Pmsm model reference adaptive system control method based on power balance model

Abstract

The application discloses a PMSM model reference adaptive system control method based on a power balance model. Active power and reactive power are obtained by current in a d-q rotating coordinate system, a power balance model is constructed by constant amplitude transformation according to the obtained active power and reactive power, an adaptive law is designed and established according to the power balance model, the speed of a permanent magnet synchronous motor is obtained by using the adaptive law, and the position angle is further obtained by the speed, and the position and speed information fed back are input into double closed loops to perform speed and current closed loop control. The MRAS based on the power balance model reduces the calculation amount, reduces the dependence on motor parameters, has good dynamic performance, and improves the robustness of the system.

Description

Technical Field

[0001] This invention relates to a control method for a model reference adaptive system of a permanent magnet synchronous motor (PMSM), and more particularly to a control method for a model reference adaptive system of a PMSM based on a power balance model. Background Technology

[0002] High-speed permanent magnet synchronous motors (PMSMs) are widely used in flywheel energy storage, high-speed grinding machines, aerospace, distributed energy, and other fields due to their advantages such as small size, high efficiency, high power factor, high reliability, and easy maintenance. Common control methods for PMSMs include field-oriented control (FOC), maximum torque-to-current ratio control (MTPA), and direct torque control (DTC). These methods require accurate rotor position angle information. Using position sensors increases production costs and motor size, makes them difficult to adapt to harsh working environments, and cannot meet the requirements of high-performance motor control. Therefore, much research has focused on sensorless control.

[0003] MRAS (Motor Range Automated Guided Vehicle) systems are simple in structure and have good stability, leading to extensive research in sensorless control of permanent magnet synchronous motors. Much research focuses on improving the speed estimation accuracy of MRAS motors, with common improvement methods including changing the selection method of the adaptive law and the reference model. However, the coefficients of the adjustable MRAS model based on the traditional model are the same as those of the reference model, requiring online identification of motor parameters to improve estimation accuracy, which significantly increases the computational load. Summary of the Invention

[0004] To address the problems existing in the prior art, this invention proposes a sensorless control method for a high-speed permanent magnet synchronous motor model reference adaptive system (MRAS) based on a power balance model, and establishes a new method for estimating and controlling the rotor speed and position of the permanent magnet synchronous motor.

[0005] This invention reduces computational load and dependence on motor parameters by using MRAS based on a power balance model, resulting in better dynamic performance and improved system robustness.

[0006] The specific technical solution of the present invention is as follows:

[0007] (1) Obtain the active power P and reactive power Q by means of the current in the dq rotating coordinate system;

[0008] (2) Based on the obtained active power P and reactive power Q, a power balance model of the PMSM model reference adaptive system MRAS is constructed through constant amplitude transformation.

[0009] (3) Based on the power balance model, an adaptive law is designed and established, and the speed and position angle of the permanent magnet synchronous motor are obtained by using the adaptive law.

[0010] (4) Speed ​​and current closed-loop control is performed by inputting the feedback position and speed information into the dual closed loop.

[0011] The power balance model includes a reference model and an adjustable model, as detailed below:

[0012]

[0013]

[0014]

[0015] Among them, R s It is the stator resistance. It is an estimate of speed, i d i q The currents along the d-axis and q-axis are respectively, u s i s L is the stator composite voltage and current vector. q For q-axis inductance, θ is the internal power factor angle, A1 represents the reference model confidence value; A2 represents the adjustable model confidence value;

[0016] The expression in A1 does not contain velocity information, while the expression in A2 does contain velocity information.

[0017] In step (3), an adaptive system is established based on the reference model and the adjustable model using the MRAS parameter identification method. The adaptive system is divided into a linear part and a nonlinear part. The nonlinear part of the adaptive system is extracted from it, and then the following adaptive law is established using the superstability theory method combined with a PI controller:

[0018]

[0019]

[0020]

[0021]

[0022] in, The velocity is estimated at time t, where k1 and k2 are the coefficients of the first and second control laws, respectively, and are taken as positive numbers; t represents time, ξ1 and ξ2 are the first and second intermediate variables, and P and Q represent active power and reactive power, respectively. The initial velocity of the rotor is v, which is the input value obtained by multiplying the confidence values ​​of the reference model and the adjustable model by the preset linear compensation matrix D(s) to ensure the strict positive reality of the linear part of the transfer function. This represents the rotor position angle. The linear compensation matrix D(s) can be used as long as the transfer function of the linear part is strictly positive and real.

[0023] In step (4), the calculated difference between the adjustable model confidence value and the reference model confidence value is input into the PI controller. The PI controller uses the above adaptive law to output the estimated speed until the estimated speed equals the actual speed, so that the reference model confidence value equals the adjustable model confidence value, thereby realizing the estimation of the speed.

[0024] The method of this invention obtains active power (P) and reactive power (Q) of the current in the dq rotating coordinate system, and constructs a power balance model of MRAS as a reference model and an adjustable model. Compared with MRAS based on traditional models, MRAS based on the power balance model can effectively obtain more accurate velocity estimation and smaller position angle error.

[0025] The beneficial effects of this invention are:

[0026] This invention, through MRAS based on a power balance model, can reduce the amount of computation, reduce dependence on motor parameters, make the estimation of speed and position more accurate, improve the dynamic performance during acceleration and stable operation, and enhance the robustness of the system.

[0027] Furthermore, when the coefficients contained in the reference model and the adjustable model are no longer the same, the improved MRAS observer has better control performance and stronger robustness. Attached Figure Description

[0028] Figure 1 It is the relationship between the coordinate system and the composite vector;

[0029] Figure 2 It is the basic structure of MRAS based on the power balance model;

[0030] Figure 3 This is a control block diagram of a specific implementation method. Detailed Implementation

[0031] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0032] The embodiments of the present invention are as follows:

[0033] (1) Figure 1 In the coordinate system, abc is a three-phase stationary coordinate system, αβ is a two-phase stationary coordinate system, and dq is a two-phase rotating coordinate system, where the d-axis is perpendicular to the coordinate system. Overlap, u s i s This represents the stator composite voltage and current vector. θ is the internal power factor angle, and δ is the I... s The angle between u and the d-axis sProjecting the current onto the d-axis yields the corresponding current components. Substituting these components into the dq-axis voltage equation of the PMSM and performing an equal-amplitude transformation, the MRAS reference model and adjustable model for power balance can be obtained. Active power P and reactive power Q are obtained by mapping the current in the dq rotating coordinate system.

[0034] (2) Figure 2 As shown, the Model Reference Adaptive System (MRAS) for a permanent magnet synchronous motor mainly constructs two models with output quantities having the same physical meaning. Based on the obtained active power P and reactive power Q, a power balance model of the MRAS is constructed through constant amplitude transformation. The motor equations without position parameters are used as the reference model, while the equations containing the parameters to be estimated are used as the adjustable model. An appropriate adaptive law is constructed using the difference between the output quantities of the two models to adjust the parameters of the adjustable model in real time. This adaptive law adopts traditional PI control, ensuring that the output of the adjustable model tracks the output of the reference model.

[0035] The power balance model includes a reference model and an adjustable model, as detailed below:

[0036]

[0037]

[0038]

[0039] Where A1 is the confidence value of the reference model, and A2 represents the confidence value of the adjustable model; R s It is the stator resistance. It is an estimate of speed, i d i q The currents along the d-axis and q-axis are respectively, i s For stator current, L q Let θ be the q-axis inductance and θ be the internal power factor angle.

[0040] The expression in A1 does not contain velocity information, while the expression in A2 does contain velocity information.

[0041] (3) Based on the power balance model, an adaptive law is designed and established. The speed of the permanent magnet synchronous motor is obtained by using the adaptive law and the position angle is obtained by further integration based on the speed.

[0042] In step (3), an adaptive system is established based on the reference model and the adjustable model using the MRAS parameter identification method. The adaptive system is divided into a linear part and a nonlinear part. The nonlinear part of the adaptive system is extracted, and then the following adaptive law is established using the superstability theory method, specifically:

[0043]

[0044] in Let t be the estimated velocity at time t, where k1 and k2 are any positive numbers. Let v be the initial velocity, v be the input of the PI controller, and s be the difference between the confidence values ​​of the reference model and the confidence values ​​of the adjustable model, multiplied by the linear compensation matrix D(s) to ensure the strict positive reality of the linear part of the transfer function.

[0045] Using the above adaptive law for PI control, the PI output value is the rotational speed. The rotational speed is fed back to the adjustable model until the confidence value of the adjustable model is equal to the confidence value of the reference model. At this point, the estimated rotational speed is equal to the actual rotational speed.

[0046] Further rotor position angle The rotor position angle is obtained by integrating the rotational speed, thus enabling a more accurate estimation. The formula is as follows:

[0047]

[0048] (4) Speed ​​and current closed-loop control is performed by inputting the feedback position and speed information into the dual closed loop.

[0049] This invention constructs a novel adjustable model and reference model based on a power balance model MRAS, reducing dependence on motor parameters, exhibiting better dynamic performance, and improving the robustness of the system.

[0050] Specific application examples are explained.

[0051] To verify the reliability of the method of this invention, relevant simulation analysis was conducted. The parameters of the surface-mounted permanent magnet synchronous motor used as an example in the analysis are shown in Table 1 below.

[0052] Table 1 Motor Parameters

[0053]

[0054]

[0055] In the simulation of permanent magnet synchronous motors (PMSMs), an MRAS algorithm based on a power balance model was established to calculate the rotor speed and position of the PMSM, and this was used for closed-loop FOC control. Based on the motor parameters in this table, the effectiveness of the invention is demonstrated by building and simulating the control system in MATLAB / Simulink according to the system shown in the abstract figures.

Claims

1. A PMSM model reference adaptive system control method based on a power balance model, characterized in that, The method steps are as follows: (1) obtaining active power P and reactive power Q in d-q rotating coordinate system through current; (2) constructing a power balance model through constant amplitude transformation according to the obtained active power P and reactive power Q; (3) designing and establishing an adaptive law according to the power balance model, and obtaining the speed and position angle of the permanent magnet synchronous motor by using the adaptive law; (4) inputting the feedback position and speed information into the double closed loop for speed and current closed loop control; The power balance model includes a reference model and an adjustable model, and specifically as follows: where R s is the stator resistance, is the estimated speed, i d , i q are the d-axis and q-axis currents, u s , i s are the stator complex voltage and current vectors, L q is the q-axis inductance, θ is the internal power factor angle, A1 represents the reference model confidence value; A2 represents the adjustable model confidence value.

2. The PMSM model reference adaptive system control method based on a power balance model according to claim 1, characterized in that: In the step (3), an adaptive system is established according to the reference model and the adjustable model through the MRAS parameter identification method, the nonlinear part of the adaptive system is extracted therefrom, and the following adaptive law is established by using the super stability theory method combined with a PI controller, specifically as follows: wherein, is the estimated speed at time t, k1, k2 are first and second control law coefficients, t represents time, ξ1, ξ2 are first and second intermediate variables, P, Q represent active power and reactive power, respectively, is the initial speed of the rotor, v is an input, obtained by multiplying the values of the reference model confidence value and the adjustable model confidence value by a predetermined linear compensation matrix D(s); denotes the rotor position angle.

3. The PMSM model reference adaptive system control method based on a power balance model according to claim 1, characterized in that: In the step (4), the calculation difference value of the adjustable model confidence value and the reference model confidence value is input into the PI controller, the PI controller outputs the estimated speed by using the above adaptive law until the estimated speed is equal to the actual speed, so that the reference model confidence value is equal to the adjustable model confidence value, and the estimation of the speed is realized.

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