Spacecraft motor strong temperature drift environment-oriented adaptive control method without position sensor

Abstract

The application discloses a position sensorless adaptive control method for a strong temperature drift environment of a spaceflight motor. The method comprises the following steps: collecting three-phase currents of the motor and converting the three-phase currents into d-axis currents and q-axis currents; injecting a high-frequency voltage signal into a stator winding of the motor through an inverter, and filtering and extracting a high-frequency current component; identifying motor parameters based on an equivalent voltage model under high-frequency excitation, and dynamically updating the motor parameters based on the identified motor parameters; constructing a sliding mode observer, estimating motor states, obtaining a current error, substituting the estimated current into a motor voltage equation, obtaining a back electromotive force, and calculating a rotor position angle according to the back electromotive force; obtaining a low-speed position estimation value; constructing a weight function, fusing the estimated position obtained by the sliding mode observer, inputting the estimated position into a vector controller, and generating a PWM signal to control the inverter to drive the motor. Without increasing the hardware cost, the robustness, control accuracy and operation stability of the system in the strong temperature drift environment are significantly improved.

Description

Technical Field

[0001] This invention relates to the field of aerospace motor control technology, and more specifically to a sensorless adaptive control method for aerospace motors in environments with strong temperature drift. Background Technology

[0002] Currently, permanent magnet synchronous motors (PMSMs) are widely used in spacecraft actuators, attitude control systems, and various electromechanical drive units due to their advantages such as high power density, high efficiency, and compact structure. In these application scenarios, the reliability and environmental adaptability of the motor control system are directly related to the success or failure of space missions.

[0003] In traditional motor control systems, rotor position information is typically obtained using rotary transformers or photoelectric encoders. However, in aerospace applications, factors such as strong radiation, drastic temperature variations, and long-term on-orbit operation can cause position sensors to age, drift, or even fail. A position sensor malfunction will prevent the motor control system from operating normally, severely impacting system reliability. Therefore, sensorless control technology has become an important development direction for aerospace motor control.

[0004] Sensorless control methods mainly include observation methods based on back electromotive force (EMF) and methods based on high-frequency signal injection. In the medium-to-high speed operating range, methods such as sliding mode observers and extended Kalman filters are typically used to estimate the back EMF through a mathematical model of the motor, thereby obtaining rotor position information. However, in the zero-speed and low-speed ranges, due to the weak back EMF signal, high-frequency signal injection methods are often used to extract position information by analyzing the motor's response characteristics to high-frequency excitation.

[0005] However, existing sensorless methods generally rely on the accuracy of motor parameters, such as stator resistance and inductance. In the extreme environment of aerospace, motors undergo large-scale temperature changes from the sunlit side to the shaded side. Their stator resistance changes significantly with temperature, and inductance parameters also shift due to magnetic saturation and temperature effects, resulting in significant drift in motor parameters. When the control system still uses fixed parameters, this will cause observation model mismatch, thereby reducing position estimation accuracy and even causing observer divergence. Furthermore, during full-speed operation, switching between high-frequency injection methods in the low-speed range and model observation methods in the medium- and high-speed range is usually required. Existing technologies often employ threshold-based switching methods, which are prone to sudden changes in estimation errors, system oscillations, and even instability when parameter mismatch or significant changes in operating conditions occur, making it difficult to meet the high reliability and stability requirements of aerospace. In summary, the existing technology still has the following shortcomings: it lacks the ability to identify motor parameters online under strong temperature drift conditions, resulting in insufficient robustness of the observer; the model observation method is highly dependent on parameters and is prone to failure in extreme environments; and there is a lack of smooth transition mechanism between different observation methods, making it difficult to achieve stable control across the entire speed domain.

[0006] Therefore, how to propose a full-speed-domain sensorless control method that can achieve online adaptive updating of motor parameters under strong temperature drift environment and smoothly integrate low-speed and medium-to-high-speed observation methods, so as to improve the reliability and environmental adaptability of aerospace motor systems, is an urgent problem to be solved by those skilled in the art. Summary of the Invention

[0007] In view of the above problems, the present invention is proposed to provide a sensorless adaptive control method for aerospace motors in environments with strong temperature drift, which overcomes or at least partially solves the above problems.

[0008] To achieve the above objectives, the present invention adopts the following technical solution:

[0009] This invention provides a sensorless adaptive control method for aerospace motors in environments with severe temperature drift, comprising the following steps:

[0010] S1. Acquire the real-time three-phase current of the motor and convert it into d-axis current and q-axis current through Clark transformation and Park transformation;

[0011] S2. Inject a high-frequency voltage signal into the stator winding of the motor through the inverter, and extract the corresponding high-frequency current component through filtering. The high-frequency voltage signal is a voltage signal with a frequency higher than the rated base frequency of the motor, and the high-frequency current component is a current signal with a frequency higher than the rated base frequency of the motor.

[0012] S3. Identify motor parameters based on the equivalent voltage model under high-frequency excitation, and dynamically update the motor parameters based on the identified parameters;

[0013] S4. Construct a sliding mode observer based on the updated motor parameters to estimate the motor state. Obtain the current error based on the estimated current and the actual current. Substitute the estimated current into the motor voltage equation to obtain the back electromotive force. Calculate the rotor position angle based on the back electromotive force.

[0014] S5. Obtain the low-speed position estimate based on the high-frequency current response signal;

[0015] S6. Construct a weighting function, fuse the sliding mode observer to obtain the estimated position, input the estimated position into the vector controller, and generate a PWM signal to control the inverter to drive the motor.

[0016] Furthermore, in step S2, the high-frequency voltage signal is injected during the low-speed operation phase of the motor, which is the operating range where the motor speed is lower than a preset switching threshold.

[0017] Furthermore, the discrete expression of the high-frequency voltage signal is as follows:

[0018]

[0019] in, High-frequency injection voltage; The injected voltage amplitude is related to the bus voltage. It is a high-frequency angular frequency; This indicates the corresponding actual time.

[0020] Furthermore, in step S3, the motor parameters are identified, including stator resistance and equivalent inductance. The equivalent inductance is used to map and update the motor's d-axis inductance parameters and q-axis inductance parameters.

[0021] Further, in step S4, the formula for calculating the back electromotive force is:

[0022]

[0023] in, for Shaft back electromotive force; for Shaft voltage; for shaft current; for Shaft back electromotive force; for Shaft voltage; for shaft current, For stator resistance, For equivalent inductance, To control the cycle.

[0024] Further, in step S4, the expression for the sliding mode observer is:

[0025]

[0026] Where K is the observer gain, and sat(·) is the saturation function. Let be the current at time k.

[0027] Furthermore, the saturation function is defined as:

[0028]

[0029] The saturation function is used to reduce chattering in sliding mode control.

[0030] Furthermore, the expression for the weighting function is:

[0031]

[0032] in, Let be the weighting coefficient, satisfying This is used to adjust the proportion of different observation results; The switching threshold; This is a coefficient used to adjust the slope.

[0033] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a sensorless adaptive control method for aerospace motors in environments with strong temperature drift, which has the following beneficial effects:

[0034] This invention addresses the problem of decreased accuracy in traditional sensorless control caused by significant drift in parameters such as resistance and inductance of aerospace motors under severe temperature drift conditions. It introduces an online parameter identification method based on high-frequency signal injection to achieve real-time acquisition and dynamic updating of motor parameters. The updated parameters are then incorporated into a sliding mode observer to improve the accuracy of back EMF and rotor position estimation. Simultaneously, by constructing a continuous weighting function based on rotational speed, a smooth integration between the low-speed high-frequency injection method and the medium-to-high-speed sliding mode observer is achieved, avoiding abrupt changes and oscillations during traditional switching processes. Thus, stable and reliable sensorless control across the entire speed range is achieved without increasing hardware costs, significantly improving the system's robustness, control accuracy, and operational stability under severe temperature drift conditions. Attached Figure Description

[0035] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0036] Figure 1 This is a flowchart of the sensorless adaptive control method provided in the embodiments of the present invention;

[0037] Figure 2 This is a schematic diagram of the sensorless adaptive control method provided in an embodiment of the present invention.

[0038] Figure 3 This is a schematic diagram illustrating the principle of online parameter identification provided in this embodiment of the invention.

[0039] Figure 4 This is a schematic diagram of the sliding mode observer provided in an embodiment of the present invention. Detailed Implementation

[0040] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0041] This invention discloses a sensorless adaptive control method for aerospace motors in environments with severe temperature drift, such as... Figure 1 As shown, the specific steps include:

[0042] S1. Acquire the real-time three-phase current of the motor and convert it into d-axis current and q-axis current through Clark transformation and Park transformation;

[0043] S2. Inject a high-frequency voltage signal into the stator winding of the motor through the inverter, and extract the corresponding high-frequency current component through filtering. The high-frequency voltage signal is a voltage signal with a frequency higher than the rated base frequency of the motor, and the high-frequency current component is a current signal with a frequency higher than the rated base frequency of the motor.

[0044] S3. Identify motor parameters based on the equivalent voltage model under high-frequency excitation, and dynamically update the motor parameters based on the identified parameters;

[0045] S4. Construct a sliding mode observer based on the updated motor parameters to estimate the motor state. Obtain the current error based on the estimated current and the actual current. Substitute the estimated current into the motor voltage equation to obtain the back electromotive force. Calculate the rotor position angle based on the back electromotive force.

[0046] S5. Obtain the low-speed position estimate based on the high-frequency current response signal;

[0047] S6. Construct a weighting function, fuse the sliding mode observer to obtain the estimated position, input the estimated position into the vector controller, and generate a PWM signal to control the inverter to drive the motor.

[0048] This invention addresses the problem of decreased accuracy in traditional sensorless control caused by significant drift in parameters such as resistance and inductance of aerospace motors under severe temperature drift conditions. It introduces an online parameter identification method based on high-frequency signal injection to achieve real-time acquisition and dynamic updating of motor parameters. The updated parameters are then incorporated into a sliding mode observer to improve the accuracy of back EMF and rotor position estimation. Simultaneously, by constructing a continuous weighting function based on rotational speed, a smooth integration between the low-speed high-frequency injection method and the medium-to-high-speed sliding mode observer is achieved, avoiding abrupt changes and oscillations during traditional switching processes. Thus, stable and reliable sensorless control across the entire speed range is achieved without increasing hardware costs, significantly improving the system's robustness, control accuracy, and operational stability under severe temperature drift conditions.

[0049] The following is a detailed description of each of the above steps:

[0050] like Figure 1 As shown in the schematic diagram, in step S1, state acquisition and coordinate transformation are performed.

[0051] First, the motor's operating status is collected in real time to obtain the motor's three-phase current signal. Simultaneously, it acquires the current operating status parameters of the motor, including the motor speed. Control cycle and DC bus voltage , where k represents the discrete sampling time.

[0052] Three-phase current Converted to d-axis current via Clark and Park transforms and q-axis current .

[0053] In this embodiment, the motor operating status is collected using a current sensor, a voltage detection circuit, and an ADC sampling unit.

[0054] In step S2, high-frequency voltage injection and high-frequency current extraction are performed;

[0055] During the low-speed phase of the motor, to improve the observability of rotor position information, a high-frequency voltage signal is injected into the stator windings of the motor via an inverter, causing the motor to generate a corresponding high-frequency current response, the discrete expression of which is:

[0056]

[0057] in, High-frequency injection voltage; The injected voltage amplitude is related to the bus voltage. It is a high-frequency angular frequency; This indicates the corresponding actual time.

[0058] The motor current response signal is acquired, and the high-frequency current component corresponding to the high-frequency voltage signal is extracted using a filtering method. .

[0059] In this embodiment, the high-frequency voltage signal is a voltage signal with a frequency higher than the motor's rated base frequency, and the high-frequency current component is a current signal with a frequency higher than the motor's rated base frequency; the low-speed stage referred to in this embodiment is... stage.

[0060] In step S3, the motor parameters are identified online and updated recursively.

[0061] like Figure 3 As shown, based on the equivalent voltage model of the motor under high-frequency excitation, the motor parameters are identified online. The discrete motor model is as follows:

[0062]

[0063] Obtain motor parameters:

[0064]

[0065] in, For stator resistance, For high-frequency injection voltage, It is a high-frequency current component. The equivalent inductance can be used to update the d-axis and q-axis inductance parameters, and Alternatively, a mapping relationship can be established based on the motor structure for conversion. When the inductance value is less than the set threshold, the inductance value from the previous moment is kept unchanged to ensure calculation stability.

[0066] To improve the smoothness of parameter estimation, the identified motor parameters are dynamically updated:

[0067]

[0068] in, To update the coefficients, .

[0069] In step S4, a sliding mode observer is constructed to obtain the back electromotive force and position estimate;

[0070] like Figure 4 As shown, during the medium-to-high speed operation phase, a sliding mode observer is constructed based on the updated motor parameters to estimate the motor state. Its current prediction model is as follows:

[0071]

[0072] in, , Let d and q be the currents at time k+1; , Let d and q be the voltages at time k; , It is an inductor; The electric angular velocity of the motor; It is a permanent magnet flux linkage.

[0073] Constructing a sliding mode observer:

[0074]

[0075] Where K is the observer gain, and sat(·) is the saturation function, defined as:

[0076]

[0077] Saturation functions are used to reduce chattering in sliding mode control.

[0078] Based on the error between the estimated current and the actual current, the current error can be obtained:

[0079]

[0080] This error reflects the dynamic deviation between the model and the actual system and can be used to improve the accuracy of back EMF estimation.

[0081] Substituting the estimated current into the motor voltage equation, the back electromotive force can be obtained:

[0082]

[0083] in, for Shaft back electromotive force; for Shaft voltage; for shaft current; for Shaft back electromotive force; for Shaft voltage; for Axis current.

[0084] Calculate the rotor position angle based on the back electromotive force:

[0085]

[0086] To avoid quadrant error, it is preferable to use the two-parameter form of the arctangent function for calculation.

[0087] In step S5, low-speed position estimation is performed;

[0088] Low-speed position estimation is obtained based on the high-frequency current response signal:

[0089]

[0090] The function f(·) represents the relationship between the high-frequency current signal and the rotor position, which can be achieved through demodulation algorithms or lookup tables.

[0091] In step S6, the observer is fused with the control output;

[0092] To achieve a smooth transition between different observation methods, a weighting function is constructed:

[0093]

[0094] in, Let be the weighting coefficient, satisfying This is used to adjust the proportion of different observation results; The switching threshold; This is a coefficient used to adjust the slope.

[0095] The final position is obtained by fusion:

[0096]

[0097] Estimated location The input vector controller generates a PWM signal to control the inverter to drive the motor.

[0098] In aerospace motor applications, the drastic temperature changes in the operating environment, such as a temperature difference exceeding 100°C from the sunlit side to the shaded side, cause significant drift in the stator resistance and inductance parameters. This leads to mismatch in the observation model of traditional sensorless control methods, affecting the accuracy of rotor position estimation and even causing system instability. This invention proposes a sensorless adaptive control method for aerospace motors in environments with severe temperature drift. By real-time correction of motor parameters and smooth switching of the observer, the method improves the stability and robustness of the system under severe temperature drift conditions.

[0099] Example: Taking a certain type of aerospace permanent magnet synchronous motor as an example;

[0100] The specific parameters are as follows:

[0101] initial value of stator resistance d-axis and q-axis inductance Permanent magnet magnetic flux polar number =4, rated speed 400 / Control cycle Simultaneously set the high-frequency injection parameters: injection frequency. Corresponding angular frequency The injected voltage amplitude is 10% of the DC bus voltage.

[0102] Condition measurement;

[0103] Measuring the three-phase current of a motor And obtain the DC bus voltage .

[0104] Coordinate transformation;

[0105] Three-phase current Converted to d-axis current via Clark and Park transforms and q-axis current .

[0106] High-frequency voltage injection;

[0107] At low motor speed During this stage, a high-frequency voltage signal is injected into the stator winding through the inverter. ;

[0108]

[0109] High-frequency current extraction;

[0110] The motor current response signal is acquired, and the high-frequency current component corresponding to the high-frequency voltage signal is extracted using a filtering method. The filter center frequency is set to 2000Hz, and the bandwidth is ±10%.

[0111] Online identification of motor parameters;

[0112] Based on discrete motor model:

[0113]

[0114] Obtain motor parameters:

[0115]

[0116] During the temperature rise from 25℃ to 120℃, the resistance changes from 0.4Ω to approximately 0.75Ω, enabling online tracking.

[0117] Parameters are updated iteratively.

[0118] Dynamically update motor parameters:

[0119]

[0120] in, .

[0121] Sliding mode observer;

[0122] Constructing a discrete current model for the motor:

[0123]

[0124] And construct a sliding mode observer:

[0125]

[0126] Back electromotive force and position estimation;

[0127]

[0128] Rotor position:

[0129]

[0130] Low-speed position estimation;

[0131] Based on the high-frequency current response:

[0132]

[0133] Fusion function;

[0134] Construct the weight function:

[0135]

[0136] in , .

[0137] Fusion location:

[0138]

[0139] Control output;

[0140] Estimated location The input vector controller generates a PWM signal to control the inverter to drive the motor.

[0141] Within temperature range 60℃ Tested at 120℃:

[0142] ① The position estimation error has been reduced from ±8° in the traditional method to ±3°;

[0143] ②No obvious oscillation during the switching process;

[0144] ③ Stable operation across the entire speed range with no loss of lock;

[0145] ④ The system remained stable after 1000 temperature cycles.

[0146] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0147] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A sensorless adaptive control method for aerospace motors in environments with severe temperature drift, characterized in that, Includes the following steps: S1. Acquire the real-time three-phase current of the motor and convert it into d-axis current and q-axis current through Clark transformation and Park transformation; S2. Inject a high-frequency voltage signal into the stator winding of the motor through the inverter, and extract the corresponding high-frequency current component through filtering. The high-frequency voltage signal is a voltage signal with a frequency higher than the rated base frequency of the motor, and the high-frequency current component is a current signal with a frequency higher than the rated base frequency of the motor. S3. Identify motor parameters based on the equivalent voltage model under high-frequency excitation, and dynamically update the motor parameters based on the identified parameters; S4. Construct a sliding mode observer based on the updated motor parameters to estimate the motor state. Obtain the current error based on the estimated current and the actual current. Substitute the estimated current into the motor voltage equation to obtain the back electromotive force (EMF). Calculate the rotor position angle based on the back EMF. ; The expression for the sliding mode observer is: ； Where K is the observer gain, and sat(·) is the saturation function. Estimate the current at time k. The actual current at time k S5. Obtain the low-speed position estimate based on the high-frequency current response signal. ; S6. Construct a weighting function, fuse the sliding mode observer to obtain the estimated position, input the estimated position into the vector controller, and generate a PWM signal to control the inverter to drive the motor; The expression for the weighting function is: ； in, Let be the weighting coefficient, satisfying This is used to adjust the proportion of different observation results; The switching threshold; To adjust the slope coefficient, Let k be the electric angular velocity of the motor at time k; The expression for the estimated position obtained by the fused sliding mode observer is: 。 2. The sensorless adaptive control method for aerospace motors in environments with strong temperature drift, as described in claim 1, is characterized in that... In step S2, the high-frequency voltage signal is injected during the low-speed operation phase of the motor, which is the operating range where the motor speed is lower than a preset switching threshold.

3. The sensorless adaptive control method for aerospace motors in environments with strong temperature drift, as described in claim 2, is characterized in that... The discrete expression for the high-frequency voltage signal is: ； in, High-frequency injection voltage; The magnitude of the injected voltage is related to the bus voltage. It is a high-frequency angular frequency; This indicates the corresponding actual time.

4. The sensorless adaptive control method for aerospace motors in environments with strong temperature drift, as described in claim 1, is characterized in that... In step S3, the motor parameters are identified, including stator resistance and equivalent inductance. The equivalent inductance is used to map and update the motor's d-axis inductance parameters and q-axis inductance parameters.

5. The sensorless adaptive control method for aerospace motors in environments with strong temperature drift, as described in claim 1, is characterized in that... In step S4, the formula for calculating the back electromotive force is: ； in, for Shaft back electromotive force; for Shaft voltage; for shaft current; for Shaft back electromotive force; for Shaft voltage; for shaft current, For stator resistance, For equivalent inductance, To control the cycle.

6. The sensorless adaptive control method for aerospace motors in environments with strong temperature drift, as described in claim 1, is characterized in that... The saturation function is defined as: ； The saturation function is used to reduce chattering in sliding mode control.

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