CN115411991A - Inverter nonlinear self-learning method of synchronous reluctance motor driver - Google Patents

Inverter nonlinear self-learning method of synchronous reluctance motor driver Download PDF

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CN115411991A
CN115411991A CN202211227201.6A CN202211227201A CN115411991A CN 115411991 A CN115411991 A CN 115411991A CN 202211227201 A CN202211227201 A CN 202211227201A CN 115411991 A CN115411991 A CN 115411991A
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synchronous reluctance
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reluctance motor
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CN115411991B (en
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杨贵杰
钟本诚
苏健勇
谭凯文
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Harbin Institute of Technology Shenzhen
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/0003—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/14—Estimation or adaptation of machine parameters, e.g. flux, current or voltage
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/22—Current control, e.g. using a current control loop
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P25/00—Arrangements or methods for the control of AC motors characterised by the kind of AC motor or by structural details
    • H02P25/02—Arrangements or methods for the control of AC motors characterised by the kind of AC motor or by structural details characterised by the kind of motor
    • H02P25/08—Reluctance motors
    • H02P25/092—Converters specially adapted for controlling reluctance motors
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P2207/00—Indexing scheme relating to controlling arrangements characterised by the type of motor
    • H02P2207/05—Synchronous machines, e.g. with permanent magnets or DC excitation
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Abstract

An inverter nonlinear self-learning method of a synchronous reluctance motor driver relates to the technical field of motor control. The invention aims to solve the problem of poor compensation effect of the traditional inverter nonlinear compensation method. The invention relates to a nonlinear self-learning method of an inverter of a synchronous reluctance motor driver, which comprises the steps of injecting step current into a d axis or a q axis of a synchronous reluctance motor under the offline working condition of the synchronous reluctance motor to obtain a dq axis current given value under a rotating coordinate system of the synchronous reluctance motor, and recording dq axis voltage given values corresponding to different current given values; converting the dq axis current set value under the rotating coordinate system into a three-phase current set value under the static coordinate system; and calculating by adopting a particle swarm optimization algorithm according to the three-phase current given value and the dq-axis voltage given value to obtain saturation voltage drop, a shape coefficient and the resistance of the synchronous reluctance motor, and completing the nonlinear self-learning of the inverter of the synchronous reluctance motor driver.

Description

同步磁阻电机驱动器的逆变器非线性自学习方法Inverter nonlinear self-learning method for synchronous reluctance motor drive

技术领域technical field

本发明属于电机控制技术领域。The invention belongs to the technical field of motor control.

背景技术Background technique

同步磁阻电机由于结构简单、可靠性高、成本低等优点，在风机、泵类为主的工业应用领域得到了大量应用。随着工业应用领域对电机系统节能减排要求的提高，高性能的同步磁阻电机驱动器受到了高度关注。然而，由于同步磁阻电机采用电压型逆变器，电机控制效果受到逆变器非线性特性的影响。逆变器非线性导致电机相电流中存在五、七次电流谐波，增加了电机损耗。此外，逆变器非线性导致的电压误差会造成电机电感、电阻参数辨识和位置角度估计的误差，对同步磁阻电机的效率最优运行轨迹规划、无位置运行产生负面影响，进一步降低电机系统效率。为提高电机控制性能，研究针对同步磁阻电机的具有高通用性的逆变器非线性自学习方法及补偿方法极具应用价值。Due to the advantages of simple structure, high reliability, and low cost, synchronous reluctance motors have been widely used in industrial applications such as fans and pumps. With the improvement of energy-saving and emission-reduction requirements for motor systems in industrial applications, high-performance synchronous reluctance motor drivers have received high attention. However, since the synchronous reluctance motor uses a voltage-type inverter, the motor control effect is affected by the nonlinear characteristics of the inverter. The non-linearity of the inverter causes the fifth and seventh current harmonics in the phase current of the motor, which increases the loss of the motor. In addition, the voltage error caused by the nonlinearity of the inverter will cause errors in the identification of the motor inductance and resistance parameters and the estimation of the position angle, which will have a negative impact on the optimal operating trajectory planning and position-free operation of the synchronous reluctance motor, further reducing the motor system. efficiency. In order to improve the motor control performance, it is of great application value to study a highly versatile inverter nonlinear self-learning method and compensation method for synchronous reluctance motors.

传统的逆变器非线性补偿方法大多通过离线学习获取非线性特性，并采用描述函数法进行实时补偿，其描述函数主要采用符号函数和梯形函数。然而，这两类函数均难以描述在小电流区域内误差电压随电流非线性变化的特性，导致传统方法的补偿效果较差。此外，为了避免零序电压对逆变器非线性自学习结果准确性的影响，需要在特定的角度位置下进行自学习，这使得电机必须在自学习前进行定位。因此，传统方法不适用于电机已连接负载的情况，应用场合受限。另外，由于逆变器非线性离线学习一般在稳态下进行，受限于电机额定电流，可施加的电压幅值较低，此时电阻参数误差将明显影响逆变器非线性自学习的准确性。Most of the traditional inverter nonlinear compensation methods obtain the nonlinear characteristics through off-line learning, and use the description function method for real-time compensation. The description functions mainly use symbolic functions and trapezoidal functions. However, it is difficult for these two types of functions to describe the characteristics of the nonlinear variation of the error voltage with the current in the small current region, resulting in poor compensation effect of the traditional method. In addition, in order to avoid the influence of zero-sequence voltage on the accuracy of inverter nonlinear self-learning results, it is necessary to perform self-learning at a specific angle position, which makes the motor must be positioned before self-learning. Therefore, the traditional method is not suitable for the situation that the motor is connected to the load, and the application occasions are limited. In addition, since the nonlinear offline learning of the inverter is generally carried out in a steady state, limited by the rated current of the motor, the voltage amplitude that can be applied is low. At this time, the error of the resistance parameter will obviously affect the accuracy of the nonlinear self-learning of the inverter. sex.

发明内容Contents of the invention

本发明是为了解决符号函数和梯形函数难以描述在小电流区域内误差电压随电流非线性变化的特性，导致传统的逆变器非线性补偿方法的补偿效果较差；传统的逆变器非线性补偿方法应用场合受限；逆变器非线性离线学习受限于电机额定电流，导致电阻参数误差对逆变器非线性自学习的准确性产生影响的问题，现提供同步磁阻电机驱动器的逆变器非线性自学习方法。The purpose of the present invention is to solve the problem that the sign function and the trapezoidal function are difficult to describe the characteristic that the error voltage changes nonlinearly with the current in the small current region, which leads to poor compensation effect of the traditional inverter nonlinear compensation method; the traditional inverter nonlinear The application of the compensation method is limited; the nonlinear off-line learning of the inverter is limited by the rated current of the motor, which leads to the problem that the error of the resistance parameter affects the accuracy of the nonlinear self-learning of the inverter. Now we provide the inverter of the synchronous reluctance motor driver. Transformer nonlinear self-learning method.

同步磁阻电机驱动器的逆变器非线性自学习方法，包括以下步骤：A non-linear self-learning method for an inverter of a synchronous reluctance motor driver, comprising the following steps:

步骤一：在同步磁阻电机离线工况下，向同步磁阻电机的d轴或q轴注入阶梯电流、获得同步磁阻电机旋转坐标系下的dq轴电流给定值，并记录不同电流给定值对应的dq轴电压给定值；Step 1: In the offline working condition of the synchronous reluctance motor, inject a step current into the d-axis or q-axis of the synchronous reluctance motor, obtain the given value of the dq-axis current in the rotating coordinate system of the synchronous reluctance motor, and record the different current given values dq axis voltage given value corresponding to the fixed value;

步骤二：将旋转坐标系下的dq轴电流给定值变换为静止坐标系下的三相电流给定值；Step 2: Transform the dq-axis current given value in the rotating coordinate system into the three-phase current given value in the stationary coordinate system;

步骤三：根据三相电流给定值和dq轴电压给定值采用粒子群优化算法计算获得饱和压降、形状系数和同步磁阻电机的电阻，完成同步磁阻电机驱动器的逆变器非线性自学习。Step 3: Calculate the saturation voltage drop, shape factor and resistance of the synchronous reluctance motor by using the particle swarm optimization algorithm according to the given value of the three-phase current and the given value of the dq axis voltage, and complete the nonlinearity of the inverter of the synchronous reluctance motor driver self-study.

进一步的，上述步骤一中，向同步磁阻电机中注入的阶梯电流表达式如下：Further, in the above step 1, the expression of the stepped current injected into the synchronous reluctance motor is as follows:

Figure BDA0003880208490000021
Figure BDA0003880208490000021

其中，

Figure BDA0003880208490000022
为同步磁阻电机d轴或q轴中任意一轴注入的电流值，
Figure BDA0003880208490000023
为向同步磁阻电机另一轴中注入的电流值，Δistep为电流阶梯变化的步长，n为电流阶梯索引值，n＝1,2,...,N，N为阶梯总数。in,
Figure BDA0003880208490000022
is the current value injected into any one of the d-axis or q-axis of the synchronous reluctance motor,
Figure BDA0003880208490000023
is the current value injected into the other axis of the synchronous reluctance motor, Δi step is the step size of the current step change, n is the index value of the current step, n=1,2,...,N, and N is the total number of steps.

进一步的，上述在阶梯电流注入过程中，同步磁阻电机的电压满足下式：Further, the voltage of the synchronous reluctance motor satisfies the following formula during the step current injection process:

Figure BDA0003880208490000024
Figure BDA0003880208490000024

其中，ud和uq分别为同步磁阻电机的d轴和q轴电压，id和iq分别为同步磁阻电机的d轴和q轴电流，Ld和Lq分别为同步磁阻电机的d轴和q轴电感，R为同步磁阻电机的电阻，p为微分算子，ω为同步磁阻电机的电角速度。Among them, u d and u q are the d-axis and q-axis voltages of the synchronous reluctance motor, id and i q are the d -axis and q-axis currents of the synchronous reluctance motor, respectively, L d and L q are the synchronous reluctance motor The d-axis and q-axis inductance of the motor, R is the resistance of the synchronous reluctance motor, p is the differential operator, and ω is the electrical angular velocity of the synchronous reluctance motor.

进一步的，上述步骤二中，通过下式将旋转坐标系下的dq轴电流给定值变换为静止坐标系下的abc相电流给定值：Further, in the above step 2, the given value of the dq-axis current in the rotating coordinate system is transformed into the given value of the abc phase current in the stationary coordinate system by the following formula:

Figure BDA0003880208490000025
Figure BDA0003880208490000025

其中，

Figure BDA0003880208490000026
和
Figure BDA0003880208490000027
分别为静止坐标系下a、b和c相的电流给定值，
Figure BDA0003880208490000028
和
Figure BDA0003880208490000029
分别为旋转坐标系下d轴和q轴的电流给定值，θ为转子位置角。in,
Figure BDA0003880208490000026
and
Figure BDA0003880208490000027
are the given current values of phase a, b and c in the stationary coordinate system, respectively,
Figure BDA0003880208490000028
and
Figure BDA0003880208490000029
They are the current given values of the d-axis and q-axis in the rotating coordinate system, and θ is the rotor position angle.

进一步的，上述步骤三的具体过程如下：Further, the specific process of the above step three is as follows:

初始化：迭代次数k＝0,1,2,...，k＝0时，随机取粒子的位置和速度；Initialization: When the number of iterations k=0,1,2,...,k=0, the position and velocity of the particles are randomly selected;

S1：在粒子搜索空间范围和粒子群速度范围内，利用下式对粒子s的位置和速度进行更新，获得粒子s第k次迭代的位置Xs(k)和速度Vs(k)：S1: Within the scope of the particle search space and the velocity of the particle swarm, use the following formula to update the position and velocity of the particle s, and obtain the position X s (k) and velocity V s (k) of the k-th iteration of the particle s:

Figure BDA0003880208490000031
Figure BDA0003880208490000031

其中，Xs(k)为粒子s第k次迭代的位置、且

Figure BDA0003880208490000032
Among them, X s (k) is the position of the kth iteration of particle s, and
Figure BDA0003880208490000032

Vs(k)为粒子s第k次迭代的速度、且Vs(k)＝[vs1(k) vs2(k) vs3(k)]，vs1(k)、vs2(k)和vs3(k)分别为

Figure BDA0003880208490000033
和
Figure BDA0003880208490000034
的变化速度，
Figure BDA0003880208490000035
为粒子s第k次迭代时同步磁阻电机的电阻估计值，
Figure BDA0003880208490000036
和
Figure BDA0003880208490000037
分别为粒子s第k次迭代时的饱和压降估计值和形状系数估计值，V s (k) is the velocity of particle s in the kth iteration, and V s (k)=[v s1 (k) v s2 (k) v s3 (k)], v s1 (k), v s2 (k ) and v s3 (k) are respectively
Figure BDA0003880208490000033
and
Figure BDA0003880208490000034
the rate of change,
Figure BDA0003880208490000035
is the estimated resistance value of the synchronous reluctance motor at the kth iteration of particle s,
Figure BDA0003880208490000036
and
Figure BDA0003880208490000037
are the estimated value of the saturation pressure drop and the estimated value of the shape coefficient at the kth iteration of the particle s, respectively,

m为粒子位置的维数且m＝1,2,3，γ为惯性因子，c1和c2均为加速常数，rand(0,1)为0或1之间的随机数，gbm(k-1)为第k-1次迭代时全局最优粒子位置的第m维参数，pbsm(k-1)为粒子s在第k-1次迭代时最优位置的第m维参数，xsm(k-1)为粒子s在第k-1次迭代位置的第m维参数；m is the dimension of the particle position and m=1,2,3, γ is the inertia factor, c 1 and c 2 are acceleration constants, rand(0,1) is a random number between 0 and 1, gb m ( k-1) is the m-th dimensional parameter of the global optimal particle position at the k-1th iteration, pb sm (k-1) is the m-th dimensional parameter of the optimal position of particle s at the k-1th iteration, x sm (k-1) is the m-th dimension parameter of particle s at the k-1 iteration position;

S2：将Xs(k)中所包含的饱和压降估计值和形状系数估计值分别作为饱和压降ΔU和形状系数ξ代入下式，分别计算粒子s在各相电流下的电压误差

Figure BDA0003880208490000038
S2: Substitute the estimated value of the saturation voltage drop and the estimated value of the shape coefficient contained in X s (k) into the following formula as the saturation voltage drop ΔU and the shape coefficient ξ respectively, and calculate the voltage error of the particle s under each phase current
Figure BDA0003880208490000038

Figure BDA0003880208490000039
Figure BDA0003880208490000039

其中，r＝a,b,c；where r = a, b, c;

S3：将S2获得的

Figure BDA00038802084900000310
代入下式，获得粒子s对应的静止坐标系下r相的电压估计值
Figure BDA00038802084900000311
S3: the obtained S2
Figure BDA00038802084900000310
Substitute into the following formula to obtain the estimated voltage value of phase r in the static coordinate system corresponding to particle s
Figure BDA00038802084900000311

Figure BDA00038802084900000312
Figure BDA00038802084900000312

其中，

Figure BDA00038802084900000313
为静止坐标系下r相的电流给定值，u0为零序电压分量，
Figure BDA00038802084900000314
in,
Figure BDA00038802084900000313
is the current given value of phase r in the static coordinate system, u 0 is the zero-sequence voltage component,
Figure BDA00038802084900000314

S4：将S3获得的

Figure BDA00038802084900000315
代入下式，获得粒子s对应的d轴和q轴电压估计值
Figure BDA00038802084900000316
和
Figure BDA00038802084900000317
S4: the obtained S3
Figure BDA00038802084900000315
Substitute into the following formula to obtain the d-axis and q-axis voltage estimates corresponding to the particle s
Figure BDA00038802084900000316
and
Figure BDA00038802084900000317

Figure BDA0003880208490000041
Figure BDA0003880208490000041

其中，θ为转子位置角；Among them, θ is the rotor position angle;

S5：将S4获得的

Figure BDA0003880208490000042
和
Figure BDA0003880208490000043
代入下式计算粒子s第k次迭代的适应度fits(k)：S5: the obtained S4
Figure BDA0003880208490000042
and
Figure BDA0003880208490000043
Substituting the following formula to calculate the fitness fit s (k) of the k-th iteration of particle s:

Figure BDA0003880208490000044
Figure BDA0003880208490000044

其中，

Figure BDA0003880208490000045
和
Figure BDA0003880208490000046
分别为旋转坐标系下d轴和q轴的电压给定值；in,
Figure BDA0003880208490000045
and
Figure BDA0003880208490000046
Respectively, the given voltage values of the d-axis and q-axis in the rotating coordinate system;

S6：判断fits(k)是否小于当前粒子s最优位置Xs对应的适应度fits，是则将Xs(k)作为Xs，将fits(k)作为fits，然后执行S7，否则不对Xs和fits更新，然后执行S8；S6: Determine whether fit s (k) is smaller than the fitness fit s corresponding to the optimal position X s of the current particle s, and if so, use X s (k) as X s and fit s (k) as fit s , and then execute S7 , otherwise do not update X s and fit s , and then execute S8;

S7：判断fits(k)是否小于全局最优粒子位置X对应的适应度fit，是则将Xs(k)作为X，将fits(k)作为fit，然后执行S8，否则不对X和fit更新，然后执行S8；S7: Judging whether fit s (k) is smaller than the fitness fit corresponding to the global optimal particle position X, if so, set X s (k) as X and fit s (k) as fit, and then execute S8, otherwise, do not set X and fit update, and then execute S8;

S8：判断k+1是否超出迭代阈值，是则执行S9，否则使k＝k+1并返回S1；S8: judge whether k+1 exceeds the iteration threshold, if so, execute S9, otherwise make k=k+1 and return to S1;

S9：将X中的参数作为最终的饱和压降、形状系数和同步磁阻电机的电阻，完成同步磁阻电机驱动器的逆变器非线性自学习。S9: Use the parameters in X as the final saturation voltage drop, shape factor and resistance of the synchronous reluctance motor to complete the inverter nonlinear self-learning of the synchronous reluctance motor driver.

进一步的，上述利用步骤三获得的逆变器非线性饱和压降和形状系数分别计算逆变器各相的非线性误差电压，并根据控制需求将逆变器各相的非线性误差电压变换到需求坐标系下，并利用变换后的非线性误差电压对逆变器非线性补偿。Further, the nonlinear saturation voltage drop and shape coefficient of the inverter obtained in step 3 are used to calculate the nonlinear error voltage of each phase of the inverter, and according to the control requirements, the nonlinear error voltage of each phase of the inverter is transformed into Under the demand coordinate system, and use the transformed nonlinear error voltage to compensate the nonlinearity of the inverter.

本发明提供了一种同步磁阻电机驱动器的逆变器非线性自学习方法，该方法在电机离线工况下，通过电流注入对逆变器非线性进行自学习，整个自学习过程中不需要额外的检测设备，具有操作简单、实用性高的优点。并且，针对特定的逆变器，仅需通过该方法进行一次自学习，即可在后续电机在线运行过程中实时补偿逆变器非线性，提高电机运行性能。The invention provides an inverter nonlinear self-learning method of a synchronous reluctance motor driver. The method performs self-learning on the inverter nonlinearity through current injection under the off-line working condition of the motor, and the whole self-learning process does not require The additional detection equipment has the advantages of simple operation and high practicability. Moreover, for a specific inverter, only one self-learning is required through this method, and the nonlinearity of the inverter can be compensated in real time during the subsequent online operation of the electric motor to improve the operating performance of the electric motor.

本发明采用Sigmoid函数描述逆变器非线性特性，进而提高了对小电流区域误差电压随电机电流非线性变化的刻画能力，提高了逆变器非线性的补偿效果。此外，在确定逆变非线性相关参数的过程中，同步辨识了电机电阻参数，抑制了电阻参数误差对自学习结果准确性的负面影响。The invention uses the Sigmoid function to describe the nonlinear characteristics of the inverter, thereby improving the ability to describe the nonlinear variation of the error voltage in the small current region with the motor current, and improving the nonlinear compensation effect of the inverter. In addition, in the process of determining the parameters related to the nonlinearity of the inverter, the motor resistance parameters are identified synchronously, which suppresses the negative impact of resistance parameter errors on the accuracy of self-learning results.

本发明还考虑了零序电压的影响，给出了不受位置角影响的逆变器非线性自学习方法。相比于传统的自学习方法，本发明能够在任意角度位置下实现逆变器非线性的准确学习，进而避免了自学习前的电机定位操作，有利于在电机已连接负载的场合的应用，扩展了自学习方法的应用范围。The invention also considers the influence of the zero-sequence voltage, and provides a non-linear self-learning method of the inverter which is not affected by the position angle. Compared with the traditional self-learning method, the present invention can realize the accurate learning of inverter nonlinearity at any angle position, thereby avoiding the motor positioning operation before self-learning, which is beneficial to the application where the motor is connected to the load. The scope of application of the self-learning method is expanded.

本发明在数字控制芯片中自动完成，不需要人工调整，简单方便。整个自学习过程在离线状态下完成，算法无实时性要求，对数字控制芯片的性能要求低，便于在不同的系统间移植应用。The invention is automatically completed in the digital control chip, does not need manual adjustment, and is simple and convenient. The entire self-learning process is completed offline, the algorithm has no real-time requirements, and the performance requirements of the digital control chip are low, which is convenient for transplantation and application among different systems.

附图说明Description of drawings

图1为本发明所述同步磁阻电机驱动器的逆变器非线性自学习方法的原理框图；Fig. 1 is the functional block diagram of the inverter nonlinear self-learning method of synchronous reluctance motor driver described in the present invention;

图2为逆变器非线性自学习过程中的电流给定示意图；Figure 2 is a schematic diagram of the current given during the nonlinear self-learning process of the inverter;

图3为粒子群算法流程图；Fig. 3 is the particle swarm algorithm flow chart;

图4为不同电角度下自学习得到的估计电压给定值

Figure BDA0003880208490000051
与真实给定值
Figure BDA0003880208490000052
之间的对比图；Figure 4 shows the estimated voltage given value obtained by self-learning under different electrical angles
Figure BDA0003880208490000051
with real given value
Figure BDA0003880208490000052
comparison chart between

图5为不同电角度下自学习得到的逆变器非线性相电压误差曲线的对比图；Fig. 5 is a comparison diagram of inverter nonlinear phase voltage error curves obtained by self-learning under different electrical angles;

图6为逆变器非线性补偿前的相电流波形与其傅里叶分析结果曲线图；Fig. 6 is the graph of the phase current waveform and its Fourier analysis result before the nonlinear compensation of the inverter;

图7为逆变器非线性补偿后的相电流波形与其傅里叶分析结果曲线图。Fig. 7 is a curve diagram of the phase current waveform and its Fourier analysis result after nonlinear compensation of the inverter.

具体实施方式Detailed ways

下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有作出创造性劳动的前提下所获得的所有其它实施例，都属于本发明保护的范围。需要说明的是，在不冲突的情况下，本发明中的实施例及实施例中的特征可以相互组合。The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention. It should be noted that, in the case of no conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other.

具体实施方式一、参照图1至7具体说明本实施方式，本实施方式所述的同步磁阻电机驱动器的逆变器非线性自学习方法，包括以下步骤：Specific embodiments 1. Referring to FIGS. 1 to 7, this embodiment is described in detail. The inverter nonlinear self-learning method of a synchronous reluctance motor driver described in this embodiment includes the following steps:

步骤一：结合图1所示，在同步磁阻电机离线工况下，向同步磁阻电机的d轴或q轴注入阶梯电流、获得同步磁阻电机旋转坐标系下的dq轴电流给定值，并记录不同电流给定值对应的dq轴电压给定值。Step 1: As shown in Figure 1, under the offline condition of the synchronous reluctance motor, inject a step current into the d-axis or q-axis of the synchronous reluctance motor to obtain the given value of the dq-axis current in the rotating coordinate system of the synchronous reluctance motor , and record the dq axis voltage given values corresponding to different current given values.

具体的，向同步磁阻电机中注入的阶梯电流表达式如下：Specifically, the expression of the stepped current injected into the synchronous reluctance motor is as follows:

Figure BDA0003880208490000053
Figure BDA0003880208490000053

其中，

Figure BDA0003880208490000054
为同步磁阻电机d轴或q轴中任意一轴注入的电流值，
Figure BDA0003880208490000055
为向同步磁阻电机另一轴中注入的电流值，Δistep为电流阶梯变化的步长，n为电流阶梯索引值，n＝1,2,...,N，N为阶梯总数。in,
Figure BDA0003880208490000054
is the current value injected into any one of the d-axis or q-axis of the synchronous reluctance motor,
Figure BDA0003880208490000055
is the current value injected into the other axis of the synchronous reluctance motor, Δi step is the step size of the current step change, n is the index value of the current step, n=1,2,...,N, and N is the total number of steps.

在阶梯电流注入过程中，同步磁阻电机的电压满足下式：During the step current injection process, the voltage of the synchronous reluctance motor satisfies the following formula:

Figure BDA0003880208490000061
Figure BDA0003880208490000061

其中，ud和uq分别为同步磁阻电机的d轴和q轴电压，id和iq分别为同步磁阻电机的d轴和q轴电流，Ld和Lq分别为同步磁阻电机的d轴和q轴电感，R为同步磁阻电机的电阻，p为微分算子，ω为同步磁阻电机的电角速度。Among them, u d and u q are the d-axis and q-axis voltages of the synchronous reluctance motor, id and i q are the d -axis and q-axis currents of the synchronous reluctance motor, respectively, L d and L q are the synchronous reluctance motor The d-axis and q-axis inductance of the motor, R is the resistance of the synchronous reluctance motor, p is the differential operator, and ω is the electrical angular velocity of the synchronous reluctance motor.

在上述电流注入过程中，电机保持静止，电角速度ω等于0。此外，为了避免引入电流微分项的影响，在电流平稳阶段进行计算电压给定幅值。结合图2，在区域I中对实时的电压给定信号取平均，滤除信号中的高频噪声，得到电流给定

Figure BDA0003880208490000062
和
Figure BDA0003880208490000063
下对应的dq轴电压给定
Figure BDA0003880208490000064
和
Figure BDA0003880208490000065
数据；在区域II中，完成电流阶跃过程。During the above current injection process, the motor remains stationary and the electrical angular velocity ω is equal to 0. In addition, in order to avoid the influence of the introduction of the current differential term, the given amplitude of the voltage is calculated during the steady state of the current. Combined with Figure 2, the real-time voltage reference signal is averaged in area I, the high-frequency noise in the signal is filtered out, and the current reference is obtained
Figure BDA0003880208490000062
and
Figure BDA0003880208490000063
The corresponding dq axis voltage given below
Figure BDA0003880208490000064
and
Figure BDA0003880208490000065
Data; in region II, the current step process is completed.

步骤二：通过下式将旋转坐标系下的dq轴电流给定值变换为静止坐标系下的abc相电流给定值：Step 2: Transform the dq-axis current given value in the rotating coordinate system into the abc phase current given value in the stationary coordinate system by the following formula:

Figure BDA0003880208490000066
Figure BDA0003880208490000066

其中，

Figure BDA0003880208490000067
和
Figure BDA0003880208490000068
分别为静止坐标系下a、b和c相的电流给定值，
Figure BDA0003880208490000069
和
Figure BDA00038802084900000610
分别为旋转坐标系下d轴和q轴的电流给定值，θ为转子位置角。in,
Figure BDA0003880208490000067
and
Figure BDA0003880208490000068
are the given current values of phase a, b and c in the stationary coordinate system, respectively,
Figure BDA0003880208490000069
and
Figure BDA00038802084900000610
They are the current given values of the d-axis and q-axis in the rotating coordinate system, and θ is the rotor position angle.

步骤三：根据三相电流给定值和dq轴电压给定值采用粒子群优化算法计算获得饱和压降、形状系数和同步磁阻电机的电阻，完成同步磁阻电机驱动器的逆变器非线性自学习。Step 3: Calculate the saturation voltage drop, shape factor and resistance of the synchronous reluctance motor by using the particle swarm optimization algorithm according to the given value of the three-phase current and the given value of the dq axis voltage, and complete the nonlinearity of the inverter of the synchronous reluctance motor driver self-study.

如图3所示，具体过程如下：As shown in Figure 3, the specific process is as follows:

初始化：迭代次数k＝0,1,2,...，k＝0时，随机取粒子的位置和速度。Initialization: the number of iterations k=0,1,2,..., when k=0, the position and velocity of the particles are randomly selected.

S1：在粒子搜索空间范围和粒子群速度范围内，利用下式对粒子s的位置和速度进行更新，获得粒子s第k次迭代的位置Xs(k)和速度Vs(k)：S1: Within the scope of the particle search space and the velocity of the particle swarm, use the following formula to update the position and velocity of the particle s, and obtain the position X s (k) and velocity V s (k) of the k-th iteration of the particle s:

Figure BDA00038802084900000611
Figure BDA00038802084900000611

其中，Xs(k)为粒子s第k次迭代的位置、且

Figure BDA0003880208490000071
Among them, X s (k) is the position of the kth iteration of particle s, and
Figure BDA0003880208490000071

Vs(k)为粒子s第k次迭代的速度、且Vs(k)＝[vs1(k) vs2(k) vs3(k)]，vs1(k)、vs2(k)和vs3(k)分别为

Figure BDA0003880208490000072
和
Figure BDA0003880208490000073
的变化速度，
Figure BDA0003880208490000074
为粒子s第k次迭代时同步磁阻电机的电阻估计值，
Figure BDA0003880208490000075
和
Figure BDA0003880208490000076
分别为粒子s第k次迭代时的饱和压降估计值和形状系数估计值。V s (k) is the velocity of particle s in the kth iteration, and V s (k)=[v s1 (k) v s2 (k) v s3 (k)], v s1 (k), v s2 (k ) and v s3 (k) are respectively
Figure BDA0003880208490000072
and
Figure BDA0003880208490000073
the rate of change,
Figure BDA0003880208490000074
is the estimated resistance value of the synchronous reluctance motor at the kth iteration of particle s,
Figure BDA0003880208490000075
and
Figure BDA0003880208490000076
are the estimated value of the saturation pressure drop and the estimated value of the shape coefficient of the particle s at the kth iteration, respectively.

m为粒子位置的维数且m＝1,2,3，γ为惯性因子，c1和c2均为加速常数，rand(0,1)为0或1之间的随机数，gbm(k-1)为第k-1次迭代时全局最优粒子位置的第m维参数，pbsm(k-1)为粒子s在第k-1次迭代时最优位置的第m维参数，xsm(k-1)为粒子s在第k-1次迭代位置的第m维参数。m is the dimension of the particle position and m=1,2,3, γ is the inertia factor, c 1 and c 2 are acceleration constants, rand(0,1) is a random number between 0 and 1, gb m ( k-1) is the m-th dimensional parameter of the global optimal particle position at the k-1th iteration, pb sm (k-1) is the m-th dimensional parameter of the optimal position of particle s at the k-1th iteration, x sm (k-1) is the m-th dimension parameter of particle s at the k-1 iteration position.

S2：逆变器非线性和母线电压幅值、开关管导通压降、反并联二极管导通压降、死区时间、器件开关延时等因素均有关系；此外，寄生电容效应会导致小电流区域逆变器非线性电压误差随电流非线性变化。通常，能够将逆变器非线性建模成大电流区域的线性段和小电流区域的非线性段。在线性段，逆变器非线性电压误差可以认为是常数，此时的电压降称为饱和压降；而在非线性段，通过调整非线性函数的形状相关系数来描述电压误差随电流的变化关系。S2: The nonlinearity of the inverter is related to factors such as the voltage amplitude of the busbar, the conduction voltage drop of the switch tube, the conduction voltage drop of the anti-parallel diode, the dead time, and the switching delay of the device; in addition, the parasitic capacitance effect will cause small Current domain inverter nonlinear voltage error varies nonlinearly with current. In general, inverter nonlinearity can be modeled as a linear segment in the high current region and a nonlinear segment in the low current region. In the linear section, the nonlinear voltage error of the inverter can be considered as a constant, and the voltage drop at this time is called the saturation voltage drop; while in the nonlinear section, the change of the voltage error with the current is described by adjusting the shape correlation coefficient of the nonlinear function relation.

将Xs(k)中所包含的饱和压降估计值和形状系数估计值分别作为饱和压降ΔU和形状系数ξ代入下式，分别计算粒子s在各相电流下的电压误差

Figure BDA0003880208490000077
Substitute the estimated value of saturation voltage drop and estimated value of shape coefficient contained in X s (k) into the following formula as saturation voltage drop ΔU and shape coefficient ξ respectively, and calculate the voltage error of particle s under each phase current
Figure BDA0003880208490000077

Figure BDA0003880208490000078
Figure BDA0003880208490000078

其中，r＝a,b,c，e表示指数函数。Among them, r=a, b, c, e represents an exponential function.

S3：将S2获得的

Figure BDA0003880208490000079
代入下式，获得粒子s对应的静止坐标系下r相的电压估计值
Figure BDA00038802084900000710
S3: the obtained S2
Figure BDA0003880208490000079
Substitute into the following formula to obtain the estimated voltage value of phase r in the static coordinate system corresponding to particle s
Figure BDA00038802084900000710

Figure BDA00038802084900000711
Figure BDA00038802084900000711

其中，

Figure BDA00038802084900000712
为静止坐标系下r相的电流给定值，u0为零序电压分量，
Figure BDA00038802084900000713
in,
Figure BDA00038802084900000712
is the current given value of phase r in the static coordinate system, u 0 is the zero-sequence voltage component,
Figure BDA00038802084900000713

S4：将S3获得的

Figure BDA0003880208490000081
代入下式，获得粒子s对应的d轴和q轴电压估计值
Figure BDA0003880208490000082
和
Figure BDA0003880208490000083
S4: the obtained S3
Figure BDA0003880208490000081
Substitute into the following formula to obtain the d-axis and q-axis voltage estimates corresponding to the particle s
Figure BDA0003880208490000082
and
Figure BDA0003880208490000083

Figure BDA0003880208490000084
Figure BDA0003880208490000084

其中，θ为转子位置角；Among them, θ is the rotor position angle;

S5：将S4获得的

Figure BDA0003880208490000085
和
Figure BDA0003880208490000086
代入下式计算粒子s第k次迭代的适应度fits(k)：S5: the obtained S4
Figure BDA0003880208490000085
and
Figure BDA0003880208490000086
Substituting the following formula to calculate the fitness fit s (k) of the k-th iteration of particle s:

Figure BDA0003880208490000087
Figure BDA0003880208490000087

其中，

Figure BDA0003880208490000088
和
Figure BDA0003880208490000089
分别为旋转坐标系下d轴和q轴的电压给定值；in,
Figure BDA0003880208490000088
and
Figure BDA0003880208490000089
Respectively, the given voltage values of the d-axis and q-axis in the rotating coordinate system;

S6：判断fits(k)是否小于当前粒子s最优位置Xs对应的适应度fits，是则将Xs(k)作为Xs，将fits(k)作为fits，然后执行S7，否则不对Xs和fits更新，然后执行S8；S6: Determine whether fit s (k) is smaller than the fitness fit s corresponding to the optimal position X s of the current particle s, and if so, use X s (k) as X s and fit s (k) as fit s , and then execute S7 , otherwise do not update X s and fit s , and then execute S8;

S7：判断fits(k)是否小于全局最优粒子位置X对应的适应度fit，是则将Xs(k)作为X，将fits(k)作为fit，然后执行S8，否则不对X和fit更新，然后执行S8；S7: Judging whether fit s (k) is smaller than the fitness fit corresponding to the global optimal particle position X, if so, set X s (k) as X and fit s (k) as fit, and then execute S8, otherwise, do not set X and fit update, and then execute S8;

S8：判断k+1是否超出迭代阈值，是则执行S9，否则使k＝k+1并返回S1；S8: judge whether k+1 exceeds the iteration threshold, if so, execute S9, otherwise make k=k+1 and return to S1;

S9：将X中的参数作为最终的饱和压降、形状系数和同步磁阻电机的电阻，完成同步磁阻电机驱动器的逆变器非线性自学习。S9: Use the parameters in X as the final saturation voltage drop, shape factor and resistance of the synchronous reluctance motor to complete the inverter nonlinear self-learning of the synchronous reluctance motor driver.

具体实施方式二、本实施方式是在上述具体实施方式一所述的同步磁阻电机驱动器的逆变器非线性自学习方法的基础上，进一步的对逆变器非线性补偿，具体如下：Specific embodiment 2. This embodiment is based on the inverter nonlinear self-learning method of the synchronous reluctance motor driver described in the above specific embodiment 1, and further compensates the nonlinearity of the inverter, as follows:

步骤四：利用步骤三获得的逆变器非线性饱和压降和形状系数分别计算逆变器各相的非线性误差电压，并根据控制需求将逆变器各相的非线性误差电压变换到需求坐标系下，并利用变换后的非线性误差电压对逆变器非线性补偿。Step 4: Use the nonlinear saturation voltage drop and shape coefficient of the inverter obtained in step 3 to calculate the nonlinear error voltage of each phase of the inverter, and transform the nonlinear error voltage of each phase of the inverter to the required coordinate system, and use the transformed nonlinear error voltage to compensate the nonlinearity of the inverter.

以αβ坐标系下的逆变器非线性补偿为例，对应坐标系下的非线性电压误差的计算方法为：Taking the inverter nonlinear compensation in the αβ coordinate system as an example, the calculation method of the nonlinear voltage error in the corresponding coordinate system is:

Figure BDA00038802084900000810
Figure BDA00038802084900000810

其中，Δuα和Δuβ为αβ坐标系下α轴和β轴的逆变器非线性补偿电压。Among them, Δu α and Δu β are the inverter nonlinear compensation voltages of the α-axis and β-axis in the αβ coordinate system.

为验证本实施方式所提方法的有效性，进行实验：In order to verify the validity of the method proposed in this embodiment, an experiment is carried out:

首先，在四个随机角度下进行了逆变器非线性自学习，得到的dq轴真实电压给定与估计电压给定如图4所示。从图4中可以看出，在不同角度位置下，逆变器非线性自学习算法均能实现电压给定的准确重构。进一步，不同角度下自学习得到的逆变器非线性相电压误差曲线如图5所示，可以看到，在不同的角度位置下进行自学习，得到的结果基本一致，证明了本发明方法可以在不同角度位置下实现逆变器非线性的准确学习。First of all, the nonlinear self-learning of the inverter is carried out under four random angles, and the obtained dq axis real voltage reference and estimated voltage reference are shown in Figure 4. It can be seen from Figure 4 that under different angle positions, the nonlinear self-learning algorithm of the inverter can realize the accurate reconstruction of the voltage given. Further, the inverter nonlinear phase voltage error curves obtained by self-study at different angles are shown in Figure 5. It can be seen that the results obtained by self-study at different angle positions are basically the same, which proves that the method of the present invention can Accurate learning of inverter nonlinearities is achieved at different angular positions.

其次，对比了补偿前后的电流波形及其傅里叶分析结果，如图6和图7所示，相电流中的五、七次谐波成分得到了明显的抑制，电流正弦度提高。Secondly, comparing the current waveform and its Fourier analysis results before and after compensation, as shown in Figure 6 and Figure 7, the fifth and seventh harmonic components in the phase current have been significantly suppressed, and the current sine degree has been improved.

综上，本实施方式的同步磁阻电机驱动器逆变器非线性自学习与补偿方法，不受零序电压的影响，可以在任意电角度位置下实现对逆变器的非线性特性的准确学习；此外，通过电压补偿可以有效降低电流中的谐波成分，从而修正逆变器非线性对电机控制的负面影响。In summary, the non-linear self-learning and compensation method of the synchronous reluctance motor drive inverter in this embodiment is not affected by the zero-sequence voltage, and can realize accurate learning of the nonlinear characteristics of the inverter at any electrical angle position ; In addition, the harmonic components in the current can be effectively reduced through voltage compensation, thereby correcting the negative impact of inverter nonlinearity on motor control.

虽然在本文中参照了特定的实施方式来描述本发明，但是应该理解的是，这些实施例仅仅是本发明的原理和应用的示例。因此应该理解的是，可以对示例性的实施例进行许多修改，并且可以设计出其他的布置，只要不偏离所附权利要求所限定的本发明的精神和范围。应该理解的是，可以通过不同于原始权利要求所描述的方式来结合不同的从属权利要求和本文中所述的特征。还可以理解的是，结合单独实施例所描述的特征可以使用在其它所述实施例中。Although the invention is described herein with reference to specific embodiments, it should be understood that these embodiments are merely illustrative of the principles and applications of the invention. It is therefore to be understood that numerous modifications may be made to the exemplary embodiments and that other arrangements may be devised without departing from the spirit and scope of the invention as defined by the appended claims. It shall be understood that different dependent claims and features described herein may be combined in a different way than that described in the original claims. It will also be appreciated that features described in connection with individual embodiments can be used in other described embodiments.

Claims (6)

1. The inverter nonlinear self-learning method of the synchronous reluctance motor driver is characterized by comprising the following steps of:
the method comprises the following steps: under the offline working condition of the synchronous reluctance motor, injecting step current into a d axis or a q axis of the synchronous reluctance motor to obtain a dq axis current given value under a rotating coordinate system of the synchronous reluctance motor, and recording dq axis voltage given values corresponding to different current given values;
step two: converting the dq axis current set value under the rotating coordinate system into a three-phase current set value under the static coordinate system;
step three: and calculating by adopting a particle swarm optimization algorithm according to the three-phase current given value and the dq-axis voltage given value to obtain a saturation voltage drop, a shape coefficient and the resistance of the synchronous reluctance motor, and completing the nonlinear self-learning of the inverter of the synchronous reluctance motor driver.
2. The inverter nonlinear self-learning method of the synchronous reluctance motor driver as claimed in claim 1, wherein the step current injected into the synchronous reluctance motor in the first step is expressed as follows:
Figure FDA0003880208480000011
wherein ,
Figure FDA0003880208480000012
injecting current value for any axis of d axis or q axis of the synchronous reluctance motor,
Figure FDA0003880208480000013
for the value of the current injected into the other shaft of the synchronous reluctance machine, Δ i step Is the step size of the current step change, N is the current step index value, N =1, 2.
3. The inverter nonlinear self-learning method of a synchronous reluctance motor driver of claim 2, wherein, during the step current injection, the voltage of the synchronous reluctance motor satisfies the following equation:
Figure FDA0003880208480000014
wherein ,ud and uq D-axis and q-axis voltages, i, of synchronous reluctance machines, respectively d and iq D-axis and q-axis currents, L, of synchronous reluctance machines, respectively d and Lq The inductance of the d axis and the q axis of the synchronous reluctance motor are respectively, R is the resistance of the synchronous reluctance motor, p is a differential operator, and omega is the electrical angular velocity of the synchronous reluctance motor.
4. The inverter nonlinear self-learning method of the synchronous reluctance motor driver as claimed in claim 1, wherein in the second step, the given value of dq-axis current in the rotating coordinate system is transformed into the given value of abc phase current in the stationary coordinate system by the following formula:
Figure FDA0003880208480000015
wherein ,
Figure FDA0003880208480000016
and
Figure FDA0003880208480000017
respectively the given current values of a phase, b phase and c phase under a static coordinate system,
Figure FDA0003880208480000018
and
Figure FDA0003880208480000019
the current given values of a d axis and a q axis under a rotating coordinate system are respectively, and theta is a rotor position angle.
5. The inverter nonlinear self-learning method of the synchronous reluctance motor driver as claimed in claim 1, wherein the specific process of the third step is as follows:
initialization: randomly taking the position and the speed of the particle when the iteration number k =0,1, 2.. And k = 0;
s1: in the particle search space range and the particle swarm velocity range, the position and the velocity of the particle s are updated by the following formula to obtain the position X of the kth iteration of the particle s s (k) And velocity V s (k)：
Figure FDA0003880208480000021
wherein ,Xs (k) Is the position of the kth iteration of the particle s, and
Figure FDA0003880208480000022
V s (k) Is the velocity of the kth iteration of the particle s, and V s (k)＝[v s1 (k) v s2 (k) v s3 (k)]，v s1 (k)、v s2(k) and vs3 (k) Are respectively as
Figure FDA0003880208480000023
And
Figure FDA0003880208480000024
the speed of change of (a) is,
Figure FDA0003880208480000025
is the resistance estimated value of the synchronous reluctance motor at the kth iteration of the particle s,
Figure FDA0003880208480000026
and
Figure FDA0003880208480000027
respectively a saturation pressure drop estimated value and a shape coefficient estimated value of the particles s at the kth iteration,
m is the dimension of the particle position and m =1,2,3, gamma is the inertia factor, c 1 and c2 Are all acceleration constants, rand (0, 1) is a random number between 0 or 1, gb m (k-1) is the m-dimensional parameter of the global optimal particle position at the k-1 iteration, pb sm (k-1) is the m-dimensional parameter, x, of the optimal position of the particle s at the k-1 iteration sm (k-1) is the m-dimension parameter of the particle s at the k-1 iteration position;
s2: mixing X s (k) The estimated value of saturation voltage drop and the estimated value of shape coefficient contained in (1) are substituted as a saturation voltage drop Δ U and a shape coefficient ξ into the following expressions, respectively, and the voltage error of the particle s under each phase current is calculated
Figure FDA0003880208480000028
Figure FDA0003880208480000029
Wherein r = a, b, c;
s3: obtained in S2
Figure FDA00038802084800000211
Substituting the formula to obtain the voltage estimated value of the r phase in the stationary coordinate system corresponding to the particle s
Figure FDA00038802084800000212
Figure FDA00038802084800000210
wherein ,
Figure FDA0003880208480000031
a given value of r-phase current in a static coordinate system, u 0 Is a component of the zero-sequence voltage,
Figure FDA0003880208480000032
s4: obtained by S3
Figure FDA0003880208480000033
Substituting the following formula to obtain d-axis and q-axis voltage estimated values corresponding to the particles s
Figure FDA0003880208480000034
And
Figure FDA0003880208480000035
Figure FDA0003880208480000036
wherein θ is a rotor position angle;
s5: obtained by S4
Figure FDA0003880208480000037
And
Figure FDA0003880208480000038
substituting the formula to calculate the fitness fit of the kth iteration of the particle s s (k)：
Figure FDA0003880208480000039
wherein ,
Figure FDA00038802084800000310
and
Figure FDA00038802084800000311
respectively setting voltage values of a d axis and a q axis under a rotating coordinate system;
s6: judgment of fit s (k) Whether it is smaller than the optimal position X of the current particle s s Corresponding fitness fit s If yes, then X is s (k) As X s Will fit s (k) As fit s Then S7 is executed, otherwise, X is not executed s and fits Updating, and then executing S8;
s7: judgment of fit s (k) Whether the fitness is smaller than the fitness fit corresponding to the global optimal particle position X or not is judged, if so, the X is judged s (k) As X, fit s (k) As fit, then executing S8, otherwise, not updating X and fit, and then executing S8;
s8: judging whether k +1 exceeds an iteration threshold value, if so, executing S9, and if not, enabling k = k +1 and returning to S1;
s9: and taking the parameters in the X as the final saturated voltage drop, the shape coefficient and the resistance of the synchronous reluctance motor to complete the nonlinear self-learning of the inverter of the synchronous reluctance motor driver.
6. The inverter nonlinear self-learning method for the synchronous reluctance motor driver as claimed in claim 1, wherein the nonlinear error voltages of the phases of the inverter are calculated by using the nonlinear saturation voltage drop and the shape factor of the inverter obtained in the third step, and are transformed into a demand coordinate system according to the control demand, and the transformed nonlinear error voltages are used to perform nonlinear compensation on the inverter.
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