阅读说明：本技术 永磁同步电机的强鲁棒性模型预测控制方法及系统 (Prediction control method and system for strong robustness model of permanent magnet synchronous motor ) 是由 桂卫华 高锦秋 杨超 陶宏伟 彭涛 殷士才 戴柳祥 阳春华 陈志文 樊欣宇 于 2021-07-09 设计创作，主要内容包括：本发明涉及永磁同步电机控制技术领域,公开一种永磁同步电机的强鲁棒性模型预测控制方法及系统,以实现永磁同步电机强鲁棒电流控制。方法包括：构建永磁同步电机的有限集预测模型；构建基于二阶滑模变结构的永磁同步电机扰动观测器；利用扰动观测器获得扰动观测值,构造扰动控制器,计算dq轴补偿电压；根据dq轴补偿电压,构造出逆变器各开关状态下含有补偿电压的dq轴控制电压；构建强鲁棒性模型预测控制的目标函数,选取令目标函数值最小的逆变器桥臂上的开关状态作为该逆变器当前周期的开关控制输出,来抑制永磁同步电机中存在的扰动。(The invention relates to the technical field of permanent magnet synchronous motor control, and discloses a strong robustness model prediction control method and system of a permanent magnet synchronous motor, so as to realize strong robustness current control of the permanent magnet synchronous motor. The method comprises the following steps: constructing a finite set prediction model of the permanent magnet synchronous motor; constructing a permanent magnet synchronous motor disturbance observer based on a second-order sliding mode variable structure; obtaining a disturbance observation value by using a disturbance observer, constructing a disturbance controller, and calculating dq axis compensation voltage; constructing dq axis control voltage containing compensation voltage under each switch state of the inverter according to the dq axis compensation voltage; and constructing a target function of the prediction control of the strong robustness model, and selecting the switch state on the bridge arm of the inverter with the minimum target function value as the switch control output of the current period of the inverter to inhibit the disturbance in the permanent magnet synchronous motor.)

1. A strong robustness model prediction control method of a permanent magnet synchronous motor is characterized by comprising the following steps:

step S1: constructing a finite set prediction model of the permanent magnet synchronous motor;

step S2: constructing a permanent magnet synchronous motor disturbance observer based on a second-order sliding mode variable structure;

step S3: obtaining a disturbance observation value by using an observer; constructing a disturbance controller, and calculating dq axis compensation voltage;

step S4: constructing dq axis control voltage containing compensation voltage under each switch state of the inverter according to the dq axis compensation voltage;

step S5: and constructing a target function of the prediction control of the strong robustness model, and selecting the switch state on the bridge arm of the inverter with the minimum target function value as the switch control output of the current period of the inverter so as to inhibit the disturbance in the permanent magnet synchronous motor.

2. A strong robustness model predictive control method of a PMSM as claimed in claim 1 wherein said PMSM finite set predictive model is a motor dq axis stator current predictive model.

3. A strong robustness model predictive control method of a permanent magnet synchronous motor according to claim 1 or 2, characterized in that said step S1 specifically comprises the steps of:

constructing a finite set prediction model of the permanent magnet synchronous motor, wherein the expression is as follows:

in the formula, L_{d_mea}、L_{q_mea}For measured d, q-axis inductance values, R_{s_mea}For measured stator winding electricityHindered, Ψ_fIs a permanent magnet flux linkage, p is the number of pole pairs of the motor, i_d(k)、i_q(k) D and q axis current values at time k, u_d(k)、u_q(k) D, q-axis voltage values at time k, ω_m(k) Motor speed at time k, T sample time, i_d(k+1)、i_q(k +1) is the estimated d and q axis predicted current values at the time k + 1.

4. The method according to claim 3, wherein the step S2 specifically includes the following steps:

constructing a permanent magnet synchronous motor disturbance observer based on a second-order sliding mode variable structure, wherein the expression of the disturbance observer is as follows:

in the formula (f)_d(k)、f_q(k) For d and q axis disturbance observed values obtained at the moment k,the d and q axis current values estimated for time k, j being 0,1,2, …, k, sign (x) are sign functions, λ₁And λ₂Is a sliding mode coefficient, and λ₁And λ₂Is selected to satisfy the following formula:

wherein max is the maximum value, and min is the minimum value.

5. The method according to claim 4, wherein the step S3 specifically includes the following steps:

obtaining a k-moment disturbance observation value f by using a disturbance observer_d(k) And f_q(k) (ii) a Constructing a disturbance controller on d and q axes, and calculating dq axis compensation voltage, wherein the calculation is represented as:

in the formula u_{d_com}(k)、u_{q_com}(k) Compensation voltage for d and q axes at time k, u_{d_com}(k-1)、u_{q_com}(k-1) is the compensation voltage of d and q axes at the k-1 moment, and the initial value is 0; f. of_{d_err}(k)、f_{q_err}(k) For the tracking error of d and q axes at time k, k_{p_d}And k_{i_d}Two parameters of the disturbance controller on the d-axis, k_{p_q}And k_{i_q}Two parameters for the disturbance controller on the q-axis, k_{p_d}、k_{i_d}、k_{p_q}And k_{i_q}The following conditions are satisfied:

6. the method according to claim 5, wherein the step S4 specifically includes the following steps:

s41: the switching states of the inverter at the time K are controlled to be K groups, and the switching state of the mth group is recorded as S_mThe expression of the control voltage is as follows:

in the formula u_unm、u_vnmAnd u_wnmRespectively, the m-th group of switches of the inverter_mThe three-phase voltage is shown as theta (k) as the rotor position angle at the moment k, u_dm(k)、u_qm(k) For the m group of switching states S of the inverter at the moment of k_mControlling voltage values of the lower d and q axes; m is 1,2, …, K;

s42: constructing a compensated mth group of switching states S of the inverter_mThe value of the dq-axis control voltage, i.e. u_{dm_com}(k) And u_{qm_com}(k) The expression is as follows:

7. the method according to claim 6, wherein the step S5 specifically includes the following steps:

s51: obtaining the m group switch state S of the inverter at the moment of k +1_mPredicted current value of_dm(k +1) and i_qm(k +1), expressed as:

s52: constructing a target function J of the permanent magnet synchronous motor model predictive control, wherein the expression is as follows:

in the formula i_d ^*、i_q ^*Reference currents of d and q axes of a permanent magnet synchronous motor control system are used;

s53: establishing a one-step optimized calculation function by taking the minimum objective function value which enables current tracking to be the optimal control as an optimization target, wherein the expression is as follows:

J(S_l)＝min{J(S_m)}

in the formula, J (S)_l) Indicating the state S of the first group of switches of the inverter_lMinimum value of lower objective function, S_l∈[S₁,S₂,...,S_K]；

S54: and taking the switching state of the first group of the inverter as a control instruction of a k-time permanent magnet synchronous motor control system, and controlling the switching state of each bridge arm of the inverter, thereby realizing the strong robustness model prediction control method of the permanent magnet synchronous motor.

8. A strong robustness model predictive control system of a permanent magnet synchronous machine comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method of any of the preceding claims 1 to 7 when executing the computer program.

Technical Field

The invention relates to the technical field of permanent magnet synchronous motor control, in particular to a strong robustness model prediction control method and system of a permanent magnet synchronous motor.

Background

In recent years, an embedded permanent magnet synchronous motor has the advantages of high power factor, high efficiency, high reliability and the like, and is widely applied to the industrial field such as numerical control machine tools, household appliances, subways, high-speed railway traction systems and the like. Compared with the traditional permanent magnet synchronous motor drive control algorithm, such as vector control based on Proportional Integral (PI), direct torque control and the like, the model predictive control developed in the 20 th century and the 70 th year has strong constraint processing capacity and follows the optimal control principle. In addition, the model predictive control method does not have the risk of integral saturation, and easily meets the requirements of control performance, safety and reliability, so that the model predictive control method becomes a competitive motor control scheme. At present, model predictive control can achieve multiple functions in a mechanical transmission system, such as torque control, flux linkage and power control, speed regulation current control, and the like. According to the implementation process of model predictive control, the method can be divided into two types of continuous control set model predictive control and limited control set model predictive control. In contrast, the finite set model predictive control does not use a strategy of generating control pulses by a modulator, and can greatly reduce the computational complexity. In addition, the algorithm is simple to realize, and engineering personnel do not need to master too much deep professional knowledge, so that the method is very suitable for industrial practical application.

A common problem of model predictive control in permanent magnet synchronous motor drives is parameter mismatch, which is unavoidable. In order to improve the robustness of model predictive control on parameter mismatch, two different solutions are available at present. Firstly, a prediction error value caused by parameter mismatching is regarded as disturbance, and then a disturbance observer is used for observation and compensation; secondly, a parameter online identification technology is introduced into model prediction control, and the problem of unmatched motor parameters is directly solved. In contrast, the disturbance observer can detect not only general disturbances caused by parameter mismatch, but also non-linearity and even external disturbances of the system, and the advantages are more obvious, and thus the disturbance observer is receiving more and more attention. Some solutions for directly compensating parameter mismatch based on the sliding mode disturbance observer are only suitable for application occasions with small parameter mismatch due to no disturbance controller.

At present, for a permanent magnet synchronous motor control system, a direct compensation parameter mismatch scheme based on a sliding mode disturbance observer with a disturbance controller is not common. Therefore, a strong robustness model prediction control method of the permanent magnet synchronous motor is urgently needed to improve the robustness of a permanent magnet synchronous motor control system, eliminate prediction errors caused by parameter mismatch in real time and realize strong robustness current control of the permanent magnet synchronous motor, and has important theoretical research value and engineering application potential.

Disclosure of Invention

Under the influence of a working environment, when the permanent magnet synchronous motor is driven to operate by adopting model prediction control, the motor parameters are not invariable, the error is brought to a prediction model under the condition of parameter mismatch, the selection of the optimal vector of the motor is influenced, the control cannot reach the expected performance, and the safety of the whole motor control system is seriously influenced. The invention provides a strong robustness model prediction control method and system of a permanent magnet synchronous motor, aiming at solving the existing problems, improving the robustness of a control system and eliminating prediction errors caused by parameter mismatch in real time.

In order to achieve the above object, the present invention discloses a strong robustness model prediction control method for a permanent magnet synchronous motor, comprising:

step S1: constructing a finite set prediction model of the permanent magnet synchronous motor;

step S2: constructing a permanent magnet synchronous motor disturbance observer based on a second-order sliding mode variable structure;

step S3: obtaining a disturbance observation value by using an observer; constructing a disturbance controller, and calculating dq axis compensation voltage;

step S4: constructing dq axis control voltage containing compensation voltage under each switch state of the inverter according to the dq axis compensation voltage;

step S5: and constructing a target function of the prediction control of the strong robustness model, and selecting the switch state on the bridge arm of the inverter with the minimum target function value as the switch control output of the current period of the inverter to inhibit the disturbance in the permanent magnet synchronous motor.

Preferably, in the prediction control method of the strong robustness model of the permanent magnet synchronous motor, the finite set prediction model of the permanent magnet synchronous motor is a motor dq-axis stator current prediction model.

Preferably, the step S1 specifically includes the following steps:

constructing a finite set prediction model of the permanent magnet synchronous motor, wherein the expression is as follows:

in the formula, L_{d_mea}、L_{q_mea}For measured d, q-axis inductance values, R_{s_mea}For measured stator winding resistance, Ψ_fIs a permanent magnet flux linkage, p is the number of pole pairs of the motor, i_d(k)、i_q(k) D and q axis current values at time k, u_d(k)、u_q(k) D, q-axis voltage values at time k, ω_m(k) Motor speed at time k, T sample time, i_d(k+1)、i_q(k +1) is the estimated d and q axis predicted current values at the time k + 1.

Preferably, the step S2 specifically includes the following steps:

constructing a permanent magnet synchronous motor disturbance observer based on a second-order sliding mode variable structure, wherein the expression of the disturbance observer is as follows:

in the formula (f)_d(k)、f_q(k) For d and q axis disturbance observed values obtained at the moment k,the d and q axis current values estimated for time k, j being 0,1,2, …, k, sign (x) are sign functions, λ₁And λ₂Is a sliding mode coefficient, and lambda is used for ensuring the stability of the disturbance observer₁And λ₂Is selected to satisfy the following formula:

wherein max is the maximum value, and min is the minimum value.

Preferably, the step S3 specifically includes the following steps:

obtaining a k-moment disturbance observation value f by using a disturbance observer_d(k) And f_q(k) (ii) a Constructing a disturbance controller (PI controller) on d and q axes, calculating dq axis compensation voltage, and expressing as:

in the formula u_{d_com}(k)、u_{q_com}(k) Compensation voltage for d and q axes at time k, u_{d_com}(k-1)、u_{q_com}(k-1) is the compensation voltage of d and q axes at the time of k-1The initial values are all 0; f. of_{d_err}(k)、f_{q_err}(k) For the tracking error of d and q axes at time k, k_{p_d}And k_{i_d}Two parameters, k, for a disturbance controller on the d-axis (PI controller)_{p_q}And k_{i_q}For two parameters of the disturbance controller (PI controller) on the q-axis, k for the stabilization of the system_{p_d}、k_{i_d}、k_{p_q}And k_{i_q}The following conditions need to be satisfied:

preferably, the step S4 specifically includes the following steps:

s41: the inverter controls the possible switch states at the time K to be K groups, and the switch state of the mth group is recorded as S_mThe expression of the control voltage is as follows:

in the formula u_unm、u_vnmAnd u_wnmRespectively, the m-th group of switches of the inverter_mThe three-phase voltage is shown as theta (k) as the rotor position angle at the moment k, u_dm(k)、u_qm(k) For the m group of switching states S of the inverter at the moment of k_mControlling voltage values of the lower d and q axes; m ═ 1,2,. K.

S42: constructing a compensated mth group of switching states S of the inverter_mLower dq-axis control voltage, i.e. u_{dm_com}(k) And u_{qm_com}(k) The expression is as follows:

preferably, the step S5 specifically includes the following steps:

s51: obtaining a predicted current value i of the inverter in the m group of switch states at the moment of k +1_dm(k +1) and i_qm(k +1), expressed as:

s52: constructing a target function J of the permanent magnet synchronous motor model predictive control, wherein the expression is as follows:

in the formula i_d ^*、i_q ^*Reference currents of d and q axes of a permanent magnet synchronous motor control system are provided.

S53: establishing a one-step optimized calculation function by taking the minimum objective function value which enables current tracking to be the optimal control as an optimization target, wherein the expression is as follows:

J(S_l)＝min{J(S_m)}

in the formula, J (S)_l) Indicating the state S of the first group of switches of the inverter_lMinimum value of lower objective function, S_l∈[S₁,S₂,...,S_K]；

S54: and taking the switching state of the first group of the inverter as a control instruction of a k-time permanent magnet synchronous motor control system, and controlling the switching state of each bridge arm of the inverter, thereby realizing the strong robustness model prediction control method of the permanent magnet synchronous motor.

In order to achieve the above object, the present invention further discloses a model predictive control system for a permanent magnet synchronous motor, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps corresponding to the above method when executing the computer program.

The invention has the following beneficial effects:

the method has the advantages that disturbance observation is achieved by using a second-order sliding-mode observer, disturbance rejection control is achieved by using a disturbance controller, the problem of parameter mismatch in model prediction control of the permanent magnet synchronous motor is solved, the method can enable a permanent magnet synchronous motor model prediction control system to have strong robustness, and the limitation that a traditional algorithm is only suitable for occasions with small-degree parameter mismatch is broken through.

The present invention will be described in further detail below with reference to the accompanying drawings.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:

FIG. 1 is a schematic diagram of the overall control principle of a model predictive control system for a permanent magnet traction motor according to an embodiment of the present invention;

FIG. 2 is a flowchart of a finite set model prediction method of a permanent magnet synchronous motor based on a disturbance observer according to an embodiment of the present invention;

fig. 3 is an experimental result of strong robustness model predictive control of a permanent magnet synchronous motor according to an embodiment of the present invention; the states of the motor voltage and the motor current before and after the parameter mismatch are respectively shown in (a) and (c), and the states of the motor voltage and the motor current before and after the compensation by the finite set model prediction method based on the disturbance observer are respectively shown in (b) and (d).

Detailed Description

The embodiments of the invention will be described in detail below with reference to the drawings, but the invention can be implemented in many different ways as defined and covered by the claims.

Example 1

In this embodiment, the "strong robustness model prediction control method of the permanent magnet synchronous motor" of the present invention is also referred to as: 'permanent magnet synchronous motor finite set model prediction method based on disturbance observer'. The subsequent system is the same and will not be described in detail.

Specifically, in this embodiment, referring to a certain type of permanent magnet traction motor control system, a schematic diagram of the overall control principle of a permanent magnet traction motor model prediction control system using a medium-point clamp type three-level inverter is shown in fig. 1. In this embodiment, a finite set model prediction method of a permanent magnet synchronous motor based on a disturbance observer is taken as an example for explanation, a permanent magnet traction motor control system adopts a double closed-loop control structure, a control outer loop is a rotating speed loop, and a control inner loop is a current loop. According to the rotating speed feedback, a lookup table is adopted, and then a maximum torque current ratio (MTPA) motor control strategy is adopted to obtain a motor dq axis reference current value; and finally, selecting the inverter switch state combination which minimizes the inverter switch state combination as a control instruction output of a motor control system by a target function of the permanent magnet synchronous motor finite set model predictive control based on a disturbance observer, and realizing tracking of a system dq axis reference current value and regulation of disturbance existing in the permanent magnet synchronous motor.

In this embodiment, the main parameters of a permanent magnet traction motor of a certain type are shown in table 1.

TABLE 1 Primary parameters of permanent magnet synchronous machine System

As shown in fig. 2, the method for predicting the finite set model of the permanent magnet synchronous motor based on disturbance suppression control in the embodiment specifically includes the following steps:

step S1: and constructing a finite set prediction model of the permanent magnet synchronous motor.

It should be noted that, as shown in fig. 2, the model predictive control of the permanent magnet synchronous motor in this embodiment is in an inner loop current control link in a motor double closed-loop control structure, and the system control quantity in this embodiment is voltage.

Step 11: and sampling the permanent magnet synchronous motor by using sensors such as voltage and current, and obtaining the motor state of the permanent magnet synchronous motor in real time. In the kth sampling period, obtaining the three-phase current i in the current period_a(k)，i_b(k)，i_c(k) The rotor position angle is theta (k), and the motor rotation speed is omega_m(k) In that respect Obtaining the current value i at the k moment under the dq axis by using a coordinate transformation formula_d(k)、i_q(k)：

Step 12: establishing a mathematical model of the permanent magnet synchronous motor, wherein the mathematical model is expressed as follows:

in the formula, L_{d_mea}、L_{q_mea}For measured d, q-axis inductance values, R_{s_mea}For measured stator winding resistance, Ψ_fIs a permanent magnet flux linkage, p is the number of pole pairs of the motor, i_d(k)、i_q(k) D and q axis current values at time k, u_d(k)、u_q(k) The d-axis and q-axis voltage values at the time k are shown.

In this example, L is_{d_mea}＝0.0030H，L_{q_mea}＝0.0480H，R_{s_mea}＝0.07Ω。

Step 13: according to a mathematical model of the permanent magnet synchronous motor, a finite set prediction model of the permanent magnet synchronous motor is constructed by using a first-order Euler formula, and the expression is as follows:

where T is the sampling time, i_d(k+1)、i_q(k +1) is the estimated d and q axis predicted current values at the time k + 1.

Step S2: and constructing a permanent magnet synchronous motor disturbance observer based on a second-order sliding mode variable structure.

It should be noted that, in this embodiment, the disturbance of the motor model predictive control system is caused by the parameter mismatch of the permanent magnet traction motor, and only this disturbance is considered in the following steps. However, based on the teaching of the present embodiment and in combination with the common sense, if the gist of the present embodiment can be obviously applied to the disturbance caused by other situations of the permanent magnet synchronous motor, such variations all fall into the protection scope of the present invention, and the protection scope of the claims is specifically subject to the protection scope.

Step 21: establishing a disturbed permanent magnet synchronous motor mathematical model, wherein the expression is as follows:

in the formula (f)_d(k)、f_q(k) The disturbance values caused by parameter mismatch on the d and q axes.

Step 22: constructing a disturbance observer based on a second-order sliding mode structure, wherein the expression of the disturbance observer is as follows:

in the formula (I), the compound is shown in the specification,the d and q axis current values estimated for time k, j being 0,1,2, …, k, sign (x) are sign functions, λ₁And λ₂Is the sliding mode coefficient.

Step 23: in order to ensure the stability of the disturbance observer, a Lyapunov function is constructed, and the expression of the Lyapunov function is as follows:

wherein, the sliding mode coefficient lambda₁And λ₂Is selected to satisfy the following formula:

it should be noted that in the present embodiment, λ₁＝-300，λ₂O is any one of 5s to 6s at-200.

Step S3: and obtaining a disturbance observation value by using an observer, constructing a disturbance controller, and calculating dq-axis compensation voltage.

Step 31: interference value f_dAnd f_qControlled to 0, the current estimated by the measured motor parameters will be equal to the accurate result. To achieve this, d-axis and q-axis disturbances f are detected with the proposed disturbance observer_d(k) And f_q(k) Then, a tracking error f with a disturbance reference of zero is calculated_{d_err}(k) And f_{q_err}(k) And then the PI controller is adopted to adjust the disturbance in the motor, thereby tracking the required compensation voltage:

in the formula u_{d_com}(k)、u_{q_com}(k) Compensation voltage for d and q axes at time k, u_{d_com}(k-1)、u_{q_com}(k-1) is the compensation voltage of d and q axes at the k-1 moment, and the initial value is 0; k is a radical of_{p_d}And k_{i_d}Two parameters, k, for a disturbance controller on the d-axis (usually a PI controller)_{p_q}And k_{i_q}Two parameters for a disturbance controller (typically a PI controller) on the q-axis.

Step 32: according to the Routh stability criterion, in order to stabilize the system, the following conditions need to be satisfied:

it should be noted that, in the present embodiment, k is_{p_d}＝2，k_{i_d}＝20，k_{p_q}＝2，k_{i_q}＝10。

Step S4: and constructing a dq axis control voltage containing the compensation voltage in each switching state of the inverter according to the compensation voltage of the dq axis.

Step 41: neutral point clampingThe switching states of the type three-level inverter are 27, the switching states of the K time control operation are K, and the switching state of the mth group is recorded as S_mThe expression is as follows:

S_m＝[S_U S_V S_W]

in the formula, S_UIndicating the switching state of the U-bridge arm, S_VIndicating the switching state of the V-arm, S_WThe switching state of the W arm is shown.

Establishing a model between an output phase voltage and an inverter bridge arm switching state, wherein the expression is as follows:

in the formula u_unm、u_vnmAnd u_wnmRespectively inverter in switched state S_mLower three-phase voltage, U_dcIs the dc side voltage.

Step 42: and obtaining dq axis control voltage corresponding to the m group of switching states of the inverter through coordinate transformation, wherein the expression is as follows:

in the formula u_dm(k)、u_qm(k) And theta (k) is a rotor position angle at the moment k.

Step 43: controlling the dq axis control voltage value, namely u, of the m group of switching states of an inverter in a permanent magnet synchronous motor control system_dm(k) And u_qm(k) Adding the dq axis compensation voltage at the k moment when the disturbance exists to obtain a dq axis control voltage value, namely u, of the compensated inverter in the m group of switching states_{d_com}(k) And u_{q_com}(k) The expression is as follows:

step S5: and constructing a target function of the prediction control of the strong robustness model, and selecting the switch state on the bridge arm of the inverter with the minimum target function value as the switch control output of the current period of the inverter to regulate the disturbance existing in the permanent magnet synchronous motor.

Step 51: d-axis and q-axis voltage values u corresponding to the compensated switching vectors of all three-level inverters_{d_com}(k)、u_{q_com}(k) Substituting the prediction current value i into the finite set prediction model of the permanent magnet synchronous motor constructed in S31 to obtain the predicted current value i of the inverter at the m group of switching states at the moment of k +1_dm(k +1) and i_qm(k +1), expressed as:

step 52: constructing a target function J of the permanent magnet synchronous motor model predictive control, wherein the expression is as follows:

in the formula i_d ^*、i_q ^*Reference currents of d and q axes of a permanent magnet synchronous motor control system are provided.

In this embodiment, the dq-axis reference current i of the permanent magnet synchronous motor control system_d ^*And i_q ^*And the calculation is given by the system control outer loop.

Step 53: establishing a one-step optimized calculation function by taking the minimum objective function value which enables current tracking to be the optimal control as an optimization target, wherein the expression is as follows:

J(S_l)＝min{J(S_m)}

in the formula, J (S)_l) Indicates when the inverter is in the first group of switch state combination S_lMinimum value of lower objective function, S_l∈[S₁,S₂,...,S_K]；

Step 54: and combining the switching states of the first group of the inverters to be used as a control instruction of a k-time permanent magnet synchronous motor control system, and controlling the switching states of each bridge arm of the inverters, thereby realizing the strong robustness model predictive control method of the permanent magnet synchronous motor.

Specifically, in this embodiment, parameter mismatch occurs at the 5 th second during the operation of the motor system, that is, parameters in the model predictive controller are changed, the states of the motor before and after the parameter mismatch are shown in fig. 3(a) (c), and the states of the motor before and after compensation by using the finite set model predictive method based on the disturbance observer are shown in fig. 3(b) (d). Therefore, the motor dq axis current tracking effect of the finite set model prediction method based on the disturbance observer is more accurate, and the anti-interference capability is stronger. Compared with a permanent magnet traction motor system based on a finite set model prediction method of a disturbance observer, the control method of the embodiment can enable the permanent magnet synchronous motor to correctly select the optimal vector of the motor under the condition that the disturbance such as parameter mismatch exists, so that the control can achieve the expected performance, the robustness of the motor control system is improved, and the prediction error caused by the parameter mismatch is eliminated in real time.

Example 2

In correspondence with the above method embodiments, the present embodiment provides a finite set model predictive control system of a permanent magnet synchronous motor based on a disturbance observer, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the above method when executing the computer program.

In summary, the methods and systems disclosed in the two embodiments of the present invention observe the motor disturbance in real time by the disturbance observer, compensate the motor model prediction control system, and achieve strong robustness control of the motor, so as to suppress the disturbance existing in the permanent magnet synchronous motor. The method is implemented without additional hardware equipment, improves the reliability level of train operation, reduces the equipment maintenance cost and the like, and has important significance.

The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.