Permanent magnet synchronous motor position-less control algorithm based on angle error compensation

Abstract

The invention discloses a permanent magnet synchronous motor position-less control method of a hybrid flux linkage model based on angle compensation. Firstly, a traditional mixed flux linkage model is analyzed, an angle error is observed by designing an extended state observer under an estimation shaft system, recursive least square stator resistance online identification is designed by considering that stator resistance possibly changes when the motor runs, and the whole permanent magnet synchronous motor sensorless control based on angle error compensation is completed. According to the invention, the structure complexity of the control system is effectively reduced, the hardware cost is reduced, and the system robustness is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of motor drive control, and in particular to a position sensorless control method for a permanent magnet synchronous motor with angle error compensation. Background Art

[0002] Permanent magnet synchronous motors (PMSMs) are used in many applications, including electric vehicles, aircraft, nuclear power plants, submarines, robotics, medical, and industrial servo drives. Vector control of PMSM drives requires rotor position / speed and phase currents. Rotor position / speed sensors used to control PMSM drives increase size and cost, reduce reliability, and require shaft extension and mounting. To overcome these shortcomings, sensorless control is a useful solution, achieving higher reliability without the need for expensive and bulky mechanical sensors. Several methods for sensorless speed control of PMSMs were investigated and applied to sensorless speed control of PMSMs.

[0003] Traditional hybrid flux models have good stability, but the uncertainty of motor parameters can introduce errors into the model, affecting the accuracy and performance of flux observation and, in turn, the accuracy and performance of rotor angles. Online adaptation or offline identification of these parameters should be considered.

[0004] Researching high-performance position-free control algorithms for hybrid flux linkage models not only helps simplify the system structure and reduce hardware costs, but also eliminates problems such as sensor cable breakage and poor interface contact, thereby improving system robustness. Summary of the Invention

[0005] The present invention addresses the deficiencies in the prior art and provides a position sensorless control method for a permanent magnet synchronous motor based on angle error compensation, thereby improving the rotor position observation accuracy and the dynamic response speed of the drive system.

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

[0007] In a first aspect, the present invention provides a position sensorless control method for a permanent magnet synchronous motor based on angle error compensation, the method comprising the following steps:

[0008] S1: The sensorless permanent magnet synchronous motor using the traditional hybrid flux observer is analyzed.

[0009] S2: Estimate the observation angle error of the expanded state observer under the axis system by design.

[0010] S3: Analyze and design the online identification of stator resistance using recursive least squares after angle compensation.

[0011] S4: The angle error and the identified stator resistance are brought into the traditional model for compensation.

[0012] Furthermore, in step S1, the position sensorless model of the permanent magnet synchronous motor using the hybrid flux observer is:

[0013] First, the stator flux equation of the permanent magnet synchronous motor has a voltage model and a current model:

[0014]

[0015] ψ d =L d i d +ψ f ,ψ q =L q i q

[0016] where u s is the stator voltage of the motor in the αβ coordinate system, i s is the stator current of the motor in the αβ coordinate system, R s is the stator resistance, L d and L q is the inductance of the motor in the dq coordinate system, θ e Rotor angle, ψ f is the rotor flux.

[0017] The mixed flux linkage model is:

[0018]

[0019] p is the differential operator, and further arrangement yields:

[0020]

[0021] The hybrid model flux involves many motor parameters, so there are certain requirements for the accuracy of the motor parameters. The q-axis inductance is identified offline through high-frequency injection, and the stator resistance is observed through online identification to ensure that the flux estimation error is within the allowable range.

[0022] Furthermore, in S2, the angle error of the expanded state observer under the estimated shaft system is designed, and the voltage equation under the estimated shaft is estimated as follows:

[0023]

[0024] The voltage equation under the estimated shaft includes the rotor angle error, so the angle error can be extracted from the voltage equation under the estimated shaft system and the voltage equation can be rewritten as:

[0025]

[0026] make Further we get:

[0027] pi γδ =αU+d

[0028] Based on this model, combined with the extended state observer, i γδ The error feedback equation with d as the state variables is as follows:

[0029]

[0030] in, β1 and β2 are the observer error feedback gains.

[0031] By expanding the observer, we get Use the inverse tangent to get the angular error:

[0032]

[0033] Furthermore, in S3, the stator resistance after angle compensation is analyzed and designed for online identification. After angle compensation, it can be considered that The voltage equation for the delta axis is:

[0034]

[0035] Arranged:

[0036]

[0037] make φ=i δ , and bring it into the recursive least squares identification algorithm to get:

[0038]

[0039] You can get an estimate of the resistance

[0040] Furthermore, in S4, the stator resistance observed in S3 is substituted into the hybrid flux equation to obtain a flux observer capable of resistance adaptation and then calculate the effective flux:

[0041]

[0042] Finally, the effective magnetic flux inverse tangent is used to obtain the rotor angle, and the rotor angle is differentiated to obtain the speed:

[0043]

[0044] Then the angle error calculated by S2 is compensated to obtain:

[0045]

[0046] The beneficial effects of the present invention are:

[0047] 1. The position-free control of the hybrid flux linkage model with angle error compensation proposed in the present invention simplifies the system structure and improves the dynamic response performance.

[0048] 2. The hybrid flux linkage model for online resistance identification proposed in the present invention improves the robustness of the system and can adapt to more extreme and harsh environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a flow chart of position sensorless control based on hybrid flux linkage model with angle error compensation.

[0050] Figure 2 This is a diagram of the position sensorless control structure based on the hybrid flux model with angle error compensation.

[0051] Figure 3 This is a simulation diagram of the angle error before and after speed compensation.

[0052] Figure 4 This is a simulation diagram of the angle error before and after loading compensation.

[0053] Figure 5 This is a simulation diagram of stator resistance identification. Specific implementation plan

[0054] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0055] Example 1

[0056] Figure 1 It is a flow chart of position sensorless control based on hybrid flux linkage model with angle error compensation. Figure 2 This is a diagram of a position sensorless control structure of a hybrid flux linkage model based on angle error compensation. This embodiment proposes a sensorless control method based on angle error compensation, which includes the following steps:

[0057] S1: The sensorless permanent magnet synchronous motor using the traditional hybrid flux observer is analyzed.

[0058] S2: Estimate the observation angle error of the expanded state observer under the axis system by design.

[0059] S3: Analyze and design the online identification of stator resistance using recursive least squares after angle compensation.

[0060] S4: The angle error and the identified stator resistance are brought into the traditional model for compensation.

[0061] 1. Establishment of traditional hybrid flux linkage model

[0062] First, the stator flux equation of the permanent magnet synchronous motor has a voltage model and a current model:

[0063]

[0064] ψ d =L d i d +ψ f ,ψ q =L q i q

[0065] where u s is the stator voltage of the motor in the αβ coordinate system, i s is the stator current of the motor in the αβ coordinate system, R s is the stator resistance, L d and L q is the inductance of the motor in the dq coordinate system, θ e Rotor angle, ψ f is the rotor flux.

[0066] The mixed flux linkage model is:

[0067]

[0068] The motor parameters designed by the hybrid flux model include d-axis and q-axis inductances, stator resistance, and rotor flux. The d-axis inductance can be considered approximately unchanged, and the rotor flux will not affect the angle error estimation in the next step. Figure 3 (a) and Figure 4 (a) shows the speed change and loading simulation without angle compensation. A certain amount of angle error exists. The angle errors before and after the speed change are -1.9 rad and -0.05 rad, respectively. The angle errors before and after loading are 1.2 rad and -0.05 rad, respectively.

[0069] 2. Observation principle for estimating axis angle error

[0070] By designing and estimating the observation angle error of the expanded state observer under the shaft system, the voltage equation under the estimated shaft is:

[0071]

[0072] The voltage equation under the estimated shaft includes the rotor angle error, so the angle error can be extracted from the voltage equation under the estimated shaft system and the voltage equation can be rewritten as:

[0073]

[0074] make Further we get:

[0075] pi γδ =αU+d

[0076] Based on this model, combined with the extended state observer, i γδ The error feedback equation with d as the state variables is as follows:

[0077]

[0078] in, β1 and β2 are the observer error feedback gains.

[0079] By expanding the observer, we get Use the inverse tangent to get the angular error:

[0080]

[0081] After obtaining the angle error, the traditional hybrid flux can be compensated. Figure 3 (b) and Figure 4 (b) is the simulation diagram of speed change and loading after angle error compensation. Figure 3 (a) and Figure 4 (a) The angular error is significantly reduced. The angular errors before and after the speed change are -0.05 rad and -0.01 rad respectively; the angular errors before and after loading are 0.03 rad and -0.02 rad respectively.

[0082] 3. Principle of online identification of stator resistance

[0083] The voltage equation for the delta axis is:

[0084]

[0085] Arranged:

[0086]

[0087] make φ=i δ , and bring it into the recursive least squares identification algorithm to get:

[0088]

[0089] You can get an estimate of the resistance

[0090] Figure 5 This is the stator resistance identification diagram. The error between the estimated resistance and the actual resistance is very small, less than 0.1%.

Claims

1. A position sensorless control method for a permanent magnet synchronous motor based on angle error compensation, comprising the following steps: S1: The problems of permanent magnet synchronous motor without position sensor using traditional hybrid flux observer are analyzed. S2: Estimate the observation angle error of the expanded state observer under the axis system by design. S3: Analyze and design the online identification of stator resistance using recursive least squares after angle compensation. S4: The angle error and the identified stator resistance are brought into the traditional model for compensation.

2. The control method according to claim 1, characterized in that: In step S1, the position sensorless model of the permanent magnet synchronous motor using the hybrid flux observer is: First, the stator flux equation of the permanent magnet synchronous motor has a voltage model and a current model: where u s is the stator voltage of the motor in the αβ coordinate system, i s is the stator current of the motor in the αβ coordinate system, R s is the stator resistance, L d and L q is the inductance of the motor in the dq coordinate system, θ e Rotor angle, ψ f is the rotor flux. The mixed flux linkage model is: p is the differential operator, and further arrangement yields: ψ d =L d I d +ψ f ,ψ q =L q I q 3. The control method according to claim 1, characterized in that: In step S2, the angle error of the expanded state observer under the estimated shaft system is designed and estimated, and the voltage equation under the shaft is estimated as follows: The voltage equation under the estimated shaft includes the rotor angle error, so the angle error can be extracted from the voltage equation under the estimated shaft system and the voltage equation can be rewritten as: make Further we get: pi γδ =αU+d Based on this model, combined with the extended state observer, i γδ The error feedback equation with d as the state variables is as follows: in, β1 and β2 are the observer error feedback gains. By expanding the observer, we get Use the inverse tangent to get the angular error:

4. The control method according to claim 1, wherein: In step S3, the stator resistance after angle compensation is analyzed and designed. The voltage equation for the delta axis is: Arranged: make φ=i δ , and bring it into the recursive least squares identification algorithm to get: You can get an estimate of the resistance 5. The control method according to claim 1, characterized in that: In step S4, the stator resistance observed in S3 is substituted into the hybrid flux equation to obtain a flux observer capable of resistance adaptation and then calculate the effective flux: Get the observed magnetic flux and then calculate the effective magnetic flux: Finally, the effective magnetic flux inverse tangent is used to obtain the rotor angle, and the rotor angle is differentiated to obtain the speed: Then the angle error calculated by S2 is compensated to obtain:

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