A Multi-Parameter Online Adaptive Identification Method for Permanent Magnet Flux Observation

Abstract

This invention discloses a multi-parameter online adaptive identification method for permanent magnet flux linkage observation, belonging to the field of permanent magnet flux linkage observation technology. It is applicable to sensorless operation control algorithms for permanent magnet motors and open-loop VF motor control algorithms. The method includes a flux linkage observer, adaptive observation errors of permanent magnet flux linkage and speed, and accurate and stable observation switching conditions. This invention proposes a multi-parameter online adaptive identification method for permanent magnet flux linkage observation, which can achieve collaborative identification of speed, motor position angle parameters, and permanent magnet flux linkage parameters. The adaptive permanent magnet flux linkage parameters can participate in subsequent motor parameter identification, solving the identification error caused by the fixed permanent magnet flux linkage parameters in traditional motor parameter identification. This helps reduce the observer's sensitivity to motor parameters, improves the identification accuracy of speed estimates and rotor position angle estimates, and thus further ensures the stable and reliable control of the motor control method.

Description

Technical Field

[0001] This invention relates to the field of permanent magnet flux linkage observation technology, specifically a multi-parameter online adaptive identification method for permanent magnet flux linkage observation. Background Technology

[0002] Permanent magnet motor drive systems are nonlinear and strongly coupled control systems. The complex and ever-changing needs of industrial control applications have led to the continuous expansion of the application of permanent magnet motor drive systems. In actual motor operation, when the motor's running stability and magnetic flux saturation change, the motor parameters will change significantly, leading to motor overheating. Overheating is generally a major cause of motor damage. Therefore, online identification of motor parameters is of great significance.

[0003] The flux linkage of permanent magnets is an important factor reflecting the health status of permanent magnet motors. However, external environmental conditions such as high temperature, mechanical vibration, or strong magnetic field can cause changes in the flux linkage parameters of permanent magnets. This places higher demands on the accurate and stable identification of permanent magnet flux linkage in industrial applications, especially in sensorless motor control algorithms. If the parameters in the sensorless method are inaccurate, rotor position estimation errors will occur, leading to system instability and deterioration of control performance. The accuracy of permanent magnet flux linkage is crucial to the performance of motor control. Existing methods for identifying permanent magnet flux linkages are mainly divided into offline and online methods. Offline methods do not consider the impact of temperature rise and magnetic saturation on the resistance and inductance parameters of permanent magnet motors when temperature and load change, resulting in lower identification accuracy. Online methods mainly focus on recursive least squares, model reference adaptation, and extended Kalman estimation. Modern control principle state estimation equations need to comprehensively consider multiple parameters of state variables such as motor resistance and inductance, and there are also coupling effects between parameters. Therefore, for sensorless control or open-loop VF control technology in motor control algorithm applications, the sampling state observer form is more stable, accurate, and reliable for estimating motor speed and position. The need for a multi-parameter online adaptive identification technology is an urgent problem to be solved in this invention.

[0004] Based on this, a multi-parameter online adaptive identification method for permanent magnet flux linkage observation is now provided, which can eliminate the drawbacks of existing technical solutions. Summary of the Invention

[0005] The purpose of this invention is to provide a multi-parameter online adaptive identification method for permanent magnet flux linkage observation, so as to solve the problems of insufficient identification accuracy of permanent magnet flux linkage and the coupling effect of parameters in the background technology.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A multi-parameter online adaptive identification method for permanent magnet flux linkage observation is proposed, applicable to sensorless operation control algorithms for permanent magnet motors and open-loop VF motor control algorithms. The method includes a flux linkage observer, adaptive observation errors for permanent magnet flux linkage and speed, and accurate and stable observation switching conditions. The method obtains the current and voltage values ​​on the d and q axes of the motor's rotating coordinate system and inputs them into the flux linkage observer. The estimated rotational speed and flux linkage value are iteratively fed back into the flux linkage observer. The equation framework for observing the flux linkage is as follows:

[0008]

[0009]

[0010] in To observe the magnetic flux linkage, u is the voltage. Where K is the observed velocity, e is the flux linkage adaptive gain coefficient, L is the flux linkage error vector, and i is the current. It is a permanent magnet flux linkage.

[0011] Furthermore, the equation framework for observing the magnetic flux includes corresponding formulas for J, the adaptive gain coefficient K of the magnetic flux, and the inductance matrix L, as follows:

[0012]

[0013] Furthermore, the decomposed flux linkage observations are calculated using d-axis and q-axis decomposition:

[0014] d-axis:

[0015]

[0016] q-axis:

[0017]

[0018] The observed flux linkages along the d and q axes are calculated using rotating coordinate systems based on the d and q axes, and the observation calculations of the permanent magnet flux linkage and the adaptive error vector are performed.

[0019] Furthermore, the excitation current and torque current of the d-axis and q-axis are sampled and fed back, and the observed rotor flux linkage and permanent magnet flux linkage are calculated using the state equation expression. And calculate the flux linkage error vector e, with the adaptive observation error input as follows:

[0020] δ1=λ T Je

[0021] δ2=λ T e

[0022]

[0023] in, δ1 is the adaptive error of velocity observation, δ2 is the adaptive error of flux linkage observation, and k f This represents the adaptive observation gain coefficient for magnetic flux linkage.

[0024] Furthermore, the adaptive observation error is substituted into the adaptive control, appropriate adaptive observer control coefficients are selected, and the gain coefficient of the adaptive flux linkage is calculated. Based on modern control principles, the state equation is constructed as follows:

[0025]

[0026]

[0027]

[0028] The flux linkage observation calculation is performed using the modern control state equation form. The equation framework of the observed flux linkage is transformed into a state equation, and the feedback gain and online adaptive coefficient are selected based on the state equation.

[0029] Furthermore, the state equations A, B1, B2, x, C1, and C2 are each provided with corresponding formulas as follows:

[0030]

[0031]

[0032] D1 = k p λ q0

[0033] C2 =

[0100]

[0034] in,

[0035] Furthermore, based on the observability and stability criteria of the linearized state equation, the gain coefficient K of the adaptive closed loop is selected:

[0036]

[0037] K 12 =-βk 11

[0038] K 22 =-βk 21

[0039]

[0040] Where a, b, and c are all design constants, and a, b, and c are all greater than 0.

[0041] Furthermore, after stability analysis, eigenvalue analysis can constrain the range of the gain coefficient K, thereby determining the selection of the gain coefficient K for the adaptive observer. Using modern control principles and state equation calculations, the flux linkage adaptive parameters are calculated, and the coefficients of the velocity loop adaptive parameters are selected.

[0042]

[0043] k p =ω0

[0044] ω0≈20*ω speed

[0045]

[0046] a = 0.1ω rate

[0047]

[0048] Where, ω rate This is the rated angular frequency of the motor.

[0049] Furthermore, the maximum threshold of the q-axis current is set to i. qMin When the rotational speed is low, the minimum threshold for observing the speed is set to

[0050] Furthermore, ω0 is set as the velocity loop bandwidth.

[0051] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0052] 1. This invention proposes a multi-parameter online adaptive identification method for permanent magnet flux linkage observation, which can achieve collaborative identification of speed, motor position angle parameters and permanent magnet flux linkage parameters. The adaptive permanent magnet flux linkage parameters can participate in subsequent motor parameter identification, solving the identification error caused by the fixed permanent magnet flux linkage parameters in traditional motor parameter identification. This helps to reduce the sensitivity of the observer to motor parameters, improve the identification accuracy of speed estimation and rotor position angle estimation, and thus further ensure the stable and reliable control of the motor control method.

[0053] 2. This invention proposes an adaptive observation coefficient k for adaptive permanent magnet flux linkage observation tuning. 11 k 12 k 21 k 22 The parameter calculation is based on the selected values, and the adaptive observation gain coefficient k of the magnetic flux linkage is used. fThe selection of equal coefficients is of great significance for the engineering implementation of this invention. In order to improve the operability of calculating the engineering coefficients, the gain calculation coefficients a, b, and c are equivalent substitutes for the calculation formula, which can ensure the global stability of the observer's adaptation.

[0054] 3. This invention designs a state observer for online observation of permanent magnet flux linkage and a position observer for adaptive observation speed. It further analyzes and proposes important correlation factors affecting the stability of the observed permanent magnet flux linkage and the influence of the observation feedback speed. By setting threshold conditions, it can better ensure the reliability and stability of the full-domain control of speed or torque control in the motor control process. Attached Figure Description

[0055] Figure 1 This is a schematic diagram of the structure of the present invention.

[0056] Figure 2 This is a schematic diagram of the algorithm of the present invention.

[0057] Figure 3 This is a schematic diagram of the q-axis current and observed velocity of the present invention. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0059] In this embodiment, as Figures 1-3 As shown, a multi-parameter online adaptive identification method for permanent magnet flux linkage observation is applicable to sensorless operation control algorithms for permanent magnet motors and open-loop VF motor control algorithms. It is used for accurate and reliable adaptive speed observation and motor control, including a flux linkage observer, adaptive observation errors of permanent magnet flux linkage and speed, and accurate and stable observation switching conditions. Using the state control equations of modern control principles, the global stability of the control is analyzed, and relevant adaptive control parameters are designed to obtain a stable and reliable permanent magnet flux linkage for the motor and to couple the observation of the motor's position and speed. The current and voltage values ​​on the d and q axes of the motor's rotating coordinate system are obtained and input into the flux linkage observer. The feedback-estimated speed and flux linkage values ​​are iterated back into the flux linkage observer. The equation framework for observing the flux linkage is as follows:

[0060]

[0061] in To observe the magnetic flux linkage, u is the voltage. Where K is the observed velocity, e is the flux linkage adaptive gain coefficient, L is the flux linkage error vector, and i is the current. It is a permanent magnet flux linkage.

[0062] Among them, such as Figure 1 and Figure 2 As shown, the equation framework for the observed flux linkage includes J, the adaptive gain coefficient K, and the inductance matrix L, all of which have corresponding formulas, as follows:

[0063]

[0064] Among them, such as Figure 1 and Figure 2 As shown, the decomposed flux linkage observations are calculated using d-axis and q-axis decomposition:

[0065] d-axis:

[0066]

[0067] q-axis:

[0068]

[0069] The observed magnetic flux linkages along the d and q axes are calculated based on the rotating coordinate systems of the d and q axes, respectively. The observation calculations of the permanent magnet flux linkage and the adaptive error vector are performed to further ensure the switching conditions for the stability of accurate and reliable observations.

[0070] Among them, such as Figure 1 and Figure 2 As shown, the excitation current and torque current of the d and q axes are sampled by input feedback, and the observed rotor flux linkage and permanent magnet flux linkage are calculated using the state equation expression. And calculate the flux linkage error vector e, with the adaptive observation error input as follows:

[0071] δ1=λ T Je

[0072] δ2=λ T e

[0073]

[0074] in, δ1 is the adaptive error of velocity observation, δ2 is the adaptive error of flux linkage observation, and k f This represents the adaptive observation gain coefficient for magnetic flux linkage.

[0075] Among them, such as Figure 1 and Figure 2 As shown, the adaptive observation error is substituted into the adaptive control, a suitable adaptive observer control coefficient is selected, and the gain coefficient of the selected flux linkage is calculated. Based on modern control principles, the state equation is constructed as follows:

[0076]

[0077] The flux linkage observation calculation is performed using the modern control state equation form. The equation framework of the observed flux linkage is transformed into a state equation, and the feedback gain and online adaptive coefficient are selected based on the state equation.

[0078] Among them, such as Figure 1 and Figure 2 As shown, A, B1, B2, x, C1, and C2 in the state equation are each assigned a corresponding formula as follows:

[0079]

[0080] D1 = k p λ q0

[0081] C2 =

[0100]

[0082] in,

[0083] Among them, such as Figure 1 and Figure 2 As shown, based on the observability and stability criteria of the linearized state equation, the gain coefficient K of the adaptive closed loop is selected:

[0084]

[0085] K 12 =-βk 11

[0086] K 22 =-βk 21

[0087]

[0088] Where a, b, and c are all design constants, and a, b, and c are all greater than 0. By using the gain calculation coefficients a, b, and c to equivalently replace the calculation formula, the selection rule can ensure the global stability of the observer's adaptation.

[0089] Among them, such as Figure 1 and Figure 2 As shown, after stability analysis, the range of the gain coefficient K can be constrained based on eigenvalue analysis, thereby determining the selection of the gain coefficient K of the adaptive observer. Using modern control principles and state equation calculations, the flux linkage adaptive parameters are calculated, and the coefficients of the velocity loop adaptive parameters are selected.

[0090]

[0091] k p =ω0

[0092] ω0≈20*ω speed

[0093]

[0094] a = 0.1ω rate

[0095]

[0096]

[0097] Where, ω rate This is the rated angular frequency of the motor.

[0098] Among them, such as Figure 3 As shown, the maximum threshold of the q-axis current is set to i. qMin When the rotational speed is low, the minimum threshold for observing the speed is set to Using threshold conditions helps to better ensure the reliability and stability of torque control across the entire control range or the speed of the motor control process.

[0099] Among them, such as Figure 2 As shown, ω0 is set as the speed loop bandwidth, which further ensures the stable and reliable control of the motor control method.

[0100] In practice, flux linkage observation is greatly affected by load fluctuations and speed, especially at low speeds where accurate flux linkage estimation is difficult. The above-mentioned scheme proposes to obtain a stable flux linkage observer by low-pass filtering the adaptive flux linkage during sampling. It adopts a switching condition based on the excitation current and torque current of the d and q axes that reflect the load conditions, which ensures accurate and stable flux linkage estimation. By using the state control equations of modern control principles, relevant adaptive control parameters are designed to obtain a stable and reliable permanent magnet flux linkage for important motors and to couple and observe the position and speed of the motor.

[0101] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A multi-parameter online adaptive identification method for permanent magnet flux linkage observation, applicable to sensorless and open-loop VF motor control algorithms for permanent magnet motor control, characterized in that, This includes a flux linkage observer, permanent magnet flux linkage and speed adaptive observation error, and accurate and stable observation switching conditions. By acquiring the current and voltage values ​​on the d and q axes of the motor's rotating coordinate system, and inputting them into the flux linkage observer, the feedback-estimated speed and flux linkage value are iterated into the flux linkage observer. The equation framework for observing the flux linkage is as follows: in To observe the magnetic flux linkage, u is the voltage. Let K be the observed velocity, K be the adaptive gain coefficient of the magnetic flux linkage, e be the magnetic flux linkage error vector, L be the inductance matrix, and i be the current. It is a permanent magnet flux linkage.

2. The multi-parameter online adaptive identification method for permanent magnet flux linkage observation according to claim 1, characterized in that, The equation framework for the observed flux linkage includes J, the adaptive gain coefficient K, and the inductance matrix L, all of which have corresponding formulas, as follows:

3. The multi-parameter online adaptive identification method for permanent magnet flux linkage observation according to claim 2, characterized in that, The decomposed flux linkage observations are calculated using d-axis and q-axis decomposition: d-axis: q-axis: The observed flux linkages along the d and q axes are calculated using rotating coordinate systems based on the d and q axes, and the observation calculations of the permanent magnet flux linkage and the adaptive error vector are performed.

4. The multi-parameter online adaptive identification method for permanent magnet flux linkage observation according to claim 1, characterized in that, Input the d-axis and q-axis excitation currents and torque currents from the feedback sampling, and calculate the observed rotor flux linkage and permanent magnet flux linkage using the state equation expression. And calculate the flux linkage error vector e, with the adaptive observation error input as follows: δ1=λ T Yes δ2=λ T e in, δ1 is the adaptive error of velocity observation, δ2 is the adaptive error of flux linkage observation, and k f This represents the adaptive observation gain coefficient for magnetic flux linkage.

5. The multi-parameter online adaptive identification method for permanent magnet flux linkage observation according to claim 4, characterized in that, Substituting the adaptive observation error into the adaptive control, selecting appropriate adaptive observer control coefficients, calculating the gain coefficient of the selected flux linkage adaptively, and constructing the state equation based on modern control principles, as follows: The flux linkage observation calculation is performed using the modern control state equation form. The equation framework of the observed flux linkage is transformed into a state equation, and the feedback gain and online adaptive coefficient are selected based on the state equation.

6. The multi-parameter online adaptive identification method for permanent magnet flux linkage observation according to claim 5, characterized in that, The state equations A, B1, B2, x, C1, and C2 are each assigned a corresponding formula as follows: D1=k p l q0 C2＝[0100] in, 7. The multi-parameter online adaptive identification method for permanent magnet flux linkage observation according to claim 2, characterized in that, Based on the observability and stability criteria of the linearized state equation, the gain coefficient K of the adaptive closed loop is selected: K 12 =-βk 11 K 22 =-βk 21 Where a, b, and c are all design constants, and a, b, and c are all greater than 0.

8. The multi-parameter online adaptive identification method for permanent magnet flux linkage observation according to claim 7, characterized in that, After stability analysis, eigenvalue analysis can constrain the range of the gain coefficient K, thereby determining the selection of the gain coefficient K for the adaptive observer. Using modern control principles and state equation calculations, the flux linkage adaptive parameters are calculated, and the coefficients of the velocity loop adaptive parameters are selected. k p =ω0 ω0≈20*ω speed a=0.1ω rate Where, ω rate This is the rated angular frequency of the motor.

9. The multi-parameter online adaptive identification method for permanent magnet flux linkage observation according to claim 1, characterized in that, The maximum threshold of the q-axis current is set to i. qMin When the rotational speed is low, the minimum threshold for observing the speed is set to 10. The multi-parameter online adaptive identification method for permanent magnet flux linkage observation according to claim 8, characterized in that, ω0 is set as the velocity loop bandwidth.

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