A motor parameter identification and control method based on improved recursive least squares method

Abstract

The present invention discloses a motor parameter identification and control method based on an improved recursive least squares method. First, the three-phase stator current and the rotor electrical angle of the permanent magnet synchronous motor are collected, and the current component i of the three-phase current in the dq axis coordinate system is calculated according to the coordinate change. d ,i q , then calculate the dq reference voltage value u output by the speed loop and current loop d1 ref ,u q1 ref , and adjust the reference voltage vector amplitude according to the comparator relationship, and then use the midpoint method to calculate the predicted value i of the dq axis current at time (k+1) dh (k+1), i qh (k+1), and finally, a least squares parameter identification model of the permanent magnet synchronous motor taking into account the current change rate is constructed. This scheme avoids the problems of large identification error and long identification time caused by sudden changes in phase current under variable speed and variable load conditions by pre-adjusting the reference voltage. At the same time, the current change rate is calculated using the predicted current value to reduce the influence of sampling error on the calculation results, greatly improving the accuracy of permanent magnet synchronous motor parameter identification under dynamic conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of motor control, and in particular to a motor parameter identification and control method based on an improved recursive least squares method. Background Art

[0002] Permanent magnet synchronous motors (PMSMs) have the advantages of high power density, high efficiency, and compact size, making them widely used in new energy vehicles, robotics, rail transit, and other fields. With the rapid development of power electronics technology and the expansion of PMSM applications, the requirements for the control performance of PMSMs are also increasing. The control performance of PMSMs is closely related to their parameters. However, in actual PMSM applications, motor parameters vary due to external factors such as sudden loads, temperature, and motor aging. To achieve better PMSM control performance, real-time identification of parameter changes during motor operation is crucial. Traditional identification methods, such as model reference adaptive methods, least squares methods, and artificial intelligence algorithms, only consider steady-state current and ignore the impact of current changes on parameter identification results. Furthermore, the discretization methods they use have large errors, resulting in low parameter identification accuracy and slow response under dynamic conditions. Summary of the Invention

[0003] Technical problem: In response to the above-mentioned existing technologies, a motor parameter identification and control method based on an improved recursive least squares method is proposed to improve the accuracy and response speed of parameter identification under dynamic conditions while ensuring the accuracy of parameter identification under steady-state conditions.

[0004] To achieve the above technical objectives, the present invention provides a motor parameter identification and control method based on an improved recursive least squares method, which is characterized by comprising the following steps: Step 1: obtaining the three-phase current i by a current sensor a 、i b 、i c The motor electrical angle θ is obtained by the encoder e and electrical angular velocity ω e , and use Clark / Park transformation to calculate the stator current dq axis component i d 、i q ; Step 2: Collect the dq axis reference voltage u output by the speed loop and current loop d1 ref ,u q1 ref ; Step 3: Calculate the dq axis reference voltage vector amplitude and adjust the dq axis reference voltage according to the amplitude comparator result Step 4: Use the midpoint method to calculate the predicted value i of the dq axis current at time (k+1)dh (k+1), i qh (k+1); Step 5: Construct a least squares parameter identification model of the permanent magnet synchronous motor taking into account the current change rate; Step 6: Use the recursive least squares method to perform parameter identification.

[0005] As a preferred embodiment of this solution, in step 3, the dq axis reference voltage amplitude is calculated by formula (1). If the dq axis reference voltage vector amplitude and the inverter output maximum voltage vector amplitude satisfy the comparator relationship shown in formula (2), the dq axis reference voltage is calculated according to formula (3). If the amplitude does not satisfy the comparator relationship shown in formula (2), the dq axis reference voltage is calculated according to formula (4):

[0006]

[0007]

[0008]

[0009]

[0010] Where u dq1 ref is the dq axis reference voltage vector, U dc is the DC bus voltage.

[0011] As a further preferred embodiment of this solution, in step 4, a discrete equation is constructed by the midpoint method shown in formula (5), and then the state equation of the permanent magnet synchronous motor dq axis current shown in formula (6) is discretized by formula (5) to obtain the predicted value i of the dq axis current at time (k+1) as shown in formula (7): dh (k+1), i qh (k+1).

[0012]

[0013] Where, T s is the sampling time.

[0014]

[0015] Where u d 、u q are the stator voltage d-axis and q-axis voltage components respectively; L s is the stator inductance; ω e is the electrical angular velocity; R is the stator resistance; ψ f Represents the flux linkage of the permanent magnet.

[0016]

[0017] Where i d (k), i q (k) are the d-axis and q-axis current values ​​at the current sampling moment; i dh (k+1), i qh (k+1) are the predicted values ​​of the d-axis and q-axis currents at the next sampling moment; u d (k),u q (k) are the d-axis and q-axis voltages at the current moment respectively.

[0018] As a further preferred embodiment of this solution, in step 5, the stator voltage equation is calculated by equation (8), equation (8) is rewritten as equation (9) according to the least squares method, and equation (9) is discretized to obtain a least squares parameter identification model of the permanent magnet synchronous motor taking into account the current change rate as shown in equation (10).

[0019]

[0020]

[0021] Where p is the differential symbol.

[0022]

[0023] As a further preferred embodiment of this solution, in step 6, a corresponding least squares algorithm model is constructed by formula (11).

[0024]

[0025] Where [a(k)b(k)c(k)] T is the current moment R s , L s and ψ f Input matrix of identification results; [a(k-1)b(k-1)c(k-1)] T R is the last moment s , L s and ψ f The input matrix of the identification result; K(k) is the gain, is the system input matrix, and y(k) is the output matrix.

[0026] in,

[0027]

[0028]

[0029]

[0030]

[0031]

[0032]

[0033] Where μ is the forgetting factor, I is the third-order identity matrix, 0<μ<1, P(0)=αI, 10 4 <α<10 10 .

[0034] Due to the adoption of the above technical solutions, the present invention has the following beneficial technical effects compared with the prior art: the present invention proposes a motor parameter identification and control method based on the improved recursive least squares method. This method avoids the problems of excessive identification error and long identification time caused by sudden changes in phase current under conditions such as variable speed and variable load by pre-adjusting the reference voltage. At the same time, it is discretized by the midpoint method. Compared with the traditional discrete method, it has higher accuracy and smaller error. In addition, considering the influence of the current change rate on the reference identification, the current change rate is calculated using the predicted current value to reduce the influence of the sampling error on the calculation result. In addition, in order to solve the problem that the sudden increase in the reference voltage vector amplitude causes current distortion and thus causes sudden changes in the identified parameters, the reference voltage vector amplitude is adjusted in real time, which greatly improves the accuracy of permanent magnet synchronous motor parameter identification under dynamic conditions, thereby improving the dynamic and steady-state performance of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a control block diagram of a motor parameter identification and control method based on an improved recursive least squares method according to the present invention;

[0036] Figure 2 This is a diagram showing the identification results of the stator resistance of a motor parameter identification and control method based on an improved recursive least squares method according to the present invention;

[0037] Figure 3 This is a diagram showing the identification results of the stator inductance of a motor parameter identification and control method based on an improved recursive least squares method according to the present invention;

[0038] Figure 4 This is a diagram showing the identification results of the permanent magnet flux linkage of a motor parameter identification and control method based on the improved recursive least squares method of the present invention. DETAILED DESCRIPTION

[0039] The present invention will be further described in detail below with reference to the accompanying drawings and through examples. The following examples are intended to explain the present invention but the present invention is not limited to the following examples.

[0040] A motor parameter identification and control method based on an improved recursive least squares method comprises the following steps:

[0041] Step 1: Obtain the three-phase current i from the current sensor a 、i b 、i c The motor electrical angle θ is obtained by the encoder e and electrical angular velocity ω e , and use Clark / Park transformation to calculate the stator current dq axis component i d 、i q ;

[0042] Step 2: Collect the dq axis reference voltage u output by the speed loop and current loop d1 ref ,u q1 ref ;

[0043] Step 3: Calculate the dq axis reference voltage amplitude by formula (1). If the dq axis reference voltage vector amplitude and the inverter output maximum voltage vector amplitude satisfy the comparator relationship shown in formula (2), calculate the dq axis reference voltage according to formula (3). If the amplitude does not satisfy the comparator relationship shown in formula (2), the dq axis reference voltage is calculated according to formula (4):

[0044]

[0045]

[0046]

[0047]

[0048] Where u dq1 ref is the dq axis reference voltage vector, U dc is the DC bus voltage.

[0049] Step 4: Construct a discrete equation using the midpoint method shown in Equation (5). Then, discretize the state equation of the dq axis current of the permanent magnet synchronous motor shown in Equation (6) using Equation (5) to obtain the predicted value i of the dq axis current at time (k+1) as shown in Equation (7): dh (k+1), i qh (k+1).

[0050]

[0051] Where, T s is the sampling time.

[0052]

[0053] Where u d 、uq are the stator voltage d-axis and q-axis voltage components respectively; L s is the stator inductance; ω e is the electrical angular velocity; R is the stator resistance; ψ f Represents the flux linkage of the permanent magnet.

[0054]

[0055] Where i d (k), i q (k) are the d-axis and q-axis current values ​​at the current sampling moment; i dh (k+1), i qh (k+1) are the predicted values ​​of the d-axis and q-axis currents at the next sampling moment; u d (k),u q (k) are the d-axis and q-axis voltages at the current moment respectively.

[0056] Step 5: Calculate the stator voltage equation from equation (8), rewrite equation (8) into equation (9) according to the least squares method, and discretize equation (9) to obtain the least squares parameter identification model of the permanent magnet synchronous motor taking into account the current change rate as shown in equation (10).

[0057]

[0058]

[0059] Where p is the differential symbol.

[0060]

[0061] Step 6: Construct the corresponding least squares algorithm model based on formula (11).

[0062]

[0063] Where [a(k)b(k)c(k)] T is the current moment R s , L s and ψ f Input matrix of identification results; [a(k-1)b(k-1)c(k-1)] T R is the last moment s , L s and ψ f The input matrix of the identification result; K(k) is the gain, is the system input matrix, and y(k) is the output matrix.

[0064] in,

[0065]

[0066]

[0067]

[0068]

[0069]

[0070]

[0071] Where μ is the forgetting factor, I is the third-order identity matrix, 0<μ<1, P(0)=αI, 10 4 <α<10 10 .

[0072] Under the conditions of DC bus voltage 300V and load torque 2N·m, the speed suddenly increases from 590r / min to 600r / min in 0.5s. The motor controller based on improved recursive least squares parameter identification is implemented. The simulation results are as follows: Figure 2-4 shown. Figure 2 The simulation results of parameter identification of stator resistance are shown in Figure 1. The given stator resistance is 0.23Ω, and the identification result is 0.233Ω. The resistance identification model can accurately identify the resistance parameters. Figure 3 The simulation results of the stator inductance parameter identification are shown. When the stator inductance is 8.35×10 -4 H, the recognition result is 8.45×10 -4 H. Figure 4 The simulation results of permanent magnet flux parameter identification are shown below. Given the permanent magnet flux is 0.033Wb, the identification result is 0.032Wb. Figure 2-4 The identification results of the stator resistance, stator inductance and permanent magnet flux show that when the speed changes suddenly, the proposed identification model can still accurately identify the parameters of the permanent magnet synchronous motor and has certain identification ability and accuracy.

[0073] Due to the adoption of the above technical solutions, the present invention has the following beneficial technical effects compared with the prior art: the present invention proposes a motor parameter identification and control method based on the improved recursive least squares method. This method avoids the problems of excessive identification error and long identification time caused by sudden changes in phase current under conditions such as variable speed and variable load by pre-adjusting the reference voltage. At the same time, it is discretized by the midpoint method. Compared with the traditional discrete method, it has higher accuracy and smaller error. In addition, considering the influence of the current change rate on the reference identification, the current change rate is calculated using the predicted current value to reduce the influence of the sampling error on the calculation result. In addition, in order to solve the problem that the sudden increase in the reference voltage vector amplitude causes current distortion and thus causes sudden changes in the identified parameters, the reference voltage vector amplitude is adjusted in real time, which greatly improves the accuracy of permanent magnet synchronous motor parameter identification under dynamic conditions, thereby improving the dynamic and steady-state performance of the system.

[0074] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A motor parameter identification and control method based on an improved recursive least squares method, characterized in that: The steps include: Step 1: Obtain the three-phase current i from the current sensor a 、i b 、i c The motor electrical angle θ is obtained by the encoder e and electrical angular velocity ω e , and use Clark / Park transformation to calculate the stator current dq axis component i d 、i q ; Step 2: Collect the dq axis reference voltage u output by the speed loop and current loop d1 ref ,u q1 ref ; Step 3: Calculate the dq axis reference voltage vector amplitude and adjust the dq axis reference voltage u according to the amplitude comparator result. d ref ,u q ref ; Step 4: Use the midpoint method to calculate the predicted value i of the dq axis current at time (k+1) dh (k+1), i qh (k+1); Step 5: Construct a least squares parameter identification model of the permanent magnet synchronous motor taking into account the current change rate; Step 6: Use the recursive least squares method to identify parameters.

2. The motor parameter identification and control method based on the improved recursive least squares method according to claim 1, characterized in that: In step 3, the dq axis reference voltage amplitude is calculated by formula (1). If the dq axis reference voltage vector amplitude and the inverter output maximum voltage vector amplitude satisfy the comparator relationship shown in formula (2), the dq axis reference voltage u is calculated according to formula (3): d ref ,u q ref If the amplitude does not satisfy the comparator relationship shown in formula (2), the dq axis reference voltage u is calculated according to formula (4): d ref ,u q ref , Where u dq1 ref is the dq axis reference voltage vector, U dc is the DC bus voltage.

3. The motor parameter identification and control method based on the improved recursive least squares method according to claim 1 or 2, characterized in that: In step 4, the midpoint method shown in formula (5) is used to construct a discrete equation, and then the state equation of the permanent magnet synchronous motor dq axis current shown in formula (6) is discretized by formula (5) to obtain the predicted value i of the dq axis current at time (k+1) as shown in formula (7): dh (k+1), i qh (k+1), Where, T s is the sampling time; Where u d 、u q are the stator voltage d-axis and q-axis voltage components respectively; L s is the stator inductance; ω e is the electrical angular velocity; R is the stator resistance; ψ f represents the permanent magnet flux, Where i d (k), i q (k) are the d-axis and q-axis current values ​​at the current sampling moment; i dh (k+1), i qh (k+1) are the predicted values ​​of the d-axis and q-axis currents at the next sampling moment; u d (k),u q (k) are the d-axis and q-axis voltages at the current moment respectively.

4. The motor parameter identification and control method based on the improved recursive least squares method according to claim 3, characterized in that: In step 5, the stator voltage equation is calculated by equation (8), and equation (8) is rewritten as equation (9) according to the least squares method, and equation (9) is discretized to obtain the least squares parameter identification model of the permanent magnet synchronous motor taking into account the current change rate as shown in equation (10). Where p is the differential symbol; 。 5. The motor parameter identification and control method based on the improved recursive least squares method according to claim 4, characterized in that: In step 6, the corresponding least squares algorithm model is constructed by formula (11), Where [a(k)b(k)c(k)] T is the current moment R s , L s and ψ f Input matrix of identification results; [a(k-1)b(k-1)c(k-1)] T R is the last moment s , L s and ψ f The input matrix of the identification result; K(k) is the gain, is the system input matrix, y(k) is the output matrix, in, Where μ is the forgetting factor, I is the third-order identity matrix, 0<μ<1, P(0)=αI, 10 4 <α<10 10 , is the transposed matrix of the system input matrix.

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