Multi-parameter on-line self-adaptive permanent magnet flux linkage identification observation method

Abstract

The invention discloses a multi-parameter on-line self-adaptive identification permanent magnet flux linkage observation method, relates to the technical field of permanent magnet flux linkage observation, is suitable for a permanent magnet motor speed sensorless operation control algorithm and an open-loop VF motor control algorithm, and comprises a flux linkage observer, a permanent magnet flux linkage and speed self-adaptive observation error and an accurate and stable observation switching condition. According to the multi-parameter on-line self-adaptive identification permanent magnet flux linkage observation method provided by the invention, the cooperative identification of the rotating speed, the motor position angle parameter and the permanent magnet flux linkage parameter can be realized, and the self-adaptive permanent magnet flux linkage parameter can participate in the subsequent motor parameter identification; the method solves the identification error caused by the fixed permanent magnet flux linkage parameter in the traditional motor parameter identification, facilitates the reduction of the sensitivity of the observer to the motor parameter, improves the identification precision of the rotation speed estimation value and the rotor position angle estimation value, and further guarantees the stable and reliable control of the motor control method.

Description

Technical Field

[0001] The invention relates to the technical field of permanent magnet flux observation, in particular to a multi-parameter online adaptive identification permanent magnet flux observation method. Background Art

[0002] The permanent magnet motor drive system is a nonlinear, strongly coupled control system. The complex and changeable requirements of industrial control applications have led to the continuous expansion of the application of permanent magnet motor drive systems. During the actual operation of the motor, when the operating stability and magnetic flux saturation of the motor change, the motor parameters will change significantly, which will lead to motor overheating. The problem of motor overheating is generally an important cause of motor damage. Therefore, online identification of motor parameters is of great significance.

[0003] The flux linkage of a permanent magnet is an important factor that reflects the health status of a permanent magnet motor. However, external environmental conditions such as high temperature, mechanical vibration, or strong magnetic field will cause changes in the flux linkage parameters of the permanent magnet, which in turn puts forward higher requirements for accurately and stably identifying the flux linkage of a permanent magnet with multiple industrial applications and working conditions. Especially in the speed sensorless motor control algorithm, if the parameters in the speed sensorless method are inaccurate, errors in rotor position estimation will occur, leading to system instability and deterioration of control performance. The accuracy of the permanent magnet flux linkage is crucial to the performance of motor control. Existing schemes for identifying permanent magnet flux linkage are mainly divided into offline identification and online identification schemes. The offline identification scheme does not consider the influence of temperature rise and magnetic saturation on the resistance and inductance parameters of the permanent magnet motor when the temperature and load change, resulting in the need to improve the identification accuracy; while the online identification scheme mainly focuses on research directions such as recursive least squares, model reference adaptation, and extended Kalman estimation. The state estimation equation using modern control principles must comprehensively consider multiple parameters of state variables such as motor resistance and motor inductance parameters, and there is also coupling between the parameters. Therefore, for speed sensorless control or open-loop VF control technology in motor control algorithm applications, the sampling state observer form is more stable, accurate and reliable to estimate the motor speed and position. A multi-parameter online adaptive identification technology is urgently needed to be solved by the present invention.

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

[0005] The purpose of the present invention is to provide a multi-parameter online adaptive identification permanent magnet flux observation method to solve the problems in the background technology that the permanent magnet flux identification accuracy needs to be improved and the parameters have coupling effects.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A multi-parameter online adaptive identification permanent magnet flux observation method is applicable to permanent magnet motor speed sensorless operation control algorithm and open-loop VF motor control algorithm, including flux observer, permanent magnet flux and speed adaptive observation error and accurate and stable observation switching conditions. By obtaining the current and voltage values ​​on the d and q axes of the rotating coordinate system of the motor and inputting them into the above-mentioned flux observer, the feedback estimated speed and flux value are iterated into the flux observer. The equation framework for observing flux is as follows:

[0008]

[0009]

[0010] in is the observed flux, u is the voltage, is the observed speed, K is the flux adaptive gain coefficient, e is the flux error vector, L is the inductance matrix, i is the current, is the permanent magnet flux.

[0011] Furthermore, J, flux adaptive gain coefficient K, and inductance matrix L in the equation framework for observing flux are all provided with corresponding formulas, as follows:

[0012]

[0013] Furthermore, the decomposed magnetic flux observation is calculated by d and q axis decomposition:

[0014] d-axis:

[0015]

[0016] q-axis:

[0017]

[0018] The observed magnetic flux of the d and q axes are calculated respectively according to the d and q axis rotating coordinate systems, and the observation calculation of the permanent magnet magnetic flux and the calculation of the adaptive error vector are performed.

[0019] Furthermore, the excitation current and torque current of the d and q axes sampled by the input feedback are used to calculate the observed rotor flux and permanent magnet flux using the state equation expression. And calculate the flux error vector e, the adaptive observation error input is as follows:

[0020] δ1=λ T Je

[0021] δ2=λ T e

[0022]

[0023] in, δ1 is the speed observation adaptive error, δ2 is the flux observation adaptive error, k f is the flux linkage adaptive observation gain coefficient.

[0024] Furthermore, the adaptive observation error is brought into the adaptive control, the appropriate adaptive observer control coefficient is selected, the gain coefficient of the flux adaptive calculation flux is calculated, and the state equation is constructed according to the modern control principle as follows:

[0025]

[0026]

[0027]

[0028] The modern control state equation form is adopted to carry out the flux observation calculation, the equation framework of the observed flux is transformed into the state equation, and the feedback gain and the online adaptive coefficient are selected according to the state equation.

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

[0030]

[0031]

[0032] D1=k p λ q0

[0033] C2=

[0100]

[0034] in,

[0035] Furthermore, according to 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] Wherein a, b, and c are all designed constants, and a, b, and c are all greater than 0.

[0041] Furthermore, after stability analysis, the range of gain coefficient K can be constrained based on eigenvalue analysis, thereby determining the selection of gain coefficient K of the adaptive observer. Modern control principle analysis and state equation calculation are used to calculate the flux adaptive parameters and select the coefficients of the speed loop adaptive parameters:

[0042]

[0043] k p =ω0

[0044] ω0≈20*ω speed

[0045]

[0046] a=0.1ω rate

[0047]

[0048] Among them, ω rate 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 speed is low, the minimum threshold of the observed speed is set to

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

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] 1. The present invention proposes a multi-parameter online adaptive identification permanent magnet flux observation method, which can realize the coordinated identification of speed, motor position angle parameters and permanent magnet flux parameters. The adaptive permanent magnet flux parameters can participate in the subsequent motor parameter identification, which solves the identification error caused by the fixed permanent magnet flux parameters in the traditional motor parameter identification, is beneficial to reduce the sensitivity of the observer to the motor parameters, and improve the identification accuracy of the speed estimation value and the rotor position angle estimation value, thereby further ensuring the stable and reliable control of the motor control method.

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

[0054] 3. The present invention designs a state observer for online observation of permanent magnet flux and a position observer for adaptive observation speed, further stably analyzes and proposes important related factors affecting the stability of associated permanent magnet flux observation and the influence of observation feedback speed, and adopts setting threshold conditions to better ensure the reliability and stability of the full-domain control of the speed or torque control of the motor control process. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a structural schematic diagram of the present invention.

[0056] Figure 2 Schematic diagram of the algorithm of the present invention.

[0057] Figure 3 It is a schematic diagram of the q-axis current and the observed speed of the present invention. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments.

[0059] In this embodiment, if Figure 1-Figure 3 As shown in the figure, a multi-parameter online adaptive identification permanent magnet flux observation method is applicable to the permanent magnet motor speed sensorless operation control algorithm and the open-loop VF motor control algorithm, and is used for accurate and reliable adaptive speed observation and motor control, including a flux observer, permanent magnet flux and speed adaptive observation errors and accurate and stable observation switching conditions. The state control equation of modern control principles is used to analyze the global stability of the control and design related adaptive control parameters, so as to obtain a stable and reliable motor permanent magnet flux and the position and speed of the motor that can be coupled to observe. By obtaining the current and voltage values ​​on the d and q axes of the rotating coordinate system of the motor and inputting them into the above-mentioned flux observer, the feedback estimated speed and flux value are iterated into the flux observer. The equation framework for observing the flux is as follows:

[0060]

[0061] in is the observed flux, u is the voltage, is the observed speed, K is the flux adaptive gain coefficient, e is the flux error vector, L is the inductance matrix, i is the current, is the permanent magnetic flux.

[0062] Among them Figure 1 and Figure 2 As shown, J, flux adaptive gain coefficient K, and inductance matrix L in the equation framework for observing flux are all provided with corresponding formulas, as follows:

[0063]

[0064] Among them Figure 1 and Figure 2 As shown, the decomposed magnetic flux observation is calculated using d and q axis decomposition:

[0065] d-axis:

[0066]

[0067] q-axis:

[0068]

[0069] The observed magnetic flux of the d and q axes is calculated respectively according to the d and q axis rotating coordinate systems, and the observation calculation of the permanent magnet magnetic flux and the adaptive error vector are performed to further ensure the switching conditions for the stability of accurate and reliable observation.

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

[0071] δ1=λ T Je

[0072] δ2=λ T e

[0073]

[0074] in, δ1 is the speed observation adaptive error, δ2 is the flux observation adaptive error, k f is the flux linkage adaptive observation gain coefficient.

[0075] Among them Figure 1 and Figure 2 As shown, the adaptive observation error is brought into the adaptive control, the appropriate adaptive observer control coefficient is selected, the gain coefficient of the flux adaptive calculation flux is calculated, and the state equation is constructed according to the modern control principle as follows:

[0076]

[0077] The modern control state equation form is adopted to carry out the flux observation calculation, the equation framework of the observed flux is transformed into the state equation, and the feedback gain and the online adaptive coefficient are selected according to the state equation.

[0078] Among them Figure 1 and Figure 2 As shown, A, B1, B2, x, C1, and C2 in the state equation are all provided with corresponding formulas as follows:

[0079]

[0080] D1=k p λ q0

[0081] C2=

[0100]

[0082] in,

[0083] Among them Figure 1 and Figure 2 As shown, according to 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] Wherein a, b, c are all designed constants, and a, b, c are all greater than 0. By equivalently replacing the calculation formula with the gain calculation coefficients a, b, c, the selection rule can ensure the global stability of the observer adaptation.

[0089] Among them Figure 1 and Figure 2 As shown in the figure, after stability analysis, the range of gain coefficient K can be constrained according to eigenvalue analysis, so as to determine the selection of gain coefficient K of adaptive observer. Modern control principle analysis and state equation calculation basis are used to calculate flux adaptive parameters and select coefficients of speed loop adaptive parameters:

[0090]

[0091] k p =ω0

[0092] ω0≈20*ω speed

[0093]

[0094] a=0.1ω rate

[0095]

[0096]

[0097] Among them, ω rate is the rated angular frequency of the motor.

[0098] Among them Figure 3 As shown, the maximum threshold of the q-axis current is set to i qMin , when the speed is low, the minimum threshold of the observed speed is set to Threshold conditions are adopted to better ensure the reliability and stability of the full-range control of the torque control or the speed of the motor control process.

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

[0100] During use, since the flux observation is greatly affected by load fluctuations and speed, it is difficult to accurately estimate the flux, especially at low speeds. The above scheme proposes sampling and low-pass filtering the adaptively observed flux to obtain a stable flux observer, and adopts the excitation current and torque current of the d and q axes according to the load conditions to coordinate the switching conditions to ensure that the flux can be accurately and stably estimated. The state control equation of modern control principles is used to design relevant adaptive control parameters, thereby obtaining stable and reliable permanent magnet flux of important motors and the position and speed of the motor that can be coupled and observed.

[0101] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A multi-parameter online adaptive identification permanent magnet flux observation method, suitable for permanent magnet motor control algorithm running speed sensorless and open-loop VF motor control algorithm, characterized in that: It includes a flux observer, permanent magnet flux and speed adaptive observation errors and accurate and stable observation switching conditions. By obtaining the current and voltage values ​​on the d and q axes of the rotating coordinate system of the motor, they are input into the above-mentioned flux observer, and the feedback estimated speed and flux value are iterated into the flux observer. The equation framework for observing the flux is as follows: in is the observed flux, u is the voltage, is the observed speed, K is the flux adaptive gain coefficient, e is the flux error vector, L is the inductance matrix, i is the current, is the permanent magnetic flux.

2. The multi-parameter online adaptive identification permanent magnet flux observation method according to claim 1 is characterized in that: J, flux adaptive gain coefficient K, and inductance matrix L in the equation framework for observing flux are all provided with corresponding formulas, as follows:

3. The multi-parameter online adaptive identification permanent magnet flux observation method according to claim 2 is characterized in that: Decomposed magnetic flux observation is calculated by d and q axis decomposition: d-axis: q-axis: The observed magnetic flux of the d and q axes are calculated respectively according to the d and q axis rotating coordinate systems, and the observation calculation of the permanent magnet magnetic flux and the calculation of the adaptive error vector are performed.

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

5. The multi-parameter online adaptive identification permanent magnet flux observation method according to claim 4 is characterized in that: The adaptive observation error is brought into the adaptive control, the appropriate adaptive observer control coefficient is selected, the gain coefficient of the flux adaptive calculation flux is calculated, and the state equation is constructed according to the modern control principle as follows: The modern control state equation form is adopted to carry out the flux observation calculation, the equation framework of the observed flux is transformed into the state equation, and the feedback gain and the online adaptive coefficient are selected according to the state equation.

6. The multi-parameter online adaptive identification permanent magnet flux observation method according to claim 5 is characterized in that: In the state equation, A, B1, B2, x, C1, and C2 are all provided with corresponding formulas as follows: D1=k p l q0 C2＝[0100] in, 7. The multi-parameter online adaptive identification permanent magnet flux observation method according to claim 2 is characterized in that: According to 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 Wherein a, b, and c are all designed constants, and a, b, and c are all greater than 0.

8. The multi-parameter online adaptive identification permanent magnet flux observation method according to claim 7 is characterized in that: After stability analysis, the range of gain coefficient K can be constrained according to eigenvalue analysis, so as to determine the selection of gain coefficient K of adaptive observer. Modern control principle analysis and state equation calculation basis are used to calculate flux adaptive parameters and select coefficients of speed loop adaptive parameters: k p =ω0 ω0≈20*ω speed a=0.1ω rate Among them, ω rate is the rated angular frequency of the motor.

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

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