技术领域Technical field
本发明属于永磁同步直线电机控制技术领域，具体涉及一种PMLSM电机推力波动抑制方法、系统、芯片及设备。The invention belongs to the technical field of permanent magnet synchronous linear motor control, and specifically relates to a PMLSM motor thrust fluctuation suppression method, system, chip and equipment.
背景技术Background technique
永磁同步直线电机(Permanent Magnet Liner Synchronous Motor，PMLSM)具有推力密度高、响应速度快；可靠性好、效率高；可控性好、精度高等显著优点，广泛应用于高档数控机床、半导体加工设备、高速物流等行业。由于PMLSM运行时存在推力波动，一方面会引起震动和噪声，损伤机械和内部电器元件，影响直线电机的性能和可靠性；另一方面，推力波动会使直线电机的运行出现波动，从而影响其运动精度和稳定性，使得直线电机难以满足高精度的运动控制要求。Permanent Magnet Liner Synchronous Motor (PMLSM) has significant advantages such as high thrust density, fast response speed, good reliability, high efficiency, good controllability, and high precision. It is widely used in high-end CNC machine tools and semiconductor processing equipment. , high-speed logistics and other industries. Since thrust fluctuations exist during PMLSM operation, on the one hand, it will cause vibration and noise, damage the machinery and internal electrical components, and affect the performance and reliability of the linear motor; on the other hand, thrust fluctuations will cause fluctuations in the operation of the linear motor, thereby affecting its operation. Movement accuracy and stability make it difficult for linear motors to meet high-precision motion control requirements.
同时，PMLSM应用环境要求电机要同时具有高动态性和强抗扰性。目前，无差拍电流预测控制(Deadbeat Predictive Current Control,DPCC)应用在电流环能满足PMLSM高动态性的要求，但依赖于系统参数，系统稳定性对参数变化敏感，特别是电机电感的变化，当控制器电机模型参数和电机实际参数不一致时会产生电流静差，导致系统效率下降，无法输出额定转矩，以及无法工作在力矩控制模式等问题。同时，由于DPCC高度依赖精准的电机模型，参数变化导致电机鲁棒性差，进而导致抗扰性降低。At the same time, the PMLSM application environment requires the motor to have both high dynamics and strong immunity. Currently, Deadbeat Predictive Current Control (DPCC) applied in the current loop can meet the high dynamic requirements of PMLSM, but it depends on the system parameters, and the system stability is sensitive to parameter changes, especially changes in motor inductance. When the controller motor model parameters are inconsistent with the actual motor parameters, a current static difference will occur, resulting in a decrease in system efficiency, the inability to output rated torque, and the inability to work in torque control mode. At the same time, because DPCC highly relies on accurate motor models, parameter changes lead to poor motor robustness, which in turn leads to reduced interference immunity.
发明内容Contents of the invention
本发明所要解决的技术问题在于针对上述现有技术中的不足，提供一种PMLSM电机推力波动抑制方法、系统、芯片及设备，用于解决PMLSM推力波动的技术问题，提高电机的控制精度。在提高精度的基础上，通过扰动观测器，对电流环输出进行补偿，能够极大提高电机抗扰性能，使电机满足多种应用场合；同时对于电流静差问题，通过采用改进的无迹卡尔曼滤波器，在改善电流静差的同时，对采集的电流进行降噪，综合改善控制效果。The technical problem to be solved by the present invention is to provide a PMLSM motor thrust fluctuation suppression method, system, chip and equipment to solve the technical problem of PMLSM thrust fluctuation and improve the control accuracy of the motor in view of the above-mentioned deficiencies in the prior art. On the basis of improving the accuracy, the current loop output is compensated through the disturbance observer, which can greatly improve the anti-disturbance performance of the motor and enable the motor to meet a variety of applications. At the same time, for the problem of current static error, by using the improved unscented Karl The Mann filter not only improves the current static difference, but also reduces the noise of the collected current and comprehensively improves the control effect.
本发明采用以下技术方案：The present invention adopts the following technical solutions:
一种PMLSM电机推力波动抑制方法，包括以下步骤：A method for suppressing thrust fluctuations of PMLSM motors, including the following steps:
S1、建立矢量永磁同步直线电机电压模型，然后经欧拉离散化后得到矢量永磁同步直线电机电压预测模型；S1. Establish a vector permanent magnet synchronous linear motor voltage model, and then obtain a vector permanent magnet synchronous linear motor voltage prediction model after Euler discretization;
S2、基于步骤S1得到的矢量永磁同步直线电机电压预测模型建立变增益Luenberger扰动观测器，根据采集的k时刻的电压和电流计算得到k时刻的扰动电压；S2. Establish a variable gain Luenberger disturbance observer based on the vector permanent magnet synchronous linear motor voltage prediction model obtained in step S1, and calculate the disturbance voltage at time k based on the collected voltage and current at time k;
S3、建立模糊无迹卡尔曼滤波器，在消除电流静态误差和噪声的同时，模糊无迹卡尔曼滤波器利用k时刻电流计算得到k+1时刻的电流；根据步骤S2得到的k时刻的扰动电压计算k+1时刻的扰动电压，根据步骤S1得到的矢量永磁同步直线电机电压预测模型计算得到k+1时刻电压矢量，结合k+1时刻扰动电压对k+1时刻的电压矢量进行补偿。S3. Establish a fuzzy unscented Kalman filter. While eliminating current static errors and noise, the fuzzy unscented Kalman filter uses the current at k time to calculate the current at k+1 time; according to the disturbance at k time obtained in step S2 The voltage is calculated as the disturbance voltage at moment k+1. The voltage vector at moment k+1 is calculated based on the vector permanent magnet synchronous linear motor voltage prediction model obtained in step S1. The voltage vector at moment k+1 is compensated based on the disturbance voltage at moment k+1. .
具体的，步骤S1中，在旋转坐标系下建立连续域矢量PMLSM模型；根据连续域矢量PMLSM模型构建电机状态方程，经欧拉离散化后得到电压预测模型，再经电压反算得到控制电压矢量如下：Specifically, in step S1, a continuous domain vector PMLSM model is established under the rotating coordinate system; the motor state equation is constructed according to the continuous domain vector PMLSM model, and the voltage prediction model is obtained after Euler discretization, and then the control voltage vector is obtained through voltage back calculation as follows:
u(k+1)＝H-1{iref-G[Gik+Huk+K]-K}u(k+1)＝H-1 {iref -G[Gik +Huk +K]-K}
其中，u(k+1)为k时刻的预测输出电压，i(k)为k时刻的采集电流，u(k)为k时刻的采集电压，G为电流矩阵，H为电压矩阵，K为反电动势系数矩阵，TS为控制器周期，iref为转子参考电流。Among them, u(k+1) is the predicted output voltage at time k, i(k) is the collection current at time k, u(k) is the collection voltage at time k, G is the current matrix, H is the voltage matrix, and K is Back electromotive force coefficient matrix, TS is the controller period, iref is the rotor reference current.
进一步的，在两次采样间隔kT到(k+1)T之间，电机的运行速度v不变；设d-q轴系下连续域内的电压值ud(t)、uq(t)与kT时刻的采样电压值ud(kT)、uq(kT)相等，连续域矢量PMLSM模型如下：Furthermore, between the two sampling intervals kT to (k+1)T, the running speed v of the motor remains unchanged; assume that the voltage values ud (t), uq (t) and kT in the continuous domain under the dq axis system are The sampling voltage values ud (kT) and uq (kT) at the moment are equal, and the continuous domain vector PMLSM model is as follows:
其中，ud、uq、id、iq分别为两相旋转d-q坐标系下的d-q轴电压、d-q轴电流；Rs、Ls、ψf、v、τ、p分别为定子绕组电阻、定子绕组电感、永磁体磁链、动子相对定子的运行速度、极距、微分算子。Among them, ud , uq ,id , and iq are respectively the dq-axis voltage and dq-axis current in the two-phase rotating dq coordinate system; Rs , Ls , ψf , v, τ, and p are the stator winding resistance respectively. , stator winding inductance, permanent magnet flux linkage, moving speed of the mover relative to the stator, pole pitch, differential operator.
具体的，步骤S2中，变增益Luenberger扰动观测器的增益系数k2如下：Specifically, in step S2, the gain coefficient k2 of the variable-gain Luenberger perturbation observer is as follows:
其中，c、a为常量，id为电流真实值，为电流估计值，e为电流真实值和估计值的差值，δ为常量。Among them, c and a are constants, id is the true value of the current, is the estimated value of the current, e is the difference between the true value and the estimated value of the current, and δ is a constant.
具体的，步骤S3具体为：Specifically, step S3 is as follows:
S301、确定离散方程如下：S301. Determine the discrete equation as follows:
其中，X(k+1)为等效模型预测的k+1时刻d-q轴电流、扰动电压，T为状态输出矩阵，G为状态变量系数矩阵，H为输出系数矩阵，w(k)为过程噪声，v(k)为测量噪声，u＝[ud uq]，ud、uq分别为两相旋转d-q坐标系下的d-q轴电压，Z(k)为k时刻的输出电流和扰动电压；Among them, Noise, v(k) is the measurement noise, u=[ud uq ], ud and uq are the dq axis voltages in the two-phase rotating dq coordinate system respectively, Z(k) is the output current and disturbance at time k Voltage;
S302、根据Sigma点集的取值，选择一组采样数据及其所占权重值；S302. According to the value of the Sigma point set, select a set of sampling data and its weight value;
S303、根据加权求和步骤S302得到的Sigma点集的预测值求解协方差矩阵和系统状态量的预测方程；S303. Solve the prediction equation of the covariance matrix and system state quantity based on the predicted value of the Sigma point set obtained in the weighted summation step S302;
S304、根据步骤S301得到的离散电压方程预测系统k+1时刻预测均值矩阵X(k+1|k)和协方差矩阵P(k+1|k)；S304. Predict the mean matrix X(k+1|k) and covariance matrix P(k+1|k) at time k+1 of the discrete voltage equation prediction system obtained in step S301;
S305、根据预测均值矩阵X(k+1|k)和协方差矩阵P(k+1|k)，再次使用UT变换，产生新的9个Sigma点集；S305. According to the predicted mean matrix X(k+1|k) and the covariance matrix P(k+1|k), use UT transformation again to generate a new set of 9 Sigma points;
S306、计算k+1时刻的预测状态量得到5个预测结果，具体为：S306. Calculate the prediction state quantity at time k+1 to obtain 5 prediction results, specifically:
Z(i)(k+1|k)＝TX(k+1|k)+v(k)Z(i) (k+1|k)=TX(k+1|k)+v(k)
Z(i)(k+1|k)为系统k+1时刻系统估计输出量，T是状态输出矩阵，v(k)为测量噪声，i取0、1、2、3、4；Z(i) (k+1|k) is the estimated output of the system at time k+1, T is the state output matrix, v(k) is the measurement noise, and i is 0, 1, 2, 3, or 4;
S307、根据步骤S306得到的Z(i)(k+1|k)计算k+1时刻预测均值和协方差矩阵的权重，当实际值和理论值的差值ΔNk＞0时，减小测量噪声协方差R(k)，当实际值和理论值的差值ΔNk＜0时，增大测量噪声协方差R(k)，当实际值和理论值的差值ΔNk＝0时，保持测量噪声协方差R(k)不变，根据不同时刻ΔNk的值确定调整因子αk，实现动态调整；S307. Calculate the weight of the predicted mean and covariance matrix at time k+1 based on Z(i) (k+1|k) obtained in step S306. When the difference between the actual value and the theoretical value ΔNk >0, reduce the measurement Noise covariance R(k), when the difference between the actual value and the theoretical value ΔNk <0, increase the measurement noise covariance R(k), when the difference between the actual value and the theoretical value ΔNk =0, keep The measurement noise covariance R(k) remains unchanged, and the adjustment factor αk is determined according to the value of ΔNk at different times to achieve dynamic adjustment;
S308、计算增益矩阵Pxkzk为先验协方差矩阵，/>为后验协方差的逆矩阵；S308. Calculate gain matrix Pxkzk is the prior covariance matrix,/> is the inverse matrix of the posterior covariance;
S309、更新无迹卡尔曼滤波系统的状态矩阵与协方差矩阵，预测出k+1时刻的采集电流、扰动电压，在无差拍电流预测控制的同时实现补偿电压扰动。S309. Update the state matrix and covariance matrix of the unscented Kalman filter system, predict the acquisition current and disturbance voltage at k+1 time, and realize compensation for voltage disturbance while deadbeat current prediction control.
进一步的，步骤S304中，预测系统k+1时刻预测均值矩阵和协方差矩阵如下：Further, in step S304, the prediction mean matrix and covariance matrix of the prediction system at time k+1 are as follows:
其中，M＝[X(i)(k+1|k)-X(i)(k+1|k)]，X(k+1|k)为k+1时刻的预测均值矩阵，P(k+1|k)为k+1时刻的协方差矩阵，Q(k)为k时刻的过程噪声协方差矩阵。Among them, M=[X(i) (k+1|k)-X(i) (k+1|k)], X(k+1|k) is the prediction mean matrix at time k+1, P( k+1|k) is the covariance matrix at time k+1, and Q(k) is the process noise covariance matrix at time k.
进一步的，步骤S309中，更新系统的状态矩阵与协方差矩阵如下：Further, in step S309, the state matrix and covariance matrix of the system are updated as follows:
其中，X(k+1|k)为k+1时刻的预测均值矩阵，K(k+1)为增益矩阵，KT(k+1)为K(k+1)的转置矩阵，Z(k+1)为k+1时刻的输出电流和扰动电压，为k+1时刻的预测输出电流和预测输出扰动电压的均值，P(k+1|k)为k+1时刻预测的协方差，Pzkzk为后验协方差矩阵。Amongthem , (k+1) is the output current and disturbance voltage at k+1 moment, is the mean value of the predicted output current and the predicted output disturbance voltage at time k+1, P(k+1|k) is the predicted covariance at time k+1, and Pzkzk is the posterior covariance matrix.
第二方面，本发明实施例提供了一种PMLSM电机推力波动抑制系统，包括：In a second aspect, embodiments of the present invention provide a PMLSM motor thrust fluctuation suppression system, including:
离散模块，建立矢量永磁同步直线电机电压模型，然后经欧拉离散化后得到矢量永磁同步直线电机电压预测模型；The discrete module establishes a vector permanent magnet synchronous linear motor voltage model, and then obtains a vector permanent magnet synchronous linear motor voltage prediction model after Euler discretization;
计算模块，基于离散模块得到的矢量永磁同步直线电机电压预测模型建立变增益Luenberger扰动观测器，根据采集的k时刻的电压和电流计算得到k时刻的扰动电压；The calculation module establishes a variable gain Luenberger disturbance observer based on the voltage prediction model of the vector permanent magnet synchronous linear motor obtained by the discrete module, and calculates the disturbance voltage at time k based on the voltage and current collected at time k;
补偿模块，建立模糊无迹卡尔曼滤波器，在消除电流静态误差和噪声的同时，模糊无迹卡尔曼滤波器利用k时刻电流计算得到k+1时刻的电流；根据计算模块得到的k时刻的扰动电压计算k+1时刻的扰动电压，根据离散模块得到的矢量永磁同步直线电机电压预测模型计算得到k+1时刻电压矢量，结合k+1时刻扰动电压对k+1时刻的电压矢量进行补偿。The compensation module establishes a fuzzy unscented Kalman filter. While eliminating the static error and noise of the current, the fuzzy unscented Kalman filter uses the current at time k to calculate the current at time k+1; according to the current at time k obtained by the calculation module Disturbance voltage Calculate the disturbance voltage at time k+1. Calculate the voltage vector at time k+1 based on the vector permanent magnet synchronous linear motor voltage prediction model obtained by the discrete module. Combine the disturbance voltage at time k+1 to calculate the voltage vector at time k+1. compensate.
第三方面，一种计算机设备，包括存储器、处理器以及存储在所述存储器中并可在所述处理器上运行的计算机程序，所述处理器执行所述计算机程序时实现上述PMLSM电机推力波动抑制方法的步骤。In a third aspect, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the above-mentioned PMLSM motor thrust fluctuation is realized. Steps of the suppression method.
第四方面，本发明实施例提供了一种计算机可读存储介质，包括计算机程序，所述计算机程序被处理器执行时实现上述PMLSM电机推力波动抑制方法的步骤。In a fourth aspect, embodiments of the present invention provide a computer-readable storage medium, including a computer program that implements the steps of the above-mentioned PMLSM motor thrust fluctuation suppression method when executed by a processor.
与现有技术相比，本发明至少具有以下有益效果：Compared with the prior art, the present invention at least has the following beneficial effects:
一种PMLSM电机推力波动抑制方法，引入新的模糊控制律对传统无迹卡尔曼滤波器进行改进；与传统无迹卡尔曼滤波器相比，采用新型模糊无迹卡尔曼滤波器能提高电流噪声的抑制情况，同时能提高对电流静差能有效抑制，具有较好的可靠性和实用性。A PMLSM motor thrust fluctuation suppression method that introduces a new fuzzy control law to improve the traditional unscented Kalman filter; compared with the traditional unscented Kalman filter, the use of the new fuzzy unscented Kalman filter can improve the current noise The suppression situation can be improved and the current static difference can be effectively suppressed, which has good reliability and practicality.
进一步的，在两次采样间隔kT到(k+1)T之间，电机的运行速度v不变；设d-q轴系下连续域内的电压值ud(t)、uq(t)与kT时刻的采样电压值ud(kT)、uq(kT)相等，在旋转坐标系下建立连续域矢量PMLSM模型；根据连续域矢量PMLSM模型构建电机状态方程，经欧拉离散化后得到电流预测模型，再经电压反算得到控制电压矢量，这样能够非常精确的得到每个计算机周期的电压输出值，极大提到电机的运行精度。Furthermore, between the two sampling intervals kT to (k+1)T, the running speed v of the motor remains unchanged; assume that the voltage values ud (t), uq (t) and kT in the continuous domain under the dq axis system are The sampling voltage values ud (kT) and uq (kT) at the moment are equal, and the continuous domain vector PMLSM model is established under the rotating coordinate system; the motor state equation is constructed based on the continuous domain vector PMLSM model, and the current prediction is obtained after Euler discretization Model, and then the control voltage vector is obtained by back-calculating the voltage, so that the voltage output value of each computer cycle can be obtained very accurately, which greatly improves the operating accuracy of the motor.
进一步的，针对自适应调节扰动观测器增益系数k2，当观测电流和实际电流相差较大时，说明此时估计的扰动电压和实际扰动电压相差较大，高增益系数能够快速减小误差，但同时高增益系数会在采样电流存在噪声时产生较大的扰动电压估计值的振荡，因此，在误差减小到设定的范围后，及时减小增益系数，能够有效减小振荡，自适应调节扰动观测器增益系数的设计，能够快速准确的估计扰动电压的。Furthermore, for the adaptive adjustment of the disturbance observer gain coefficient k2 , when the difference between the observed current and the actual current is large, it means that the estimated disturbance voltage and the actual disturbance voltage are greatly different at this time, and a high gain coefficient can quickly reduce the error. But at the same time, a high gain coefficient will produce large oscillations that disturb the voltage estimate when there is noise in the sampling current. Therefore, after the error is reduced to the set range, reducing the gain coefficient in time can effectively reduce the oscillation and make the adaptive The design of adjusting the gain coefficient of the disturbance observer can quickly and accurately estimate the disturbance voltage.
进一步的，在整个系统中，我们通过估计电流和真实电流的对比，实现电压扰动的估计，这就要求采集到的电流值必须足够接近电机中真正的电流。但采集电流的多个环节，都会对采集的电流产生影响；通过无迹卡尔曼滤波算法，可以避免因电流的噪声，造成扰动电压的估计偏差，同时，由于电流噪声存在随机性，测量噪声协方差矩阵能够实时根据的电流值的平均值改变权重，减小因噪声的随机性造成的计算误差。Furthermore, in the entire system, we estimate the voltage disturbance by comparing the estimated current with the real current, which requires that the collected current value must be close enough to the real current in the motor. However, multiple aspects of collecting current will have an impact on the collected current; through the unscented Kalman filtering algorithm, the estimation deviation of the disturbance voltage caused by the noise of the current can be avoided. At the same time, due to the randomness of the current noise, the measurement noise is inconsistent. The variance matrix can change the weight in real time based on the average value of the current value, reducing calculation errors caused by the randomness of noise.
可以理解的是，上述第二方面至第四方面的有益效果可以参见上述第一方面中的相关描述，在此不再赘述。It can be understood that the beneficial effects of the above-mentioned second aspect to the fourth aspect can be referred to the relevant description in the above-mentioned first aspect, and will not be described again here.
综上所述，本发明通过设计无差拍电流预测控制，极大提高电机的运行精度，变增益扰动观测器，能够极大提高电机的抗扰性，同时使用模糊无迹卡尔曼滤波器，对采集电流进行处理，在降低电流噪声的同时，能够达到降低电机运行噪声的效果。In summary, the present invention greatly improves the operating accuracy of the motor by designing deadbeat current prediction control, and the variable gain disturbance observer can greatly improve the immunity of the motor. At the same time, the fuzzy unscented Kalman filter is used. Processing the collected current can achieve the effect of reducing motor operating noise while reducing current noise.
下面通过附图和实施例，对本发明的技术方案做进一步的详细描述。The technical solution of the present invention will be further described in detail below through the accompanying drawings and examples.
附图说明Description of the drawings
图1为本发明PMLSM矢量控制框图；Figure 1 is a PMLSM vector control block diagram of the present invention;
图2为改进模糊卡尔曼滤波算法流程图；Figure 2 is a flow chart of the improved fuzzy Kalman filter algorithm;
图3为本发明根据一实施例提供的一种芯片的框图。FIG. 3 is a block diagram of a chip provided according to an embodiment of the present invention.
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做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.
在本发明的描述中，需要理解的是，术语“包括”和“包含”指示所描述特征、整体、步骤、操作、元素和/或组件的存在，但并不排除一个或多个其它特征、整体、步骤、操作、元素、组件和/或其集合的存在或添加。In the description of the present invention, it is to be understood that the terms "comprising" and "including" indicate the presence of described features, integers, steps, operations, elements and/or components, but do not exclude one or more other features, The existence or addition of an integer, a step, an operation, an element, a component, and/or a collection thereof.
还应当理解，在本发明说明书中所使用的术语仅仅是出于描述特定实施例的目的而并不意在限制本发明。如在本发明说明书和所附权利要求书中所使用的那样，除非上下文清楚地指明其它情况，否则单数形式的“一”、“一个”及“该”意在包括复数形式。It should also be understood that the terminology used in the description of the present invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this specification and the appended claims, the singular forms "a", "an" and "the" are intended to include the plural forms unless the context clearly dictates otherwise.
还应当进一步理解，在本发明说明书和所附权利要求书中使用的术语“和/或”是指相关联列出的项中的一个或多个的任何组合以及所有可能组合，并且包括这些组合，例如，A和/或B，可以表示：单独存在A，同时存在A和B，单独存在B这三种情况。另外，本发明中字符“/”，一般表示前后关联对象是一种“或”的关系。It will be further understood that the term "and/or" as used in the specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items. , for example, A and/or B can mean: A alone exists, A and B exist simultaneously, and B exists alone. In addition, the character "/" in the present invention generally indicates that the related objects are in an "or" relationship.
应当理解，尽管在本发明实施例中可能采用术语第一、第二、第三等来描述预设范围等，但这些预设范围不应限于这些术语。这些术语仅用来将预设范围彼此区分开。例如，在不脱离本发明实施例范围的情况下，第一预设范围也可以被称为第二预设范围，类似地，第二预设范围也可以被称为第一预设范围。It should be understood that although the terms first, second, third, etc. may be used to describe the preset ranges and the like in embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are only used to distinguish preset ranges from each other. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be called a second preset range, and similarly, the second preset range may also be called a first preset range.
取决于语境，如在此所使用的词语“如果”可以被解释成为“在……时”或“当……时”或“响应于确定”或“响应于检测”。类似地，取决于语境，短语“如果确定”或“如果检测(陈述的条件或事件)”可以被解释成为“当确定时”或“响应于确定”或“当检测(陈述的条件或事件)时”或“响应于检测(陈述的条件或事件)”。Depending on the context, the word "if" as used herein may be interpreted as "when" or "when" or "in response to determination" or "in response to detection." Similarly, depending on the context, the phrase "if determined" or "if (stated condition or event) is detected" may be interpreted as "when determined" or "in response to determining" or "when (stated condition or event) is detected )" or "in response to detecting (a stated condition or event)".
在附图中示出了根据本发明公开实施例的各种结构示意图。这些图并非是按比例绘制的，其中为了清楚表达的目的，放大了某些细节，并且可能省略了某些细节。图中所示出的各种区域、层的形状及它们之间的相对大小、位置关系仅是示例性的，实际中可能由于制造公差或技术限制而有所偏差，并且本领域技术人员根据实际所需可以另外设计具有不同形状、大小、相对位置的区域/层。Various structural schematic diagrams according to disclosed embodiments of the present invention are shown in the accompanying drawings. The drawings are not drawn to scale, with certain details exaggerated and may have been omitted for purposes of clarity. The shapes of the various regions and layers shown in the figures and the relative sizes and positional relationships between them are only exemplary. In practice, there may be deviations due to manufacturing tolerances or technical limitations, and those skilled in the art will base their judgment on actual situations. Additional regions/layers with different shapes, sizes, and relative positions can be designed as needed.
本发明提供了一种PMLSM电机推力波动抑制方法，建立矢量永磁同步直线电机模型，欧拉离散化后得到电流预测模型，并向前预测两个周期后的电流，经过电压反算得到控制电压矢量；建立变增益Luenberger扰动观测器，根据估计电流和实际电流的差值，动态调节扰动观测器增益系数；建立模糊无迹卡尔曼滤波器，在消除电流静态误差和噪声的同时，预测下一拍电流和扰动。The invention provides a method for suppressing thrust fluctuations of PMLSM motors. It establishes a vector permanent magnet synchronous linear motor model, obtains a current prediction model after Euler discretization, and predicts the current after two cycles forward, and obtains the control voltage through voltage back calculation. vector; establish a variable-gain Luenberger disturbance observer, and dynamically adjust the disturbance observer gain coefficient according to the difference between the estimated current and the actual current; establish a fuzzy unscented Kalman filter to predict the next step while eliminating current static errors and noise. Shoot currents and disturbances.
请参阅图1，本发明一种PMLSM电机推力波动抑制方法，包括以下步骤：Please refer to Figure 1. A method for suppressing thrust fluctuations of a PMLSM motor according to the present invention includes the following steps:
S1、建立矢量永磁同步直线电机模型，欧拉离散化后得到电流预测模型，并向前预测一个周期后的电流，经过电压反算得到控制电压矢量；S1. Establish a vector permanent magnet synchronous linear motor model. After Euler discretization, the current prediction model is obtained, and the current after one cycle is predicted forward. The control voltage vector is obtained through voltage back calculation;
S101、在旋转坐标系下，建立连续域矢量PMLSM模型；S101. Under the rotating coordinate system, establish a continuous domain vector PMLSM model;
其中，ud、uq、id、iq分别是两相旋转d-q坐标系下的d-q轴电压、d-q轴电流；Rs、Ls、ψf、v、τ、p分别为定子绕组电阻、定子绕组电感、永磁体磁链、动子相对定子的运行速度、极距、微分算子。Among them, ud , uq ,id , and iq are respectively the dq-axis voltage and dq-axis current in the two-phase rotating dq coordinate system; Rs , Ls , ψf , v, τ, and p are the stator winding resistance respectively. , stator winding inductance, permanent magnet flux linkage, moving speed of the mover relative to the stator, pole pitch, differential operator.
S102、根据公式(1)构建电机状态方程：S102. Construct the motor state equation according to formula (1):
其中，ud、uq、id、iq分别为两相旋转d-q坐标系下的d-q轴电压、d-q轴电流；Rs、Ls、ψf、v、τ、p分别为定子绕组电阻、定子绕组电感、永磁体磁链、动子相对定子的运行速度、极距、微分算子。in, ud , uq ,id , and iq are respectively the dq-axis voltage and dq-axis current in the two-phase rotating dq coordinate system; Rs , Ls , ψf , v, τ, and p are respectively the stator winding resistance, stator Winding inductance, permanent magnet flux linkage, moving speed of the mover relative to the stator, pole pitch, differential operator.
初始条件为时间t0时刻的电流id(t0)、iq(t0)，得到时间t时刻对应的电流响应id(t)、iq(t)，电机动子相对定子的运行速度v和电压ud、uq是时变的，为简化公式(1)中的积分运算，对v和ud、uq做以下限定：The initial conditions are the currents id (t 0) and iq (t0 ) at time t 0. The corresponding current responses id (t) and iq (t) at time t are obtained. The operation of the motor rotor relative to the stator is obtained. The speed v and voltage ud and uq are time-varying. In order to simplify the integral operation in formula (1), v, ud and uq are limited as follows:
1、在两次采样间隔kT到(k+1)T之间，电机的运行速度v不变；1. Between the two sampling intervals kT to (k+1)T, the running speed v of the motor remains unchanged;
2、设d-q轴系下连续域内的电压值ud(t)、uq(t)与kT时刻的采样电压值ud(kT)、uq(kT)相等，即：2. Assume that the voltage values ud (t) and uq (t) in the continuous domain under the dq axis system are equal to the sampled voltage values ud (kT) and uq (kT) at time kT, that is:
进一步欧拉离散化后的电流预测模型为：The current prediction model after further Euler discretization is:
i(k+1)＝Gi(k)+Hu(k)+K (3)i(k+1)＝Gi(k)+Hu(k)+K (3)
其中，i(k+1)为k+1时刻的预测电流，i(k)为k时刻的采集电流，/>u(k)为k时刻的控制电压，/>G为电流矩阵，H为电压矩阵，/>K为反电动势系数矩阵，TS为控制器周期。Among them, i(k+1) is the predicted current at k+1 moment, i(k) is the collection current at time k,/> u(k) is the control voltage at time k,/> G is the current matrix, H is the voltage matrix,/> K is the back electromotive force coefficient matrix, TS is the controller cycle.
经过电压反算，得出控制电压矢量为：After voltage back calculation, the control voltage vector is obtained as:
u(k+1)＝H-1{iref-G[Gik+Huk+K]-K} (4)u(k+1)＝H-1 {iref -G[Gik +Huk +K]-K} (4)
其中，u(k+1)为k时刻的预测电压，iref为转子参考电流。Among them, u(k+1) is the predicted voltage at time k, iref is the rotor reference current.
S2、建立变增益Luenberger扰动观测器，根据估计电流和实际电流的差值，动态调节扰动观测器增益系数；S2. Establish a variable gain Luenberger disturbance observer, and dynamically adjust the gain coefficient of the disturbance observer based on the difference between the estimated current and the actual current;
无差拍电流预测方法有易于实现和动态响应快等优势，但劣势同样明显即对电机参数变化太过敏感；在实际的应用过程中电机会出现参数不匹配和未建模系统动态等干扰会使得控制性能下降，为此需要构建精准的扰动模型；具体如下：The deadbeat current prediction method has the advantages of easy implementation and fast dynamic response, but the disadvantage is also obvious, that is, it is too sensitive to changes in motor parameters; in the actual application process, interference such as parameter mismatch and unmodeled system dynamics may occur in the motor. This causes the control performance to degrade, for which an accurate disturbance model needs to be constructed; the details are as follows:
S201、根据步骤S101得到的公式(1)中的电机电压方程有：S201. The motor voltage equation in formula (1) obtained according to step S101 is:
由于扰动的存在，重新构建电机模型如下： Due to the existence of disturbance, the motor model is reconstructed as follows:
由此式，推倒出d-q轴扰动量为：From this formula, the d-q axis disturbance amount can be deduced as:
S202、根据推导的扰动方程，重构电机模型如下：S202. According to the derived disturbance equation, reconstruct the motor model as follows:
选取id、iq、fd、fq为观测器状态量，结合电机模型，构建包含扰动的状态方程如下：Select id , iq , fd , and fq as the observer state quantities. Combined with the motor model, the state equation including the disturbance is constructed as follows:
其中，x＝[xd xq fd fq]T，y＝[id iq id iq]T，u＝[ud uq]T，Among them, x＝[xd xq fd fq ]T , y＝[id iq id iq ]T , u＝[ud uq ]T ,
S203、将上述建立的状态空间表达式带入到扰动观测器中，得下式：S203. Bring the state space expression established above into the disturbance observer to get the following formula:
其中，in,
对上述公式离散化得扰动观测器模型：Discretize the above formula to get the disturbance observer model:
其中，in,
S204、扰动观测器增益系数根据观测电流和实际电流差值自适应调成，为此引入变增益函数：S204. The gain coefficient of the disturbance observer is adaptively adjusted according to the difference between the observed current and the actual current. For this purpose, a variable gain function is introduced:
其中，c、a为常量，id为电流真实值，为电流估计值，e为电流真实值和估计值的差值，δ为常量。Among them, c and a are constants, id is the true value of the current, is the estimated value of the current, e is the difference between the true value and the estimated value of the current, and δ is a constant.
电流差值补偿增益k1设为常数，扰动差值补偿增益计算如上式所示，实现小误差小增益，大误差大增益的效果。The current difference compensation gain k1 is set to a constant, and the disturbance difference compensation gain is calculated as shown in the above formula, achieving the effect of small error and small gain, and large error and large gain.
S3、建立模糊无迹卡尔曼滤波器，在消除电流静态误差和噪声的同时，预测下一拍电流和扰动。S3. Establish a fuzzy unscented Kalman filter to predict the next beat current and disturbance while eliminating current static errors and noise.
请参阅图2，由于控制器电机模型参数和实际电机参数存在不同，会导致轴电流存在电流静差，影响控制效果，采用模糊无迹卡尔曼滤波器进行噪声的处理同时对采集的电流进行降噪处理，在对电流处理的同时，可以对扰动进行预测，具体的步骤如下：Please refer to Figure 2. Due to the difference between the controller motor model parameters and the actual motor parameters, there will be a static current difference in the shaft current, which will affect the control effect. The fuzzy unscented Kalman filter is used to process the noise and reduce the collected current. Noise processing, while processing the current, can predict the disturbance. The specific steps are as follows:
S301、根据电机电压方程得到的离散方程有如下关系式：S301. The discrete equation obtained according to the motor voltage equation has the following relationship:
其中u＝[ud uq]，/>in u＝[ud uq ],/>
其中，X(k+1)为等效模型预测k+1时刻d-q轴电流、扰动电压，T为状态输出矩阵，G为状态变量系数矩阵，H为输出系数矩阵，TS为控制器周期，w(k)为过程噪声，v(k)为测量噪声，Z(k)为k时刻的输出电流和扰动电压。Among them, w(k) is the process noise, v(k) is the measurement noise, and Z(k) is the output current and disturbance voltage at time k.
S302、根据Sigma点集的取值，选择一组采样数据及其所占权重值：S302. According to the value of the Sigma point set, select a set of sampling data and its weight value:
其中，L为协方差匹配开窗大小，为当前状态的协方差矩阵(每步会实时更新)，Q为过程噪声协方差、R为测量噪声协方差，λ为缩放参数，用来降低总的预测误差；α为正的常数，决定着Sigma点的分布范围，/>为矩阵方根的第i列数的开平方值，X(i)为矩阵的第i列元素。Among them, L is the covariance matching window size, is the covariance matrix of the current state (updated in real time at each step), Q is the process noise covariance, R is the measurement noise covariance, λ is the scaling parameter, used to reduce the total prediction error; α is a positive constant, which determines The distribution range of Sigma points,/> is the square root value of the i-th column number of the matrix square root, and X(i) is the i-th column element of the matrix.
将公式(15)整理成：Organize formula (15) into:
其中，X(i)(k|k)为当前修正值，X(k|k)为当前时刻估计值，P(k|k)为更新修正的协方差。Amongthem ,
S303、根据加权求和步骤S302得到的Sigma点集的预测值来求解协方差矩阵和系统状态量的预测方程，其加权由公式得到：S303. Solve the prediction equation of the covariance matrix and system state quantity based on the predicted value of the Sigma point set obtained in the weighted summation step S302. The weighting is obtained by the formula:
其中，α为采样点的分布状态，用于改变Sigma点和之间的距离，β为状态分布参数，ω(i)为预测方程权值，/>为方差权重，/>为协方差权重。Among them, α is the distribution state of the sampling points, which is used to change the Sigma point and The distance between them, β is the state distribution parameter, ω(i) is the weight of the prediction equation,/> is the variance weight,/> is the covariance weight.
S304、根据步骤S301得到的离散电压方程预测系统k+1时刻预测均值矩阵和协方差矩阵：S304. Predict the mean matrix and covariance matrix at time k+1 of the discrete voltage equation prediction system obtained in step S301:
其中，M＝[X(i)(k+1|k)-x(i)(k+1|k)]，X(k+1|k)为k+1时刻的预测均值矩阵，P(k+1|k)为k+1时刻的协方差矩阵，Q(k)为k时刻的过程噪声协方差矩阵。Among them, M=[X(i) (k+1|k)-x(i) (k+1|k)], X(k+1|k) is the prediction mean matrix at time k+1, P( k+1|k) is the covariance matrix at time k+1, and Q(k) is the process noise covariance matrix at time k.
S305、根据预测均值矩阵X(k+1|k)和协方差矩阵P(k+1|k)，再次使用UT变换，产生新的9个Sigma点集；S305. According to the predicted mean matrix X(k+1|k) and the covariance matrix P(k+1|k), use UT transformation again to generate a new set of 9 Sigma points;
其中，X(i)(k+1|k)为k+1时刻的估计值，P(k+1|k)为k+1时刻预测的协方差，i取0、1、2、3、4。Amongthem , 4.
S306、将公式(19)带入公式(14)中，计算k+1时刻的预测状态量：S306. Put formula (19) into formula (14) to calculate the predicted state quantity at time k+1:
Z(i)(k+1|k)＝TX(k+1|k)+v(k) (18)Z(i) (k+1|k)=TX(k+1|k)+v(k) (18)
Z(i)(k+1|k)为系统k+1时刻系统估计输出量，T为状态输出矩阵，v(k)为测量噪声。Z(i) (k+1|k) is the estimated output of the system at time k+1, T is the state output matrix, and v(k) is the measurement noise.
S307、根据步骤S306得到的Z(i)(k+1|k)计算k+1时刻预测均值和协方差矩阵的权重：S307. Calculate the weight of the predicted mean and covariance matrix at k+1 time according to Z(i) (k+1|k) obtained in step S306:
其中，Pxkxk为先验协方差，Pzkzk为后验协方差，R(k+1)为k+1时刻噪声协方差。Among them, Pxkxk is the prior covariance, Pzkzk is the posterior covariance, and R(k+1) is the noise covariance at k+1 time.
设过程噪声Q(k)已知，采用模糊推理协方差匹配技术，实现自动调整测量噪声协方差R(k)，协方差匹配的思想是使量测噪声实际值和理论值保持相等。Assuming that the process noise Q(k) is known, fuzzy inference covariance matching technology is used to automatically adjust the measurement noise covariance R(k). The idea of covariance matching is to keep the actual value of the measurement noise equal to the theoretical value.
量测噪声实际值：Actual value of measurement noise:
量测噪声理论值：Theoretical value of measurement noise:
实际值和理论值的差值：Difference between actual value and theoretical value:
ΔNk＝Nk-Mk (22)ΔNk =Nk -Mk (22)
根据ΔNk的大小调整R(k)，使Nk和Mk保持一致；当ΔNk＞0时，减小R(k)，当ΔNk＜0时，增大R(k)，当ΔNk＝0时，保持R(k)不变，根据不同时刻ΔNk的值确定调整因子αk，实现动态调整。Adjust R(k) according to the size of ΔNk to keep Nk and Mk consistent; when ΔNk > 0, reduce R (k), when ΔNk < 0, increase R (k), when ΔN Whenk = 0, keep R(k) unchanged, and determine the adjustment factor αk according to the value of ΔNk at different times to achieve dynamic adjustment.
S308、计算增益矩阵：S308. Calculate the gain matrix:
其中，Pxkzk为先验协方差矩阵，为后验协方差的逆矩阵。Among them, Pxkzk is the prior covariance matrix, is the inverse matrix of the posterior covariance.
S309、更新无迹卡尔曼滤波系统的状态矩阵与协方差矩阵：S309. Update the state matrix and covariance matrix of the unscented Kalman filter system:
其中，X(k+1|k)为k+1时刻的预测均值矩阵，K(k+1)为增益矩阵，KT(k+1)为K(k+1)的转置矩阵，Z(k+1)为k+1时刻的输出电流和扰动电压，为k+1时刻的预测输出电流和预测输出扰动电压的均值，P(k+1|k)为k+1时刻预测的协方差，Pzkzk为后验协方差矩阵。Amongthem , (k+1) is the output current and disturbance voltage at k+1 moment, is the mean value of the predicted output current and the predicted output disturbance voltage at time k+1, P(k+1|k) is the predicted covariance at time k+1, and Pzkzk is the posterior covariance matrix.
至此，通过无迹卡尔曼滤波，在消除电流静差的同时，预测出k+1时刻的采集电流、扰动电压，实现无差拍电流预测控制的同时，及时补偿电压扰动，有效减小PMLSM推力扰动。At this point, through the unscented Kalman filter, while eliminating the current static difference, the acquisition current and disturbance voltage at k+1 time are predicted, achieving dead-beat current prediction control while compensating the voltage disturbance in time, effectively reducing the PMLSM thrust disturbance.
本发明再一个实施例中，提供一种PMLSM电机推力波动抑制系统，该系统能够用于实现上述PMLSM电机推力波动抑制方法，具体的，该PMLSM电机推力波动抑制系统包括离散模块、计算模块以及补偿模块。In yet another embodiment of the present invention, a PMLSM motor thrust fluctuation suppression system is provided, which can be used to implement the above PMLSM motor thrust fluctuation suppression method. Specifically, the PMLSM motor thrust fluctuation suppression system includes a discrete module, a calculation module and a compensation module. module.
其中，离散模块，建立矢量永磁同步直线电机电压模型，然后经欧拉离散化后得到矢量永磁同步直线电机电压预测模型；Among them, the discrete module establishes a vector permanent magnet synchronous linear motor voltage model, and then obtains a vector permanent magnet synchronous linear motor voltage prediction model after Euler discretization;
计算模块，基于离散模块得到的矢量永磁同步直线电机电压预测模型建立变增益Luenberger扰动观测器，根据采集的k时刻的电压和电流计算得到的k时刻的扰动电压；The calculation module establishes a variable gain Luenberger disturbance observer based on the vector permanent magnet synchronous linear motor voltage prediction model obtained by the discrete module, and calculates the disturbance voltage at time k based on the voltage and current collected at time k;
补偿模块，建立模糊无迹卡尔曼滤波器，在消除电流静态误差和噪声的同时，模糊无迹卡尔曼滤波器利用k时刻电流计算得到k+1时刻的电流；根据计算模块得到的k时刻的扰动电压计算k+1时刻的扰动电压，根据离散模块得到的矢量永磁同步直线电机电压预测模型计算得到k+1时刻电压矢量，结合k+1时刻扰动电压对k+1时刻的电压矢量进行补偿。The compensation module establishes a fuzzy unscented Kalman filter. While eliminating the static error and noise of the current, the fuzzy unscented Kalman filter uses the current at time k to calculate the current at time k+1; according to the current at time k obtained by the calculation module Disturbance voltage Calculate the disturbance voltage at time k+1. Calculate the voltage vector at time k+1 based on the vector permanent magnet synchronous linear motor voltage prediction model obtained by the discrete module. Combine the disturbance voltage at time k+1 to calculate the voltage vector at time k+1. compensate.
本发明再一个实施例中，提供了一种终端设备，该终端设备包括处理器以及存储器，所述存储器用于存储计算机程序，所述计算机程序包括程序指令，所述处理器用于执行所述计算机存储介质存储的程序指令。处理器可能是中央处理单元(Central ProcessingUnit，CPU)，还可以是其他通用处理器、数字信号处理器(Digital Signal Processor、DSP)、专用集成电路(Application Specific Integrated Circuit，ASIC)、现成可编程门阵列(Field-Programmable Gate Array，FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等，其是终端的计算核心以及控制核心，其适于实现一条或一条以上指令，具体适于加载并执行一条或一条以上指令从而实现相应方法流程或相应功能；本发明实施例所述的处理器可以用于PMLSM电机推力波动抑制方法的操作，包括：In yet another embodiment of the present invention, a terminal device is provided. The terminal device includes a processor and a memory. The memory is used to store a computer program. The computer program includes program instructions. The processor is used to execute the computer program. A storage medium stores program instructions. The processor may be a Central Processing Unit (CPU), or other general-purpose processor, Digital Signal Processor (DSP), Application Specific Integrated Circuit (ASIC), or off-the-shelf programmable gate Array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, and are suitable for implementing one or more instructions, specifically Suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding functions; the processor described in the embodiment of the present invention can be used to operate the PMLSM motor thrust fluctuation suppression method, including:
建立矢量永磁同步直线电机电压模型，然后经欧拉离散化后得到矢量永磁同步直线电机电压预测模型；基于矢量永磁同步直线电机电压预测模型建立变增益Luenberger扰动观测器，根据采集的k时刻的电压和电流计算得到k时刻的扰动电压；建立模糊无迹卡尔曼滤波器，在消除电流静态误差和噪声的同时，模糊无迹卡尔曼滤波器利用k时刻电流计算得到k+1时刻的电流；根据k时刻的扰动电压计算k+1时刻的扰动电压，根据矢量永磁同步直线电机电压预测模型计算得到k+1时刻电压矢量，结合k+1时刻扰动电压对k+1时刻的电压矢量进行补偿。A vector permanent magnet synchronous linear motor voltage model is established, and then the vector permanent magnet synchronous linear motor voltage prediction model is obtained after Euler discretization. Based on the vector permanent magnet synchronous linear motor voltage prediction model, a variable gain Luenberger disturbance observer is established. According to the collected k The voltage and current at time are calculated to obtain the disturbance voltage at time k; a fuzzy unscented Kalman filter is established to eliminate the static error and noise of the current. The fuzzy unscented Kalman filter uses the current at time k to calculate the voltage at time k+1. Current; calculate the disturbance voltage at moment k+1 based on the disturbance voltage at moment k, calculate the voltage vector at moment k+1 according to the vector permanent magnet synchronous linear motor voltage prediction model, and combine the disturbance voltage at moment k+1 to the voltage at moment k+1 vector to compensate.
请参阅图3，芯片600包括处理器622，其数量可以为一个或多个，以及存储器632，用于存储可由处理器622执行的计算机程序。存储器632中存储的计算机程序可以包括一个或一个以上的每一个对应于一组指令的模块。此外，处理器622可以被配置为执行该计算机程序，以执行上述的PMLSM电机推力波动抑制方法。Referring to FIG. 3 , the chip 600 includes a processor 622 , which may be one or more, and a memory 632 for storing computer programs executable by the processor 622 . The computer program stored in memory 632 may include one or more modules, each of which corresponds to a set of instructions. In addition, the processor 622 may be configured to execute the computer program to perform the above-mentioned PMLSM motor thrust fluctuation suppression method.
另外，芯片600还可以包括电源组件626和通信组件650，该电源组件626可以被配置为执行芯片600的电源管理，该通信组件650可以被配置为实现芯片600的通信，例如，有线或无线通信。此外，该芯片600还可以包括输入/输出(I/O)接口658。芯片600可以操作基于存储在存储器632的操作系统。In addition, the chip 600 may also include a power supply component 626 that may be configured to perform power management of the chip 600 and a communication component 650 that may be configured to implement communication of the chip 600, for example, wired or wireless communication. . In addition, the chip 600 may also include an input/output (I/O) interface 658. Chip 600 may operate based on an operating system stored in memory 632 .
在另一示例性实施例中，还提供了一种包括程序指令的计算机可读存储介质，该程序指令被处理器执行时实现上述的PMLSM电机推力波动抑制方法的步骤。例如，该非临时性计算机可读存储介质可以为上述包括程序指令的存储器632，上述程序指令可由芯片600的处理器622执行以完成上述的PMLSM电机推力波动抑制方法。In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided. When the program instructions are executed by a processor, the steps of the above-mentioned PMLSM motor thrust fluctuation suppression method are implemented. For example, the non-transitory computer-readable storage medium can be the above-mentioned memory 632 including program instructions, and the above-mentioned program instructions can be executed by the processor 622 of the chip 600 to complete the above-mentioned PMLSM motor thrust fluctuation suppression method.
本发明再一个实施例中，本发明还提供了一种存储介质，具体为计算机可读存储介质(Memory)，所述计算机可读存储介质是终端设备中的记忆设备，用于存放程序和数据。可以理解的是，此处的计算机可读存储介质既可以包括终端设备中的内置存储介质，当然也可以包括终端设备所支持的扩展存储介质。计算机可读存储介质提供存储空间，该存储空间存储了终端的操作系统。并且，在该存储空间中还存放了适于被处理器加载并执行的一条或一条以上的指令，这些指令可以是一个或一个以上的计算机程序(包括程序代码)。需要说明的是，此处的计算机可读存储介质可以是高速RAM存储器，也可以是非不稳定的存储器(Non-Volatile Memory)，例如至少一个磁盘存储器。In yet another embodiment of the present invention, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory). The computer-readable storage medium is a memory device in a terminal device and is used to store programs and data. . It can be understood that the computer-readable storage medium here may include a built-in storage medium in the terminal device, and of course may also include an extended storage medium supported by the terminal device. The computer-readable storage medium provides storage space, and the storage space stores the operating system of the terminal. Furthermore, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space. These instructions may be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here may be a high-speed RAM memory or a non-volatile memory (Non-Volatile Memory), such as at least one disk memory.
可由处理器加载并执行计算机可读存储介质中存放的一条或一条以上指令，以实现上述实施例中有关PMLSM电机推力波动抑制方法的相应步骤；计算机可读存储介质中的一条或一条以上指令由处理器加载并执行如下步骤：One or more instructions stored in the computer-readable storage medium can be loaded and executed by the processor to implement the corresponding steps related to the PMLSM motor thrust fluctuation suppression method in the above embodiment; one or more instructions in the computer-readable storage medium are composed of The processor loads and performs the following steps:
建立矢量永磁同步直线电机电压模型，然后经欧拉离散化后得到矢量永磁同步直线电机电压预测模型；基于矢量永磁同步直线电机电压预测模型建立变增益Luenberger扰动观测器，根据采集的k时刻的电压和电流计算得到k时刻的扰动电压；建立模糊无迹卡尔曼滤波器，在消除电流静态误差和噪声的同时，模糊无迹卡尔曼滤波器利用k时刻电流计算得到k+1时刻的电流；根据k时刻的扰动电压计算k+1时刻的扰动电压，根据矢量永磁同步直线电机电压预测模型计算得到k+1时刻电压矢量，结合k+1时刻扰动电压对k+1时刻的电压矢量进行补偿。A vector permanent magnet synchronous linear motor voltage model is established, and then the vector permanent magnet synchronous linear motor voltage prediction model is obtained after Euler discretization. Based on the vector permanent magnet synchronous linear motor voltage prediction model, a variable gain Luenberger disturbance observer is established. According to the collected k The voltage and current at time are calculated to obtain the disturbance voltage at time k; a fuzzy unscented Kalman filter is established to eliminate the static error and noise of the current. The fuzzy unscented Kalman filter uses the current at time k to calculate the voltage at time k+1. Current; calculate the disturbance voltage at moment k+1 based on the disturbance voltage at moment k, calculate the voltage vector at moment k+1 according to the vector permanent magnet synchronous linear motor voltage prediction model, and combine the disturbance voltage at moment k+1 to the voltage at moment k+1 vector to compensate.
综上所述，本发明一种PMLSM电机推力波动抑制方法、系统、芯片及设备，通过设计无差拍电流预测控制，极大提高电机的运行精度，变增益扰动观测器，能够极大提高电机的抗扰性，同时使用模糊无迹卡尔曼滤波器，对采集电流进行处理，在降低电流噪声的同时，能够达到降低电机运行噪声的效果。In summary, the present invention is a PMLSM motor thrust fluctuation suppression method, system, chip and equipment. By designing deadbeat current prediction control, the operating accuracy of the motor is greatly improved. The variable gain disturbance observer can greatly improve the motor's operation accuracy. It also uses a fuzzy unscented Kalman filter to process the collected current, which not only reduces the current noise, but also reduces the motor operating noise.
所属领域的技术人员可以清楚地了解到，为了描述的方便和简洁，仅以上述各功能单元、模块的划分进行举例说明，实际应用中，可以根据需要而将上述功能分配由不同的功能单元、模块完成，即将所述装置的内部结构划分成不同的功能单元或模块，以完成以上描述的全部或者部分功能。实施例中的各功能单元、模块可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中，上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。另外，各功能单元、模块的具体名称也只是为了便于相互区分，并不用于限制本申请的保护范围。上述系统中单元、模块的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above functional units and modules is used as an example. In actual applications, the above functions can be allocated to different functional units and modules according to needs. Module completion means dividing the internal structure of the device into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be hardware-based. It can also be implemented in the form of software functional units. In addition, the specific names of each functional unit and module are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present application. For the specific working processes of the units and modules in the above system, please refer to the corresponding processes in the foregoing method embodiments, and will not be described again here.
在上述实施例中，对各个实施例的描述都各有侧重，某个实施例中没有详述或记载的部分，可以参见其它实施例的相关描述。In the above embodiments, each embodiment is described with its own emphasis. For parts that are not detailed or documented in a certain embodiment, please refer to the relevant descriptions of other embodiments.
本领域普通技术人员可以意识到，结合本发明中所公开的实施例描述的各示例的单元及算法步骤，能够以电子硬件、或者计算机软件和电子硬件的结合来实现。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本发明的范围。Those of ordinary skill in the art can appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented with electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans may implement the described functionality using different methods for each specific application, but such implementations should not be considered to be beyond the scope of the present invention.
在本发明所提供的实施例中，应该理解到，所揭露的装置/终端和方法，可以通过其它的方式实现。例如，以上所描述的装置/终端实施例仅仅是示意性的，例如，所述模块或单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通讯连接可以是通过一些接口，装置或单元的间接耦合或通讯连接，可以是电性，机械或其它的形式。In the embodiments provided by the present invention, it should be understood that the disclosed device/terminal and method can be implemented in other ways. For example, the device/terminal embodiments described above are only illustrative. For example, the division of modules or units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or units. Components may be combined or may be integrated into another system, or some features may be ignored, or not implemented. On the other hand, the coupling or direct coupling or communication connection between each other shown or discussed may be through some interfaces, indirect coupling or communication connection of devices or units, which may be in electrical, mechanical or other forms.
所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or they may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.
另外，在本发明各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。In addition, each functional unit in various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated units can be implemented in the form of hardware or software functional units.
所述集成的模块/单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本发明实现上述实施例方法中的全部或部分流程，也可以通过计算机程序来指令相关的硬件来完成，所述的计算机程序可存储于一计算机可读存储介质中，该计算机程序在被处理器执行时，可实现上述各个方法实施例的步骤。其中，所述计算机程序包括计算机程序代码，所述计算机程序代码可以为源代码形式、对象代码形式、可执行文件或某些中间形式等。所述计算机可读介质可以包括：能够携带所述计算机程序代码的任何实体或装置、记录介质、U盘、移动硬盘、磁碟、光盘、计算机存储器、只读存储器(Read-Only Memory，ROM)、随机存取存储器(RandomAccess Memory，RAM)、电载波信号、电信信号以及软件分发介质等，需要说明的是，所述计算机可读介质包含的内容可以根据司法管辖区内立法和专利实践的要求进行适当的增减，例如在某些司法管辖区，根据立法和专利实践，计算机可读介质不包括是电载波信号和电信信号。If the integrated module/unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention can implement all or part of the processes in the methods of the above embodiments, and can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can be stored in a computer-readable storage medium. When the program is executed by the processor, the steps of each of the above method embodiments can be implemented. Wherein, the computer program includes computer program code, which may be in the form of source code, object code, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording media, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (Read-Only Memory, ROM) , random access memory (Random Access Memory, RAM), electrical carrier signals, telecommunications signals, software distribution media, etc. It should be noted that the content contained in the computer readable media can be based on the requirements of legislation and patent practice in the jurisdiction. Making appropriate additions or subtractions, for example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunications signals.
本申请是参照根据本申请实施例的方法、设备(系统)、和计算机程序产品的流程图和/或方框图来描述的。应理解可由计算机程序指令实现流程图和/或方框图中的每一流程和/或方框、以及流程图和/或方框图中的流程和/或方框的结合。可提供这些计算机程序指令到通用计算机、专用计算机、嵌入式处理机或其他可编程数据处理设备的处理器以产生一个机器，使得通过计算机或其他可编程数据处理设备的处理器执行的指令产生用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的装置。The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each process and/or block in the flowchart illustrations and/or block diagrams, and combinations of processes and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions executed by the processor of the computer or other programmable data processing device produce a use A device for realizing the functions specified in one process or multiple processes of the flowchart and/or one block or multiple blocks of the block diagram.
这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理设备以特定方式工作的计算机可读存储器中，使得存储在该计算机可读存储器中的指令产生包括指令装置的制造品，该指令装置实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能。These computer program instructions may also be stored in a computer-readable memory that causes a computer or other programmable data processing apparatus to operate in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including the instruction means, the instructions The device implements the functions specified in a process or processes of the flowchart and/or a block or blocks of the block diagram.
这些计算机程序指令也可装载到计算机或其他可编程数据处理设备上，使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理，从而在计算机或其他可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的步骤。These computer program instructions may also be loaded onto a computer or other programmable data processing device, causing a series of operating steps to be performed on the computer or other programmable device to produce computer-implemented processing, thereby executing on the computer or other programmable device. Instructions provide steps for implementing the functions specified in a process or processes of a flowchart diagram and/or a block or blocks of a block diagram.
以上内容仅为说明本发明的技术思想，不能以此限定本发明的保护范围，凡是按照本发明提出的技术思想，在技术方案基础上所做的任何改动，均落入本发明权利要求书的保护范围之内。The above contents are only for illustrating the technical ideas of the present invention and cannot be used to limit the protection scope of the present invention. Any changes made based on the technical ideas proposed by the present invention and based on the technical solutions shall fall within the scope of the claims of the present invention. within the scope of protection.
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311028215.XACN117040352A (en) | 2023-08-15 | 2023-08-15 | PMLSM motor thrust fluctuation suppression method, system, chip and equipment |
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311028215.XACN117040352A (en) | 2023-08-15 | 2023-08-15 | PMLSM motor thrust fluctuation suppression method, system, chip and equipment |
| Publication Number | Publication Date |
|---|---|
| CN117040352Atrue CN117040352A (en) | 2023-11-10 |
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311028215.XAPendingCN117040352A (en) | 2023-08-15 | 2023-08-15 | PMLSM motor thrust fluctuation suppression method, system, chip and equipment |
| Country | Link |
|---|---|
| CN (1) | CN117040352A (en) |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118300467A (en)* | 2024-04-09 | 2024-07-05 | 安徽大学 | Current prediction control method based on variable gain dead beat motor and computer equipment |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118300467A (en)* | 2024-04-09 | 2024-07-05 | 安徽大学 | Current prediction control method based on variable gain dead beat motor and computer equipment |
| Publication | Publication Date | Title |
|---|---|---|
| CN106452247B (en) | Method and device for identifying rotational inertia of permanent magnet synchronous motor | |
| CN111835251B (en) | Permanent magnet synchronous motor high-performance control method based on speed-free sensing | |
| CN111342729B (en) | Self-adaptive reverse thrust control method of permanent magnet synchronous motor based on gray wolf optimization | |
| CN117040355A (en) | Synchronous reluctance motor model prediction speed control method, system, chip and equipment | |
| CN114722528A (en) | Induction motor sensorless vector control method based on adaptive extended Kalman filtering | |
| CN117040352A (en) | PMLSM motor thrust fluctuation suppression method, system, chip and equipment | |
| CN114614714A (en) | A stable control method for speed sensorless induction motor in high speed domain | |
| CN116418260A (en) | Torque estimation method, device, equipment and storage medium of permanent magnet synchronous motor | |
| CN116442223A (en) | A Design Method of Nonlinear Dynamic Controller for Trajectory Tracking of Manipulator System | |
| WO2025091782A1 (en) | Low-precision incremental encoder-based method for ultra-low speed control of servo system | |
| CN119921618B (en) | Model-free deadbeat predictive current control method for permanent magnet synchronous motor based on current separation | |
| CN116885991A (en) | Asynchronous motor control method and system | |
| Tian et al. | Speed-sensorless control of induction motors based on adaptive EKF | |
| CN108512476A (en) | A kind of induction machine turn count method based on novel Luenberger observer | |
| CN111313781A (en) | A Model-Free Predictive Current Control Method Based on Hyperlocal Model | |
| CN109617482A (en) | L2 sliding mode control method of permanent magnet synchronous motor | |
| CN110768600A (en) | PMSM speed sensorless rotor detection method | |
| CN118381392A (en) | Permanent magnet synchronous motor control method, device, equipment and computer program product | |
| CN118646308A (en) | A disturbance-resistant underwater joint motor control method and related device | |
| CN114584026B (en) | A method for optimizing the robustness of permanent magnet synchronous motor parameters and related equipment | |
| CN118137907A (en) | Current prediction control method and computer equipment based on sensorless deadbeat motor | |
| CN116191985A (en) | Method and device for determining rotor temperature of permanent magnet synchronous motor and computer equipment | |
| Chongwu et al. | The study on the PMSM sensorless control using the sub-optimal fading extend Kalman filter | |
| CN114679098A (en) | Feedforward compensation method, device, computer equipment and medium for permanent magnet synchronous motor | |
| Li et al. | Vector control of permanent magnet synchronous linear motor based on improved bp neural network |
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |