技术领域Technical Field
本发明涉及电学领域，更具体地，涉及一种感应电机超局部模型变矢量序列无模型预测电流控制方法。The present invention relates to the field of electricity, and more specifically, to a model-free predictive current control method for an induction motor super-local model variable vector sequence.
背景技术Background Art
模型预测控制(model predictive Control，MPC)方法是近年来受到广大学者关注的功率控制器开关序列生成方法，其目标是从功率控制器的所有开关状态集合中选择最适合的开关状态。相比于传统的异步不对称脉宽调制方法和同步最优脉宽调制策略，模型预测控制具有更优的控制效果和动态特性。但是由于温度和磁饱和的影响，电机的参数会发生变化。而预测和控制过程中需要使用大量的电机参数，MPC对电机参数的准确性有着高度的依赖性，鲁棒性较差。且传统模型预测控制在整个控制周期只作用一个电压矢量时，电流脉动较大，限制了其在高性能电机控制中的应用。The model predictive control (MPC) method is a power controller switching sequence generation method that has attracted the attention of many scholars in recent years. Its goal is to select the most suitable switching state from all the switching state sets of the power controller. Compared with the traditional asynchronous asymmetric pulse width modulation method and the synchronous optimal pulse width modulation strategy, model predictive control has better control effect and dynamic characteristics. However, due to the influence of temperature and magnetic saturation, the parameters of the motor will change. A large number of motor parameters are required in the prediction and control process. MPC is highly dependent on the accuracy of motor parameters and has poor robustness. In addition, when the traditional model predictive control only acts on one voltage vector in the entire control cycle, the current pulsation is large, which limits its application in high-performance motor control.
为提高模型预测控制的参数鲁棒性，现有文献提出了多种方法。文献《Predictive-Control-Based Direct Power Control With an Adaptive ParameterIdentification Technique for Improved AFE Performance》介绍了一种基于最小二乘法的参数在线辨识方法，利用采样的输入电流和输入电压，在每个采样周期内计算AFE的输入电感和输入电阻。文献《Design and Implementation of Disturbance Compensation-Based Enhanced Robust Finite Control Set Predictive Torque Control forInduction Motor Systems》利用扰动观测器观测出由外部环境影响和参数失配等因素造成的扰动量，再对选择出的电压矢量应用前馈环节进行补偿，削弱了参数变化对系统的影响。但是，参数在线辨识和采用扰动观测器的方法提高了参数鲁棒性，但上述方法在实现过程中仍基于系统模型，而且实现过程稍显复杂。为了进一步解决参数依赖问题，Linet等人提出了一种无模型预测电流控制方法，它不需要任何电机参数，在永磁同步电机方面取得了良好的效果。但这种方法需要在一个控制周期内进行两次电流采样，增加了系统实现的复杂度。In order to improve the parameter robustness of model predictive control, various methods have been proposed in existing literature. The paper "Predictive-Control-Based Direct Power Control With an Adaptive Parameter Identification Technique for Improved AFE Performance" introduces a parameter online identification method based on the least squares method, which uses the sampled input current and input voltage to calculate the input inductance and input resistance of the AFE in each sampling cycle. The paper "Design and Implementation of Disturbance Compensation-Based Enhanced Robust Finite Control Set Predictive Torque Control for Induction Motor Systems" uses a disturbance observer to observe the disturbance caused by factors such as external environmental influences and parameter mismatch, and then applies a feedforward link to the selected voltage vector to compensate, thereby weakening the impact of parameter changes on the system. However, the online identification of parameters and the use of disturbance observers improve parameter robustness, but the above methods are still based on the system model in the implementation process, and the implementation process is slightly complicated. In order to further solve the parameter dependence problem, Linet et al. proposed a model-free predictive current control method, which does not require any motor parameters and has achieved good results in permanent magnet synchronous motors. However, this method requires two current samplings in one control cycle, which increases the complexity of system implementation.
为提高模型预测控制的稳态性能，文献《Performance Improvement of Model-Predictive Current Control of Permanent Magnet Synchronous Motor Drives》在一个控制周期内，将零矢量与从传统MPCC获得的电压矢量一起应用，通过获得最优占空比改善了系统的稳态性能。但是该方法应用了大量电机参数，鲁棒性效果相比无模型预测控制较差。文献《基于空间矢量调制的感应电机无速度传感器模型预测磁链控制》引入了无差拍控制，但该方法采用基于基础7段式矢量序列的单矢量作用整个控制周期，在高调制比时电流谐波相对较大。文献《感应电机三矢量模型预测磁链控制》提出了在一个控制周期内使用两个有效电压矢量以及一个零矢量类似于SVM的矢量选择，但是也带来了矢量选择复杂、计算量较大及开关频率高等问题。In order to improve the steady-state performance of model predictive control, the paper "Performance Improvement of Model-Predictive Current Control of Permanent Magnet Synchronous Motor Drives" applies the zero vector together with the voltage vector obtained from the traditional MPCC in one control cycle, and improves the steady-state performance of the system by obtaining the optimal duty cycle. However, this method applies a large number of motor parameters, and the robustness effect is poorer than that of model-free predictive control. The paper "Speed Sensorless Model Predictive Flux Control of Induction Motor Based on Space Vector Modulation" introduces zero-beat control, but this method uses a single vector based on the basic 7-segment vector sequence to act on the entire control cycle, and the current harmonics are relatively large at high modulation ratios. The paper "Three-vector Model Predictive Flux Control of Induction Motor" proposes a vector selection similar to SVM using two effective voltage vectors and one zero vector in one control cycle, but it also brings problems such as complex vector selection, large amount of calculation and high switching frequency.
综上，目前尚没有较好的方法能够同时满足：(1)具备良好的鲁棒性；(2)在全调制比具有良好的稳态性能，较低的电流谐波；(3)方法实现简单，矢量选择方式易于理解，不需要复杂的计算；调制方式易于和其他控制方法相统一，易于在统一的控制程序框架下实现不同的控制模式。In summary, there is currently no good method that can simultaneously meet the following requirements: (1) good robustness; (2) good steady-state performance at full modulation ratio and low current harmonics; (3) the method is simple to implement, the vector selection method is easy to understand and does not require complex calculations; the modulation method is easy to unify with other control methods, and it is easy to implement different control modes under a unified control program framework.
发明内容Summary of the invention
本申请提供了一种能够显著提升控制效果，具有优秀的鲁棒性，同时在全调制比内显著降低电流谐波的方法。在解析推导基于4种电压矢量序列的空间矢量调制的电流谐波基础上，本发明提供了一种基于超局部模型的变矢量序列感应电机无模型预测电流控制方法。The present application provides a method that can significantly improve the control effect, has excellent robustness, and significantly reduces current harmonics within the full modulation ratio. Based on the analytical derivation of current harmonics based on space vector modulation of four voltage vector sequences, the present invention provides a model-free predictive current control method for variable vector sequence induction motor based on a super local model.
本申请提供了一种感应电机超局部模型变矢量序列无模型预测电流控制方法，包括如下步骤：The present application provides a model-free predictive current control method for an induction motor super-local model variable vector sequence, comprising the following steps:
步骤A:根据电机数学模型，得到定子电流的状态方程表达式。考虑一拍延迟补偿，得到新的定子电流状态方程。Step A: According to the motor mathematical model, the state equation expression of the stator current is obtained. Considering one-beat delay compensation, a new stator current state equation is obtained.
步骤B:将超局部模型的原理与电机定子电流的状态方程结合起来，得到关于定子电流的超局部模型方程，并根据输入的电流信息、输出的电压信息在线估计系统参数F和α；同时为了保持开关频率的一致性，序列012的脉宽调制周期是另外三种序列(0127、0121、1012)的2/3倍；Step B: Combine the principle of the hyperlocal model with the state equation of the motor stator current to obtain the hyperlocal model equation about the stator current, and estimate the system parameters F and α online according to the input current information and the output voltage information; at the same time, in order to maintain the consistency of the switching frequency, the pulse width modulation period of the sequence 012 is 2/3 times that of the other three sequences (0127, 0121, 1012);
步骤C：根据上一步骤在线估计得到的F和α，考虑一拍延迟补偿，根据无差拍原理以及关于定子电流的超局部模型来计算电压矢量参考值；Step C: Based on F and α estimated online in the previous step, the voltage vector reference value is calculated according to the deadbeat principle and the hyperlocal model of the stator current, taking into account one-beat delay compensation;
步骤D：根据电流谐波有效值的计算公式，得到四种不同电压矢量序列(0127、0121、012、1012)在一个脉宽调制(PWM)周期的电流谐波有效值的标幺值；将待选矢量序列由一个扩展到三个，根据调制比M选择使电流谐波最小的矢量序列，从而降低电流谐波。Step D: According to the calculation formula of the effective value of current harmonics, the per-unit values of the effective values of current harmonics of four different voltage vector sequences (0127, 0121, 012, 1012) in one pulse width modulation (PWM) cycle are obtained; the vector sequence to be selected is expanded from one to three, and the vector sequence that minimizes the current harmonics is selected according to the modulation ratio M, thereby reducing the current harmonics.
在一些实施例中，所述步骤A中的公式如下：In some embodiments, the formula in step A is as follows:
式中，分别为k时刻、k+1时刻定子电流；为k时刻到k+1时刻作用的电压矢量；Rs、Rr分别为定子、转子电阻；Ls、Lr、Lm分别为定子电感、转子电感、定子转子之间的互感；ψr为转子磁链；ωr为电机转速；Tr＝Lr/Rr，考虑一拍延迟补偿，可得：In the formula, They are the stator current at time k and time k+1 respectively; is the voltage vector acting from time k to time k+1;Rs andRr are the stator and rotor resistances respectively;Ls ,Lr andLm are the stator inductance, rotor inductance and mutual inductance between stator and rotor respectively;ψr is the rotor flux;ωr is the motor speed;Tr =Lr /Rr , Considering one-beat delay compensation, we can get:
式中，为k+2时刻定子电流；为k+1时刻到k+2时刻作用的电压矢量；In the formula, is the stator current at time k+2; is the voltage vector acting from time k+1 to time k+2;
在一些实施例中，所述步骤B包括：In some embodiments, step B comprises:
电机的超局部模型：将此公式与上一步骤提到的定子电流状态方程进行对比，可得F为整个系统的扰动变量，包括系统中所有已知因素引起的扰动和未知条件引入的其他扰动。Hyperlocal model of a motor: Comparing this formula with the stator current state equation mentioned in the previous step, we can get F is the disturbance variable of the entire system, including disturbances caused by all known factors in the system and other disturbances introduced by unknown conditions.
根据过去时刻的电压、电流值估计F和α；Estimate F and α based on the voltage and current values at past moments;
定义电流的变化量：Define the change in current:
由上一步骤提到，序列012与其余三种序列对应的控制周期不同，所以Tsc有不同的上标，具体选择如下公式：As mentioned in the previous step, the control period corresponding to sequence 012 is different from that of the other three sequences, so Tsc has different superscripts. The specific formula is as follows:
根据过去两个时刻的电压电流信息与超局部模型，可以得到F和α估计值：According to the voltage and current information of the past two moments and the hyperlocal model, the estimated values of F and α can be obtained:
其中：和分别为k-1时刻的电流值、在k-1到k时刻的平均电压矢量和控制周期；和分别为k-2时刻的电流值、在k-2到k-1时刻的平均电压矢量和控制周期；为k时刻的电流值。in: and are the current value at time k-1, the average voltage vector from time k-1 to time k, and the control period; and They are the current value at time k-2, the average voltage vector from time k-2 to time k-1, and the control period; is the current value at time k.
在一些实施例中，所述步骤C包括：In some embodiments, step C comprises:
在实际数字控制系统中，输出电压与指令电压之间会存在一拍延迟，为了消除一拍延迟影响，对k时刻电流值进行补偿，k+1时刻的电流预测值：In actual digital control systems, there is a one-beat delay between the output voltage and the command voltage. In order to eliminate the influence of the one-beat delay, the current value at time k is compensated, and the current prediction value at time k+1 is:
根据无差拍原理计算参考电压矢量：Calculate the reference voltage vector according to the deadbeat principle:
在一些实施例中，所述步骤D包括：In some embodiments, step D comprises:
计算调制比M，并根据步骤A中的电流谐波表达式选择使电流谐波最小的矢量序列：Calculate the modulation ratio M and select the vector sequence that minimizes the current harmonics according to the current harmonic expression in step A:
根据调制比M计算四种序列电流谐波有效值的标幺值Calculate the per unit value of the four sequence current harmonics based on the modulation ratio M
为序列0127、012、0121、1012对应的电流谐波有效值的标幺值；M为调制比；π为数学符号，其值为3.1415926。对比电流谐波有效值之后，剔除1012序列。 is the per unit value of the effective value of the current harmonics corresponding to the sequences 0127, 012, 0121, and 1012; M is the modulation ratio; π is the mathematical symbol, and its value is 3.1415926. After comparing the effective values of the current harmonics, the 1012 sequence is eliminated.
根据选择的矢量序列，求取三相占空比：According to the selected vector sequence, the three-phase duty cycle is obtained:
其中，为标准化的三相参考电压，为零序分量；in, is the standardized three-phase reference voltage, is the zero-sequence component;
通过以上步骤获取的矢量序列和三相占空比得到逆变器每个开关管的信号。The vector sequence and three-phase duty cycle obtained through the above steps are used to obtain the signal of each switch tube of the inverter.
本发明具有如下特点和优势：The present invention has the following characteristics and advantages:
1.基于超局部模型的变矢量序列感应电机无模型预测电流控制，不依赖任何电机参数，具有极强的鲁棒性。1. Model-free predictive current control of variable vector sequence induction motor based on super local model does not depend on any motor parameters and has extremely strong robustness.
2.相对传统无差拍预测电流控制，将备选电压矢量序列由一种扩展为三种，在线选择最优的矢量序列且矢量选择方式易于理解，有效降低了电流谐波，提高了系统的稳态性能。2. Compared with traditional deadbeat predictive current control, the alternative voltage vector sequence is expanded from one to three, the optimal vector sequence is selected online and the vector selection method is easy to understand, which effectively reduces current harmonics and improves the steady-state performance of the system.
3.F与α的值都是在线更新的，无需任何先验知识，开关频率固定，对采样频率要求不高。3. The values of F and α are updated online without any prior knowledge. The switching frequency is fixed and the sampling frequency requirement is not high.
4.方法实现简单，不需要复杂的计算；调制方式易于和其他控制方法相统一，易于在统一的控制程序框架下实现不同的控制模式。4. The method is simple to implement and does not require complex calculations; the modulation method is easy to unify with other control methods, and it is easy to implement different control modes under a unified control program framework.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
图1是感应电机电机调速控制系统硬件结构图；FIG1 is a hardware structure diagram of an induction motor speed control system;
图2是基于超局部模型的变矢量序列感应电机无模型预测电流控制结构框图；FIG2 is a block diagram of a model-free predictive current control structure of a variable vector sequence induction motor based on a super-local model;
图3是全调制比下四种电压矢量序列在一个基波周期的的电流谐波有效值的标幺值；FIG3 is the per unit value of the effective value of current harmonics of four voltage vector sequences in one fundamental wave cycle under full modulation ratio;
图4是采用准确参数的无差拍预测电流控制，开关频率为5kHz，电机运行在600r/min带额定负载时的实验结果；Figure 4 shows the experimental results of deadbeat predictive current control with accurate parameters, a switching frequency of 5kHz, and a motor running at 600r/min with rated load;
图5是采用全部电机参数扩大3倍的无差拍预测电流控制，开关频率为5kHz，电机运行在600r/min带额定负载时的实验结果；Figure 5 shows the experimental results of deadbeat predictive current control with all motor parameters enlarged 3 times, a switching frequency of 5kHz, and a motor running at 600r/min with rated load;
图6是采用基于超局部模型的变矢量序列感应电机无模型预测电流控制，开关频率为5kHz，电机运行在600r/min带额定负载时的实验结果；Figure 6 shows the experimental results of the variable vector sequence induction motor model-free predictive current control based on the hyperlocal model, with a switching frequency of 5kHz and the motor running at 600r/min with rated load;
图7是采用准确参数的无差拍预测电流控制，开关频率为5kHz，电机运行在1200r/min带额定负载时的实验结果；Figure 7 shows the experimental results of deadbeat predictive current control with accurate parameters, a switching frequency of 5kHz, and a motor running at 1200r/min with rated load;
图8是采用全部电机参数扩大3倍的无差拍预测电流控制，开关频率为5kHz，电机运行在1200r/min带额定负载时的实验结果；FIG8 is an experimental result of using deadbeat predictive current control with all motor parameters enlarged 3 times, a switching frequency of 5 kHz, and a motor running at 1200 r/min with rated load;
图9是采用基于超局部模型的变矢量序列感应电机无模型预测电流控制，开关频率为5kHz，电机运行在1200r/min带额定负载时的实验结果；Figure 9 shows the experimental results of the variable vector sequence induction motor model-free predictive current control based on the super-local model, with a switching frequency of 5kHz and the motor running at 1200r/min with rated load;
图10是采用准确参数的无差拍预测电流控制，开关频率为5kHz，电机运行在1350r/min带额定负载时的实验结果；Figure 10 shows the experimental results of deadbeat predictive current control with accurate parameters, a switching frequency of 5kHz, and a motor running at 1350r/min with rated load;
图11是采用全部电机参数扩大3倍的无差拍预测电流控制，开关频率为5kHz，电机运行在1350r/min带额定负载时的实验结果；FIG11 is an experimental result of deadbeat predictive current control with all motor parameters enlarged 3 times, a switching frequency of 5 kHz, and a motor running at 1350 r/min with rated load;
图12是基于超局部模型的变矢量序列感应电机无模型预测电流控制，开关频率为5kHz，电机运行在1350r/min带额定负载时的实验结果；Figure 12 is the experimental results of model-free predictive current control of the variable vector sequence induction motor based on the super-local model, with the switching frequency of 5kHz and the motor running at 1350r/min with rated load;
图13是采用准确参数的无差拍预测电流控制，开关频率为5kHz，电机运行在1500r/min带额定负载时的实验结果；FIG13 is an experimental result of using deadbeat predictive current control with accurate parameters, a switching frequency of 5 kHz, and a motor running at 1500 r/min with rated load;
图14是采用全部电机参数扩大3倍的无差拍预测电流控制，开关频率为5kHz，电机运行在1500r/min带额定负载时的实验结果；FIG14 is an experimental result of using deadbeat predictive current control with all motor parameters enlarged 3 times, a switching frequency of 5 kHz, and a motor running at 1500 r/min with rated load;
图15是基于超局部模型的变矢量序列感应电机无模型预测电流控制，开关频率为5kHz，电机运行在1500r/min带额定负载时的实验结果；Figure 15 is the experimental results of the model-free predictive current control of the variable vector sequence induction motor based on the super-local model, with the switching frequency of 5kHz and the motor running at 1500r/min with rated load;
图16是准确参数的无差拍预测电流控制、全部电机参数扩大3倍的无差拍预测电流控制和基于超局部模型的变矢量序列感应电机无模型预测电流控制在不同平均调制比下的电流总谐波畸变(THD)对比图；FIG16 is a comparison diagram of the total harmonic distortion (THD) of current under different average modulation ratios of the deadbeat predictive current control with accurate parameters, the deadbeat predictive current control with all motor parameters expanded by 3 times, and the variable vector sequence induction motor model-free predictive current control based on the super-local model;
图17是采用准确参数的无差拍预测电流控制，开关频率为5kHz，电机从0直接启动到1500r/min的实验结果；Figure 17 shows the experimental results of using deadbeat predictive current control with accurate parameters, a switching frequency of 5kHz, and a motor started directly from 0 to 1500r/min;
图18是采用全部电机参数扩大3倍的无差拍预测电流控制，开关频率为5kHz，电机从0直接启动到1500r/min的实验结果的实验结果；FIG18 is an experimental result of the deadbeat predictive current control with all motor parameters enlarged 3 times, the switching frequency is 5kHz, and the motor is started directly from 0 to 1500r/min;
图19是基于超局部模型的变矢量序列感应电机无模型预测电流控制，开关频率为5kHz，电机从0直接启动到1500r/min的实验结果的实验结果；Figure 19 is the experimental results of the model-free predictive current control of the induction motor with variable vector sequence based on the super-local model, the switching frequency is 5kHz, and the motor is started directly from 0 to 1500r/min;
图20是采用准确参数的无差拍预测电流控制，开关频率为5kHz，电机突加额定负载的实验结果；FIG20 is an experimental result of using deadbeat predictive current control with accurate parameters, a switching frequency of 5 kHz, and a sudden addition of rated load to the motor;
图21是采用全部电机参数扩大3倍的无差拍预测电流控制，开关频率为5kHz，电机突加额定负载的实验结果；FIG21 is an experimental result of using deadbeat predictive current control with all motor parameters expanded 3 times, a switching frequency of 5 kHz, and a sudden addition of rated load to the motor;
图22是基于超局部模型的变矢量序列感应电机无模型预测电流控制，开关频率为5kHz，电机突加额定负载的实验结果。FIG22 shows the experimental results of model-free predictive current control of an induction motor with variable vector sequence based on a super-local model, with a switching frequency of 5 kHz and a sudden increase in rated load on the motor.
具体实施方式DETAILED DESCRIPTION
下面的实施例可以使本领域技术人员更全面地理解本发明，但不以任何方式限制本发明。The following examples may enable those skilled in the art to more fully understand the present invention, but are not intended to limit the present invention in any way.
本发明所采用的技术方案如下：The technical solution adopted by the present invention is as follows:
基于超局部模型的变矢量序列感应电机无模型预测电流控制方法，其步骤包括：A model-free predictive current control method for a variable vector sequence induction motor based on a super local model comprises the following steps:
步骤1：整个系统采用串联控制结构，通过速度外环比例积分(PI)调节器得到q轴电流参考值，d轴电流参考值设为额定值。Step 1: The entire system adopts a series control structure, and the q-axis current reference value is obtained through the speed outer loop proportional integral (PI) regulator , the d-axis current reference value is set to the rated value.
步骤2：通过q轴电流参考值和d轴电流参考值得到转差ωsl，而后得到电角速度ωe，经积分得到磁场位置角θe。Step 2: Obtain the slip ωsl through the q-axis current reference value and the d-axis current reference value, and then obtain the electrical angular velocity ωe , and obtain the magnetic field position angle θe through integration.
步骤3：根据步骤1得到的q轴电流参考值给定的d轴电流参考值步骤2得到的磁场位置角θe应用坐标变换得到静止坐标系下的电流矢量参考值Step 3: Based on the q-axis current reference value obtained in step 1 Given d-axis current reference value The magnetic field position angle θe obtained in step 2is applied to coordinate transformation to obtain the current vector reference value in the stationary coordinate system.
步骤4：根据一阶超局部模型以及过去两个时刻的电压矢量、电流矢量和控制周期来估计出无模型控制参数F和α。Step 4: Estimate the model-free control parameters F and α based on the first-order hyperlocal model and the voltage vector, current vector and control period of the past two moments.
步骤5：根据当前时刻的电流矢量步骤4估计的F和α进行一拍延迟补偿，求得k+1时刻的电流矢量Step 5: According to the current vector at the current moment Step 4: The estimated F and α are subjected to one-beat delay compensation to obtain the current vector at time k+1.
步骤6：根据步骤3得到的电流矢量参考值步骤4得到的F和α、步骤5得到的k+1时刻的电流矢量结合电机的超局部模型，基于无差拍原理可以求解出空间矢量调制所需的参考电压矢量Step 6: Current vector reference value obtained from step 3 F and α obtained in step 4, and the current vector at time k+1 obtained in step 5 Combined with the super-local model of the motor, the reference voltage vector required for space vector modulation can be solved based on the deadbeat principle.
步骤7：根据步骤6中的参考电压矢量求取调制比M。Step 7: Based on the reference voltage vector in step 6 Find the modulation ratio M.
步骤8：结合步骤7，从三种备选的电压矢量序列中选择使电流谐波最小的矢量序列及确定三相占空比da,b,c。Step 8: Combined with step 7, select a vector sequence that minimizes current harmonics from the three candidate voltage vector sequences and determine the three-phase duty ratios da,b,c .
步骤9：根据步骤8选择的矢量序列和三相占空比da,b,c，构建逆变器每个开关管的驱动信号。Step 9: Construct a drive signal for each switch tube of the inverter according to the vector sequence and the three-phase duty cycle da,b,c selected in step 8.
图1为本发明的硬件电路结构图，包括三相电压源、异步电机、三相二极管整流桥、直流侧电容、异步电机、电压电流采样电路、DSP控制器和驱动电路。电压电流采样电路利用电压霍尔传感器和电流霍尔传感器分别采集直流侧电压以及异步电机a、b相电流，采样信号经过信号调理电路后进入数字信号处理(DSP)控制器转换为数字信号。DSP控制器完成本发明所提出方法的运算，输出六路开关脉冲，然后经过驱动电路后得到逆变器的六个开关管的最终驱动信号。FIG1 is a hardware circuit structure diagram of the present invention, including a three-phase voltage source, an asynchronous motor, a three-phase diode rectifier bridge, a DC side capacitor, an asynchronous motor, a voltage and current sampling circuit, a DSP controller and a drive circuit. The voltage and current sampling circuit uses a voltage Hall sensor and a current Hall sensor to respectively collect the DC side voltage and the a and b phase currents of the asynchronous motor. The sampling signals enter the digital signal processing (DSP) controller and are converted into digital signals after passing through the signal conditioning circuit. The DSP controller completes the operation of the method proposed by the present invention, outputs six-way switch pulses, and then obtains the final drive signals of the six switch tubes of the inverter after passing through the drive circuit.
图2为本发明的控制原理框图，该控制方法在图1的DSP控制器上按照如下步骤依次实现：FIG. 2 is a block diagram of the control principle of the present invention. The control method is implemented in the DSP controller of FIG. 1 in accordance with the following steps:
步骤1：通过速度外环PI调节器得到q轴电流参考值具体表示为(kp和ki分别为PI调节器中的比例增益和积分增益)，d轴电流参考值设为额定值；式中，为q轴参考电流；ωr分别为电机参考转速和实际转速；表示积分。Step 1: Get the q-axis current reference value through the speed outer loop PI regulator Specifically expressed as (kp and ki are the proportional gain and integral gain in the PI regulator respectively), the d-axis current reference value is set to the rated value; where, is the q-axis reference current; ωr are the reference speed and actual speed of the motor respectively; Represents integral.
步骤2：通过q轴电流参考值和d轴电流参考值得到转差ωsl，而后得到电角速度ωe，经积分得到磁场位置角θe；Step 2: Obtain the slip ωsl through the q-axis current reference value and the d-axis current reference value, and then obtain the electrical angular velocity ωe , and obtain the magnetic field position angle θe through integration;
步骤3：根据步骤1获取的参考电流指令求出参考电流矢量根据步骤2得到的磁场位置角θe，经过坐标变换将参考电流矢量从同步坐标系转换为静止坐标系下；Step 3: According to the reference current command obtained in step 1 Find the reference current vector According to the magnetic field position angle θe obtained in step 2, the reference current vector is transformed from the synchronous coordinate system to the stationary coordinate system through coordinate transformation;
式中，为同步坐标系下定子电流矢量参考值；为d轴参考电流；为静止坐标系下定子电流矢量参考值；ejθ为角度变换。In the formula, is the stator current vector reference value in the synchronous coordinate system; is the d-axis reference current; is the stator current vector reference value in the stationary coordinate system; ejθ is the angle transformation.
步骤4：估计系统无模型控制参数F和α。Step 4: Estimate the system model-free control parameters F and α.
根据感应电机数学模型，传统无差拍预测电流控制复矢量模型为：According to the mathematical model of induction motor, the traditional deadbeat predictive current control complex vector model is:
式中，分别为k时刻、k+1时刻定子电流；为k时刻到k+1时刻作用的电压矢量；Rs、Rr分别为定子、转子电阻；Ls、Lr、Lm分别为定子电感、转子电感、定子转子之间的互感；ψr为转子磁链；ωr为电机转速；Tr＝Lr/Rr，In the formula, They are the stator current at time k and time k+1 respectively; is the voltage vector acting from time k to time k+1;Rs andRr are the stator and rotor resistances respectively;Ls ,Lr andLm are the stator inductance, rotor inductance and mutual inductance between stator and rotor respectively;ψr is the rotor flux;ωr is the motor speed;Tr =Lr /Rr ,
电机的超局部模型：Hyperlocal model of a motor:
当前时刻的电压时，将过去两个时刻的电压、电流矢量存储起来，可以得到定子电流的变化量为：The voltage at the current moment When the voltage and current vectors of the past two moments are stored, the change in stator current can be obtained as:
其中：和分别为k-1时刻的电流值和控制周期；和分别为k-2时刻的电流值和控制周期；为k时刻的电流值。in: and are the current value and control period at time k-1 respectively; and are the current value and control period at time k-2 respectively; is the current value at time k.
从上式可以看出感应电机的电流预测公式非常复杂且用到了大量电机参数，为简化控制复杂度，采用间接磁场定向控制，依据无模型理论，把整个控制系统看为一个利用复矢量描述的输入输出系统，并将系统内部的参数看作一个黑匣子，即可仅用到无模型控制参数而无需任何被控对象信息。将传统无差拍预测电流控制复矢量模型与超局部模型的方程进行比较，可以将传统无差拍预测电流控制中的写成F，写成α，可以得到无模型的复矢量电机数学模型From the above formula, we can see that the current prediction formula of the induction motor is very complex and uses a large number of motor parameters. In order to simplify the control complexity, indirect field oriented control is adopted. According to the model-free theory, the entire control system is regarded as an input-output system described by complex vectors, and the parameters inside the system are regarded as a black box. Only model-free control parameters are used without any information about the controlled object. By comparing the equations of the traditional deadbeat predictive current control complex vector model with those of the hyperlocal model, we can see that the traditional deadbeat predictive current control Written as F, Written as α, we can get the mathematical model of the model-free complex vector motor
其中：为k时刻到k+1时刻作用的电压矢量，Tsc为控制周期，α为输入变量权重系数，F为整个系统的扰动变量，包括系统中所有已知因素引起的扰动和未知条件引入的其他扰动。in: is the voltage vector acting from time k to time k+1,Tsc is the control period, α is the input variable weight coefficient, and F is the disturbance variable of the entire system, including disturbances caused by all known factors in the system and other disturbances introduced by unknown conditions.
将超局部无模型控制思想想与预测电流控制相结合后，我们就可以计算超局部模型中位置的参数α和FCombining the idea of hyperlocal model-free control with predictive current control, we can calculate the parameters α and F at the location in the hyperlocal model.
其中：和分别为k-1时刻的定子电流矢量值、在k-1到k时刻的平均电压矢量和控制周期；和分别为k-2时刻的电流值、在k-2到k-1时刻的平均电压矢量和控制周期；为k时刻的定子电流矢量值。in: and are the stator current vector value at time k-1, the average voltage vector from time k-1 to time k, and the control period; and They are the current value at time k-2, the average voltage vector from time k-2 to time k-1, and the control period; is the stator current vector value at time k.
步骤5：基于步骤4估计出的F和α，结合电机的超局部模型，对控制系统进行一拍延迟补偿Step 5: Based on the estimated F and α in step 4, combined with the super-local model of the motor, the control system is compensated for one-beat delay
步骤6：基于无差拍原理计算参考电压矢量并求取调制比M。Step 6: Calculate the reference voltage vector based on the deadbeat principle And calculate the modulation ratio M.
其中：Udc为母线电压。Where: Udc is the bus voltage.
步骤7：根据步骤6中的求取的调制比M选择矢量序列并求取三相占空比。其中，序列选取根据以下原则，首先计算电流谐波有效值的标幺值在一个基波周期的表达式，公式如下：Step 7: Select the vector sequence and obtain the three-phase duty cycle according to the modulation ratio M obtained in step 6. The sequence is selected according to the following principles. First, the expression of the per-unit value of the effective value of the current harmonics in one fundamental wave period is calculated. The formula is as follows:
为序列0127、012、0121、1012对应的电流谐波有效值的标幺值；M为调制比；π为数学符号，其值为3.1415926。对比电流谐波有效值之后，剔除1012序列。 is the per unit value of the effective value of the current harmonics corresponding to the sequences 0127, 012, 0121, and 1012; M is the modulation ratio; π is the mathematical symbol, and its value is 3.1415926. After comparing the effective values of the current harmonics, the 1012 sequence is eliminated.
由图3，当M<0.72时，序列0127对应的电流谐波最低，选择序列0127，之后的序列选择也以电流谐波最低为选择标准；当0.72<M<0.92时，选择序列012；当M>0.92时，选择序列0121。不需要将M代入表达式进行繁琐计算，仅仅根据M的范围就可以选择使电流谐波最小的矢量序列，实现简单。根据选择的矢量序列，求取三相占空比From Figure 3, when M<0.72, the current harmonics corresponding to sequence 0127 are the lowest, so sequence 0127 is selected, and the selection of subsequent sequences is also based on the lowest current harmonics; when 0.72<M<0.92, sequence 012 is selected; when M>0.92, sequence 0121 is selected. There is no need to substitute M into the expression for tedious calculations. The vector sequence that minimizes the current harmonics can be selected based on the range of M, which is simple to implement. According to the selected vector sequence, the three-phase duty cycle is obtained.
其中，为标准化的三相参考电压，为零序分量。in, is the standardized three-phase reference voltage, is the zero sequence component.
步骤8：根据步骤7中的矢量序列和三相占空比da,b,c，构建逆变器每个开关管的驱动信号。Step 8: Construct a drive signal for each switch tube of the inverter according to the vector sequence and the three-phase duty cycle da,b,c in step 7.
本发明所提出方法的有效性可以通过对比图4至图15的三种工况的实验结果以及图16的THD对比结果得出。实验结果都是5kHz开关频率，从上到下的波形分别为电机转速、q轴电流、d轴电流、A相电流和电压矢量序列。图中seq为1、2、3时分别代表序列0127、012和0121。图4、图5与图6的平均调制比为0.43，三种方法均选择序列0127，采用误差参数的无差拍预测电流控制稳态效果明显变差。图7、图8和图9的平均调制比为0.78。三种方法选择的电压矢量序列出现差异，无差拍预测电流控制方法为序列0127，基于超局部模型的变矢量序列感应电机无模型预测电流控制方法大部分序列选择为012。从图中可以看出，基于超局部模型的变矢量序列感应电机无模型预测电流控制方法的q轴电流脉动更小，采用误差参数的无差拍预测电流控制q轴电流脉动明显增大。图10、图11和图12的平均调制比为0.86。基于超局部模型的变矢量序列感应电机无模型预测电流控制方法的q轴电流脉动最小，A相电流更为正弦。采用准确参数的无差拍预测电流控制方法的电流THD为4.5911％，采用误差参数的无差拍预测电流控制方法的电流THD为17.3334％，基于超局部模型的变矢量序列感应电机无模型预测电流控制方法的电流THD为3.7432％。同采用准确参数的无差拍预测电流控制方法相比，基于超局部模型的变矢量序列感应电机无模型预测电流控制方法电流THD降低了18.47％，表现出更优异的稳态性能和鲁棒性。图13、图14和图15的平均调制比为0.95。三种方法选择的电压矢量序列出现差异，无差拍预测电流控制方法为序列0127，基于超局部模型的变矢量序列感应电机无模型预测电流控制方法大部分序列选择为0121。采用准确参数的无差拍预测电流控制方法的电流THD为4.8216％，采用误差参数的无差拍预测电流控制方法的电流THD为29.9616％，基于超局部模型的变矢量序列感应电机无模型预测电流控制方法的电流THD为3.6898％。同采用准确参数的无差拍预测电流控制方法相比，基于超局部模型的变矢量序列感应电机无模型预测电流控制方法电流THD降低了23.47％，表现出更优异的稳态性能和鲁棒性。图16为三种方法在不同调制比下的电流THD汇总。经过对比可知，基于超局部模型的变矢量序列感应电机无模型预测电流控制方法不依赖电机参数，鲁棒性好，并且在全调制比下电流THD最小；高调制比时电流THD也比无差拍预测电流控制低。图17、图18和图19给出了三种方法电机从0直接启动到1500r/min的实验波形；图20、图21和图22给出了三种方法电机在额定转速下突加载的实验波形，可以看出基于超局部模型的变矢量序列感应电机无模型预测电流控制方法空载启动迅速，能够和传统无模型预测电流控制具有相同的效果；对于外部突加负载有较强的抗干扰能力，具有优异的鲁棒性，动态性能良好。The effectiveness of the method proposed in the present invention can be obtained by comparing the experimental results of the three working conditions of Figures 4 to 15 and the THD comparison results of Figure 16. The experimental results are all 5kHz switching frequency, and the waveforms from top to bottom are motor speed, q-axis current, d-axis current, A-phase current and voltage vector sequence. In the figure, seq is 1, 2, and 3, respectively, representing sequences 0127, 012, and 0121. The average modulation ratio of Figures 4, 5, and 6 is 0.43. All three methods select sequence 0127, and the steady-state effect of the zero-beat predictive current control using error parameters is significantly deteriorated. The average modulation ratio of Figures 7, 8, and 9 is 0.78. There are differences in the voltage vector sequences selected by the three methods. The zero-beat predictive current control method is sequence 0127, and most of the sequences of the variable vector sequence induction motor model-free predictive current control method based on the super-local model are selected as 012. It can be seen from the figure that the q-axis current pulsation of the model-free predictive current control method for variable vector sequence induction motor based on the super local model is smaller, and the q-axis current pulsation of the deadbeat predictive current control method using error parameters is significantly increased. The average modulation ratio of Figures 10, 11 and 12 is 0.86. The q-axis current pulsation of the model-free predictive current control method for variable vector sequence induction motor based on the super local model is the smallest, and the A phase current is more sinusoidal. The current THD of the deadbeat predictive current control method using accurate parameters is 4.5911%, the current THD of the deadbeat predictive current control method using error parameters is 17.3334%, and the current THD of the model-free predictive current control method for variable vector sequence induction motor based on the super local model is 3.7432%. Compared with the deadbeat predictive current control method using accurate parameters, the current THD of the model-free predictive current control method for variable vector sequence induction motor based on the super local model is reduced by 18.47%, showing better steady-state performance and robustness. The average modulation ratio of Figures 13, 14 and 15 is 0.95. The voltage vector sequences selected by the three methods are different. The deadbeat predictive current control method uses sequence 0127, and most of the sequences selected by the variable vector sequence induction motor model-free predictive current control method based on the super local model are 0121. The current THD of the deadbeat predictive current control method using accurate parameters is 4.8216%, the current THD of the deadbeat predictive current control method using error parameters is 29.9616%, and the current THD of the variable vector sequence induction motor model-free predictive current control method based on the super local model is 3.6898%. Compared with the deadbeat predictive current control method using accurate parameters, the current THD of the variable vector sequence induction motor model-free predictive current control method based on the super local model is reduced by 23.47%, showing better steady-state performance and robustness. Figure 16 is a summary of the current THD of the three methods at different modulation ratios. By comparison, it can be seen that the variable vector sequence induction motor model-free predictive current control method based on the super local model does not rely on motor parameters, has good robustness, and has the lowest current THD at full modulation ratio; the current THD is also lower than the zero-beat predictive current control at high modulation ratio. Figures 17, 18 and 19 show the experimental waveforms of the three methods of motor starting directly from 0 to 1500r/min; Figures 20, 21 and 22 show the experimental waveforms of the three methods of motor sudden loading at rated speed. It can be seen that the variable vector sequence induction motor model-free predictive current control method based on the super local model has a fast no-load start and can have the same effect as the traditional model-free predictive current control; it has strong anti-interference ability for external sudden loads, excellent robustness, and good dynamic performance.
综上可知，同无差拍预测电流控制相比，本发明中所述的方法兼具强鲁棒性和更优异的稳态性能，高调制比下，可降低电流THD最高达23.47％。本发明中所述的方法具有良好的通用性与实用性。In summary, compared with the deadbeat predictive current control, the method described in the present invention has both strong robustness and better steady-state performance, and can reduce the current THD by up to 23.47% at a high modulation ratio. The method described in the present invention has good versatility and practicality.
本申请解决了传统无差拍预测电流控制方案中依赖电机参数，鲁棒性较差以及单一的矢量序列在高速重载等高调制比工况下电流谐波相对较大的问题。本申请根据电机的数学模型，生成感应电机的一阶超局部模型，此模型不用到任何被控对象信息，具有极强的鲁棒性，应用过去时刻的电压电流来在线更新控制参数，同时根据无差拍原理来得到定子电压矢量参考值。本申请计算了在纯感性负载下的三种电压矢量序列的电流谐波有效值的标幺值，在不同调制比时选择电流谐波最小的矢量序列，将传统无差拍控制单一的矢量选择更新到3种矢量序列，在全调制比下降低了电流THD，做到了既不需要任何先验知识来进行电机控制，又能够降低电流THD，算法简单实用，调制方式易于和其他控制方法相统一，易于在统一的控制程序框架下实现不同的控制模式。The present application solves the problem that the traditional deadbeat predictive current control scheme relies on motor parameters, has poor robustness, and has relatively large current harmonics in a single vector sequence under high modulation ratio conditions such as high speed and heavy load. The present application generates a first-order super-local model of an induction motor based on the mathematical model of the motor. This model does not require any information about the controlled object and has extremely strong robustness. It uses the voltage and current at past moments to update the control parameters online, and at the same time obtains the stator voltage vector reference value based on the deadbeat principle. The present application calculates the per-unit value of the effective value of the current harmonics of three voltage vector sequences under pure inductive loads, selects the vector sequence with the smallest current harmonics at different modulation ratios, updates the single vector selection of the traditional deadbeat control to three vector sequences, reduces the current THD under the full modulation ratio, and does not require any prior knowledge to control the motor, but can reduce the current THD. The algorithm is simple and practical, the modulation method is easy to unify with other control methods, and it is easy to implement different control modes under a unified control program framework.
本领域技术人员应理解，以上实施例仅是示例性实施例，在不背离本发明的精神和范围的情况下，可以进行多种变化、替换以及改变。Those skilled in the art should understand that the above embodiments are merely exemplary embodiments and that various changes, substitutions and alterations may be made without departing from the spirit and scope of the present invention.
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| CN202211309764.XACN116073713B (en) | 2022-10-25 | 2022-10-25 | Model-free predictive current control method for variable vector sequence induction motor |
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| CN202211309764.XACN116073713B (en) | 2022-10-25 | 2022-10-25 | Model-free predictive current control method for variable vector sequence induction motor |
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| CN118889920B (en)* | 2024-06-28 | 2025-06-24 | 华北电力大学 | Asynchronous motor symmetrical generalized double-vector model prediction current control method |
| CN119030385A (en)* | 2024-08-15 | 2024-11-26 | 华北电力大学 | A variable vector model predictive control method |
| CN119010685B (en)* | 2024-10-22 | 2025-01-28 | 中国科学院长春光学精密机械与物理研究所 | A deadbeat predictive control method |
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| CN106301127A (en)* | 2016-10-20 | 2017-01-04 | 北方工业大学 | A kind of asynchronous motor prediction flux linkage control method and device |
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| JP3919003B2 (en)* | 2002-09-26 | 2007-05-23 | 本田技研工業株式会社 | DC brushless motor rotor angle detector |
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| CN106301127A (en)* | 2016-10-20 | 2017-01-04 | 北方工业大学 | A kind of asynchronous motor prediction flux linkage control method and device |
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| CN116073713A (en) | 2023-05-05 |
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