state sliding mode observer and online temperature decoupled identification, improves the practicability of the method of the present invention, and provides an innovative solution for high-reliability and lightweight motor control. Description of the Drawings

[0012] Figure 1 is the overall block diagram of the sensorless control temperature identification method for the UAV power system of the present invention; Figure 2 is the relative position schematic diagram of the estimated synchronous rotating γ-δ reference frame and the axis system and the d-q coordinate system; Figure 3 is the online temperature identification flow chart of the affine projection algorithm; Figure 4 is the comparison diagram of the encoder speed and the observer speed in the first embodiment; Figure 5 is the angle error schematic diagram in the first embodiment; Figure 6 is the schematic diagram of the experimental results of stator temperature identification and error analysis when the motor speed in the second embodiment accelerates in a step form of 500 rpm; Figure 7It is a schematic diagram of the experimental results of stator temperature identification and error analysis when the motor speed in Embodiment 2 decelerates in a step form of 500 rpm. Detailed implementation manners

[0013] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0014] It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other.

[0015] The present invention will be further described below in conjunction with the accompanying drawings, but it is not limited to the present invention.

[0016] Combined Figures 1 to 3 As shown, the present invention provides a method for sensorless control temperature identification of an unmanned aerial vehicle power system, including: Based on the stator voltage equation and flux linkage equation of a permanent magnet synchronous motor, a permanent magnet synchronous motor model based on the extended back electromotive force in the d-q coordinate system is obtained; an estimated synchronous rotating γ-δ reference frame is established, and the permanent magnet synchronous motor model based on the extended back electromotive force in the d-q coordinate system is transformed into the estimated synchronous rotating γ-δ reference frame to obtain a complex vector model of the permanent magnet synchronous motor based on the extended back electromotive force in the estimated synchronous rotating γ-δ reference frame; Based on the complex vector model of the permanent magnet synchronous motor, a state equation with stator current and extended back electromotive force as state variables in the estimated synchronous rotating γ-δ reference frame is obtained, and a full-order state sliding mode observer based on the complex vector is established based on the state equation; The full-order state sliding mode observer based on the complex vector is discretized by forward Euler to obtain a full-order state sliding mode observer in the discrete domain, which is used to observe the estimated value of the stator current and the estimated value of the extended back electromotive force; then, based on the estimated value of the stator current and the estimated value of the extended back electromotive force, a normalized orthogonal phase-locked loop is used to obtain the rotor observation position and the rotor observation speed; An affine projection algorithm is designed based on the estimated value of the stator current and the estimated value of the extended back electromotive force; the affine projection algorithm combines a piecewise regularization factor strategy to identify the stator resistance in real time, and then an estimated value of the motor temperature is obtained based on the identified stator resistance.

[0017] Further, in the d-q coordinate system, the stator voltage equation and flux linkage equation of the permanent magnet synchronous motor are: (1), (2), Wherein is the d-axis stator voltage, is the q-axis stator voltage, is the stator resistance, is the d-axis stator current, is the q-axis stator current, and p is the differential operator, is the d-axis stator flux linkage, is the q-axis stator flux linkage, is the electrical angular velocity of the motor; is the d-axis stator inductance, is the q-axis stator inductance, is the permanent magnet flux linkage; By transforming Formulas (1) and (2), a permanent magnet synchronous motor model based on the extended back electromotive force in the d-q coordinate system is obtained: (3), (4), Wherein is the extended back electromotive force.

[0018] The method for obtaining the complex vector model of the permanent magnet synchronous motor based on the extended back electromotive force in the estimated synchronous rotating γ-δ reference frame is as follows: Write Formulas (3) and (4) in complex vector form: (5), (6), Wherein is the stator voltage in the d-q axis system in complex vector form, is the stator current in the d-q axis system in complex vector form, is the extended back electromotive force in complex vector form; Formulas (5) and (6) show the complex vector structure of the permanent magnet synchronous motor. By respectively converting the d-q axis system components into the real part and the imaginary part in the complex vector model, the two-dimensional structure of the original model can be converted into a one-dimensional structure to simplify subsequent analysis.

[0019] Combining Figure 2 as shown, an estimated synchronous rotating γ-δ reference frame is established, and Formulas (5) and (6) are transformed into the estimated synchronous rotating γ-δ reference frame to obtain the complex vector model of the permanent magnet synchronous motor based on the extended back electromotive force in the estimated synchronous rotating γ-δ reference frame: (7), (8), (9), where is the estimated stator voltage in the synchronous rotating γ-δ reference frame, is the observed rotor speed, is the estimated stator current in the synchronous rotating γ-δ reference frame, is the estimated extended back electromotive force in the synchronous rotating γ-δ reference frame; is the rotor position error, is the rotor speed error; is the rotor position, is the observed rotor position.

[0020] Based on equations (7) and (8), the state equation with stator current and extended back electromotive force as state variables in the estimated synchronous rotating γ-δ reference frame is obtained: (10), where represents the derivative of with respect to, represents the derivative of with respect to; Based on equation (10), a full-order state sliding mode observer based on complex vectors is established: (11), where: (12), where is the estimated value of , is the derivative of with respect to, is the estimated value of , is the feedback gain matrix of the full-order state sliding mode observer based on complex vectors; is the second-order system damping ratio of the full-order state sliding mode observer based on complex vectors, is the second-order system angular frequency of the full-order state sliding mode observer based on complex vectors.

[0021] A feedback correction channel is added to the design of the full-order state sliding mode observer. When the observed state of the observer is not equal to the actual state of the system, it is reflected that their outputs and are also not equal, so an error signal is generated and fed to the input end of the observer through the feedback gain matrix K to participate in adjusting the state of the observer, so that it approaches the true state of the system with a certain accuracy and speed.

[0022] Performing forward Euler discretization on equation (11), the full-order state sliding mode observer in the discrete domain is obtained: (13), where is the discrete sampling time of the drive system, is the sampling period, is the estimated value of the stator current, is the estimated value of the extended back electromotive force.

[0023] Through the full-order state sliding mode observer in the discrete domain, the and in the observed axis system can be obtained; for the estimated value of the extended back electromotive force a normalized orthogonal phase-locked loop is used to obtain the rotor observed position and the rotor observed speed .

[0024] Furthermore, in order to realize the online identification of the motor temperature, the method based on online resistance identification is preferred. This is because there is a clear linear relationship between the stator resistance and the motor temperature. By identifying the resistance value in real time, the temperature change of the stator winding can be accurately calculated.

[0025] The affine projection algorithm is used to estimate the motor resistance parameters. The discrete system time model of the affine projection algorithm is designed as: (14), where is the discrete system output matrix, is the discrete system input matrix, is the true value of the vector to be identified, is the identified value of the vector to be identified, is the iteration step size, the smaller it is, the smaller the steady-state error and the slower the convergence speed, and vice versa; is the regularization factor, is an adjustment parameter introduced to avoid numerical problems in the matrix inversion process and overcome possible singular situations of the algorithm; is the identity matrix.

[0026] The stator resistance is identified in real time according to formula (13) and formula (14): (15), where is the γ-axis component of the estimated value of the stator current , is the δ-axis component of the estimated value of the stator current , is the stator voltage 's γ-axis component, is the estimated value of the extended back electromotive force The γ-axis component of is the stator current The γ-axis component of is the stator current The δ-axis component of is the identified value of the stator resistance.

[0027] The affine projection algorithm is identified in real time by using a complex vector model of a permanent magnet synchronous motor based on the extended back electromotive force in the estimated synchronous rotating γ-δ reference frame. This is because the γ-δ reference frame contains the position information estimated by the sensorless observer, and at the same time the extended back electromotive force model includes the electromotive force generated by the permanent magnet and the stator inductance. That is to say, this model takes into account the influence of the rotor salient pole. Therefore, the affine projection algorithm using the complex vector model of the permanent magnet synchronous motor based on the extended back electromotive force provides a more accurate real-time estimation, and the entire system does not require any position sensors for variable transformation and can be directly applied to many fields, such as sensorless control of motors, condition monitoring, and fault detection.

[0028] At the same time, the affine projection algorithm is derived by using the observer current equation, and thus has stronger robustness under speed change conditions.

[0029] In order to improve the convergence speed and steady-state error of the algorithm identification, the segmentation method is adopted in this embodiment to select the regularization factor of the affine projection algorithm. By selecting appropriate regularization factors and iteration steps, the online temperature identification can be relatively fast and accurate, improving the algorithm performance.

[0030] Based on the identified value of the stator resistance The estimated value of the motor temperature is obtained: (16), where is the estimated value of the motor temperature, is the ambient temperature The resistance of the stator winding under is the resistance temperature coefficient.

[0031] Figure 1 In is the given value of the rotational speed, is the given value of the γ-axis component of the stator current, is the given value of the δ-axis component of the stator current, is the stator voltage The axis component, is the stator voltage The axis component, is the three-phase current, is the stator current The axis component, is the stator current Axial component; Figure 3 Medium is the regularization factor value in the low-speed section, is the regularization factor value in the medium-speed section, is the regularization factor value in the high-speed section; Figure 4 The observed speed in Medium is the rotor observed speed ; Figure 5 The angle error in Medium is the rotor position error ; Figure 6 The identified temperature in Medium is the estimated value of the motor temperature , where Y in the figure represents the temperature coordinate axis.

[0032] The following specific embodiments are used to verify the beneficial effects of the present invention: Embodiment 1:

[0033] To verify the effectiveness of the method of the present invention, verification is carried out on the built UAV experimental platform.

[0034] The main parameters of the permanent magnet assisted synchronous reluctance motor used are: rated voltage 44.4V, rated current 11A, d-axis inductance L d = 27.5e-6H, q-axis inductance L q = 42.2e-6H, number of pole pairs p = 21, stator resistance Rs = 0.09Ω, permanent magnet flux linkage = 0.002425Wb.

[0035] As Figure 4 and Figure 5 shown, are the experimental results of the full-order state sliding mode observer based on the extended back electromotive force, where the motor speed increases from 0 to the maximum speed in a step of 500 rpm, and 100% rated load is applied. As Figure 4 shown is the waveform of the encoder speed (actual speed) and the observer speed, and it can be seen that the speed error of the motor does not exceed ±10 rpm during rated steady-state operation; as Figure 5 shown is the rotor angle error, and it can be seen from Figure 5 that the maximum steady-state deviation of the electrical angle is at 3350 rpm, and the steady-state deviation of the electrical angle does not exceed 0.35 rad. The experimental results show that the sensorless control method of the present invention can achieve good steady-state tracking performance and high estimation accuracy during both steady-state operation and acceleration / deceleration operation, and the system has good load-bearing performance.

[0036] Embodiment 2: The key to the online identification of the parameters of the UAV sensorless control system is whether the algorithm identification value can track the actual value after the operating speed changes.

[0037] To verify the performance of the online temperature identification algorithm based on the affine projection algorithm proposed by the method of the present invention, Figure 6 For the experimental results of stator temperature identification and error analysis when the motor speed accelerates in a step form of 500 rpm, the online temperature estimation error is less than 3.02 °C, and the estimated stator temperature can quickly and accurately track the actually measured temperature. Figure 7 For the experimental results of stator temperature identification and error analysis when the motor speed decelerates in a step form of 500 rpm, the online temperature estimation error is less than 2.06 °C, and it can be seen that the stator temperature information can be accurately tracked in real time within the full speed range.

[0038] Figure 6 and Figure 7 The experimental results of... show that since the affine projection algorithm uses the piecewise method to select the regularization factor, it can still quickly and effectively estimate the motor temperature in the case of a step change in speed, and has good convergence and high identification accuracy during the acceleration or deceleration process, so as to realize the online monitoring of the UAV motor temperature within the full speed range.

[0039] Although the present invention has been described herein with reference to specific embodiments, it should be understood that these embodiments are merely examples of the principles and applications of the present invention. Therefore, it should be understood that many modifications can be made to the exemplary embodiments, and other arrangements can be designed, as long as they do not deviate from the spirit and scope of the present invention defined by the appended claims. It should be understood that different dependent claims and the features described herein can be combined in a manner different from that described in the original claims. It should also be understood that the features described in connection with a single embodiment can be used in other described embodiments.

Claims

1. A sensorless control temperature identification method for an unmanned aerial vehicle power system, characterized in that, Including: Based on the stator voltage equation and flux linkage equation of a permanent magnet synchronous motor, a permanent magnet synchronous motor model based on extended back electromotive force in the d-q coordinate system is obtained; an estimated synchronous rotating γ-δ reference frame is established, and the permanent magnet synchronous motor model based on extended back electromotive force in the d-q coordinate system is transformed into the estimated synchronous rotating γ-δ reference frame to obtain a complex vector model of the permanent magnet synchronous motor based on extended back electromotive force in the estimated synchronous rotating γ-δ reference frame; Based on the complex vector model of the permanent magnet synchronous motor, a state equation with stator current and extended back electromotive force as state variables in the estimated synchronous rotating γ-δ reference frame is obtained, and a full-order state sliding mode observer based on complex vectors is established based on the state equation; The full-order state sliding mode observer based on complex vectors is discretized by forward Euler method to obtain a full-order state sliding mode observer in the discrete domain, which is used to observe the estimated value of stator current and the estimated value of extended back electromotive force; then, based on the estimated value of stator current and the estimated value of extended back electromotive force, the rotor observation position and rotor observation speed are obtained by using a normalized orthogonal phase-locked loop; An affine projection algorithm is designed based on the estimated value of stator current and the estimated value of extended back electromotive force; the affine projection algorithm combines a piecewise regularization factor strategy to identify the stator resistance in real time, and then the estimated value of motor temperature is obtained based on the identified stator resistance.

2. The temperature identification method for sensorless control of an unmanned aerial vehicle power system according to claim 1, wherein The stator voltage equation and flux linkage equation of the permanent magnet synchronous motor are: （1）， （2）， where is the d-axis stator voltage, is the q-axis stator voltage, is the stator resistance, is the d-axis stator current, is the q-axis stator current, p is the differential operator, is the d-axis stator flux linkage, is the q-axis stator flux linkage, is the electrical angular velocity of the motor; is the d-axis stator inductance, is the q-axis stator inductance, is the permanent magnet flux linkage; By transforming formula (1) and formula (2), a permanent magnet synchronous motor model based on extended back electromotive force in the d-q coordinate system is obtained: （3）， （4）， In the formula is the extended back electromotive force.

3. The temperature identification method for sensorless control of an unmanned aerial vehicle power system according to claim 2, wherein The method for obtaining a complex vector model of the permanent magnet synchronous motor based on extended back electromotive force in the estimated synchronous rotating γ-δ reference frame is: Writing formula (3) and formula (4) in complex vector form: （5）， （6）， where is the stator voltage in the d-q axis system in the form of a complex vector, is the stator current in the d-q axis system in the form of a complex vector, is the extended back electromotive force in the form of a complex vector; Transforming formula (5) and formula (6) into the estimated synchronous rotating γ-δ reference frame to obtain a complex vector model of the permanent magnet synchronous motor based on extended back electromotive force in the estimated synchronous rotating γ-δ reference frame: （7）， （8）， （9）， where is the estimated stator voltage in the synchronous rotating γ-δ reference frame, is the observed rotor speed, is the estimated stator current in the synchronous rotating γ-δ reference frame, is the estimated extended back electromotive force in the synchronous rotating γ-δ reference frame; is the rotor position error, is the rotor speed error; is the rotor position, is the observed rotor position.

4. The temperature identification method for sensorless control of an unmanned aerial vehicle power system according to claim 3, wherein Based on formula (7) and formula (8), a state equation with stator current and extended back electromotive force as state variables in the estimated synchronous rotating γ-δ reference frame is obtained: （10）， In the formula denotes the derivative with respect to , and denotes the derivative with respect to . According to formula (10), a full-order state sliding mode observer based on complex vectors is established: （11）， Where: （12）， In the formula is 's estimated value, is to take the derivative, is 's estimated value, is the feedback gain matrix of the full-order state sliding mode observer based on the complex vector; is the second-order system damping ratio of the full-order state sliding mode observer based on the complex vector, is the second-order system angular frequency of the full-order state sliding mode observer based on the complex vector.

5. The temperature identification method for sensorless control of an unmanned aerial vehicle power system according to claim 4, wherein By discretizing formula (11) by forward Euler method, a full-order state sliding mode observer in the discrete domain is obtained: （13）， where is the discrete sampling time of the drive system, is the sampling period, is the estimated value of the stator current, is the estimated value of the extended back electromotive force.

6. The temperature identification method for sensorless control of an unmanned aerial vehicle power system according to claim 5, wherein Estimated value of extended back electromotive force A normalized orthogonal phase-locked loop is used to obtain the rotor observed position and the rotor observed speed .

7. The temperature identification method for sensorless control of an unmanned aerial vehicle power system according to claim 6, wherein The discrete system time model for designing the affine projection algorithm is: （14）， In the formula is the output matrix of the discrete system, is the input matrix of the discrete system, is the true value of the vector to be identified, is the identified value of the vector to be identified, is the iteration step size, is the regularization factor, is the identity matrix.

8. The sensorless control temperature identification method for the UAV power system according to claim 7, characterized in that The stator resistance is identified in real time according to formula (13) and formula (14): （15）， In the formula is the γ-axis component of the estimated stator current , is the δ-axis component of the estimated stator current , is the γ-axis component of the stator voltage , is the γ-axis component of the estimated extended back electromotive force , is the γ-axis component of the stator current , is the δ-axis component of the stator current , is the identified value of the stator resistance 9. The sensorless control temperature identification method for the UAV power system according to claim 8, characterized in that Based on the identified value of the stator resistance The estimated value of the motor temperature is obtained by estimation: （16）， Wherein is the estimated value of the motor temperature, is the ambient temperature is the resistance of the lower stator winding, is the temperature coefficient of resistance.

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