CN108599656B - Hybrid vehicle switched reluctance BSG position sensorless control system and method - Google Patents

Hybrid vehicle switched reluctance BSG position sensorless control system and method Download PDF

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CN108599656B
CN108599656B CN201810390858.1A CN201810390858A CN108599656B CN 108599656 B CN108599656 B CN 108599656B CN 201810390858 A CN201810390858 A CN 201810390858A CN 108599656 B CN108599656 B CN 108599656B
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psi
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bsg
flux linkage
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CN108599656A (en
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孙晓东
金志佳
陈龙
苏伯凯
杨泽斌
李可
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Jiangsu University
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/14—Estimation or adaptation of machine parameters, e.g. flux, current or voltage
    • H02P21/18—Estimation of position or speed
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/0003—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control
    • H02P21/0017—Model reference adaptation, e.g. MRAS or MRAC, useful for control or parameter estimation
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/24—Vector control not involving the use of rotor position or rotor speed sensors
    • H02P21/26—Rotor flux based control
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P25/00—Arrangements or methods for the control of AC motors characterised by the kind of AC motor or by structural details
    • H02P25/02—Arrangements or methods for the control of AC motors characterised by the kind of AC motor or by structural details characterised by the kind of motor
    • H02P25/08—Reluctance motors
    • H02P25/086—Commutation
    • H02P25/089—Sensorless control
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P2203/00—Indexing scheme relating to controlling arrangements characterised by the means for detecting the position of the rotor
    • H02P2203/03—Determination of the rotor position, e.g. initial rotor position, during standstill or low speed operation

Abstract

The invention discloses a switched reluctance BSG (magnetic reluctance generator) position sensorless control system and method for a hybrid electric vehicle, which aim at phase current i1Angle theta of rotortAnd flux linkage psitAveragely dividing the data into prediction sample data and training sample data, and taking the training sample as an input sample of a least square support vector machine to obtain a prediction model of the least square support vector machine; optimizing a normalized parameter gamma and the width sigma of the radial basis function in the model by adopting an ant colony algorithm to obtain a final model; collecting the instantaneous current i of BSGs0Processing the output current i by a noise-removing modulestAnd the flux linkage is psistWill current istAnd flux linkage psistSubstituting the obtained position angle theta into the final model to obtain a predicted position angle thetastThe BSG is controlled by the rotation speed of the position-free sensor; the purpose of no position control is achieved by adopting finite element analysis and an improved ant colony least square support vector machine-based method, and the stability of control is improved.

Description

Hybrid vehicle switched reluctance BSG position sensorless control system and method
Technical Field
The invention relates to a belt-driven starter generator (hereinafter, referred to as BSG) system for a hybrid vehicle, in particular to a position-sensor-free control system and a position-sensor-free control method for the BSG system, which are suitable for high-performance control of the BSG system and belong to the field of BSG hybrid power control.
Background
The hybrid electric vehicle has the advantages of energy conservation and emission. The BSG is a hybrid power technology with idle stop and start functions, adopts a belt transmission mode to perform power mixing, and can reduce oil consumption and emission when a vehicle works at idle speed. When the automobile normally runs, the BSG has the same working principle as the traditional generator, and the engine drives the generator to generate electricity to charge the battery; the BSG can enable the engine to stop working when the automobile stops; when the vehicle starts again, the BSG system starts the engine quickly, and oil consumption, emission and noise of the engine during idling operation are eliminated. The BSG technology is adopted to stop the automobile when the automobile runs, and meanwhile, the braking energy can be recovered, so that the fuel consumption of the automobile is reduced, and the emission level of the automobile is improved.
In order to control the switch reluctance BSG on angle and off angle of the hybrid vehicle, it is important to accurately determine the position of the rotor. In order to determine the position of the rotor, electromagnetic or optical sensors are generally used in conventional control systems, but these sensors increase the mechanical dimensions of the machine and secondly they reduce the safety of the system and generate noise; as far as the sensor itself is concerned, its sensitivity varies greatly with the temperature variation, so that the stability of the motor control is lowered. To improve the stability of the control, sensorless speed and position control needs to be used.
Disclosure of Invention
The invention aims to provide a high-performance position-sensorless control system and a control method thereof for accurately determining the position of a switched reluctance BSG rotor for an existing hybrid vehicle.
The invention relates to a switched reluctance BSG position sensorless control system of a hybrid vehicle, which adopts the technical scheme that: the device comprises a control module, a power conversion module, a signal measurement module, a noise elimination module, a prediction module and a rotating speed measurement module;
the noise removing module inputs the acquired instantaneous current i of the BSGs0And a feedback phase voltage vs0The output is the flux linkage psistAnd current istThe output end of the denoising module is respectively connected with the prediction module and the control module, and the output end of the prediction module is connected with the control module;
the input of the prediction module is the magnetic linkage psistCurrent istAnd phase current i of BSG1Outputs the predicted position angle thetast；
Measuring instantaneous speed n of BSG by using a speed measuring module, and comparing the instantaneous speed n with a reference speed nrefComparing to obtain a rotating speed error delta e;
saidThe control module inputs the rotating speed error delta e and the predicted rotor position angle thetastAnd current istThe control module outputs a voltage signal V, and the voltage signal V outputs a control voltage V through the power conversion modulesThe output end of the power conversion module is respectively connected with the BSG and the signal measurement module, and the signal measurement module detects the feedback phase voltage vs0。
Furthermore, the prediction module consists of a simulation module, a data processing module, a least square support vector machine model and an ant colony algorithm optimization module;
the simulation module carries out finite element simulation modeling on the BSG to obtain phase current i1Magnetic linkage psi1And rotor angle theta1；
The data processing module is used for aligning the magnetic linkage psi1And rotor angle theta1Processing to obtain rotor angle theta after eliminating interference datatAnd flux linkage psit；
The phase current i1Angle theta of rotortMagnetic linkage psitMagnetic linkage psistCurrent istAre used as the input of least square support vector machine model which outputs the predicted position angle thetast；
The ant colony algorithm optimization module optimizes a regularization parameter gamma and a width sigma of a radial basis function in a least square support vector machine.
The control method of the switched reluctance BSG position sensorless control system of the hybrid electric vehicle adopts the technical scheme that the control method comprises the following steps:
A. finite element simulation modeling is carried out on BSG to obtain phase current i1Magnetic linkage psi1And rotor angle theta1The flux linkage psi obtained1And rotor angle theta1Eliminating interference data in the data processing module;
B. phase current i1Angle theta of rotortAnd flux linkage psitThe data of the method is averagely divided into prediction sample data and training sample data, and the training sample is a least square support vectorInputting a sample by the machine to obtain a prediction model of a least square support vector machine; optimizing a normalized parameter gamma and the width sigma of the radial basis function in the model by adopting an ant colony algorithm to obtain a final model;
C. collecting the instantaneous current i of BSGs0Processing the output current i through a noise removing modulestAnd the flux linkage is psistWill current istAnd the flux linkage is psistSubstituting the obtained position angle into the final model to calculate the predicted position angle thetast。
D. Final control voltage V output by power convertersAnd the rotation speed control without a position sensor is realized for the BSG.
The invention adopts the technical scheme to remarkably have the advantages that:
1. the control method of the invention does not need to use an electromagnetic sensor or an optical sensor, thereby reducing the cost and the mechanical size of the motor, improving the safety performance of the system and reducing the noise.
2. The flux linkage and rotor position data are obtained through finite element simulation, the rotating speed and the position of the motor during operation are predicted by applying the data calculated by the finite elements, the accuracy is high, the reliability of the training data of the support vector machine is improved, the timeliness is high, and the control accuracy and the development efficiency are improved.
3. As far as the sensor itself is concerned, its sensitivity varies greatly with the temperature variation, so that the stability of the motor control is lowered. The invention uses the speed and position control without a sensor, and adopts finite element analysis and a method based on an improved ant colony least square support vector machine to achieve the aim of no position control and improve the stability of control.
Drawings
FIG. 1 is a block diagram of a switched reluctance BSG position sensorless control system of a hybrid electric vehicle according to the present invention;
FIG. 2 is a block diagram of the internal structure of the prediction module of FIG. 1 and its external connection to the BSG and denoising modules;
fig. 3 is a transient current coring process of fig. 2.
Detailed Description
As shown in FIG. 1, the hybrid electric vehicle switch reluctance BSG position sensorless control system of the invention is composed of a control module, a power conversion module, a signal measurement module, a noise elimination module, a prediction module and a rotating speed measurement module.
Measuring instantaneous speed n of BSG by using a speed measuring module, and comparing the instantaneous speed n with a reference speed nrefComparing to obtain a rotation speed error delta e, inputting the rotation speed error delta e into a control module, outputting a voltage signal V by the control module, connecting an output end of the control module with an input end of a power conversion module, inputting the voltage signal V into the power conversion module, and outputting a final control voltage V by the power conversion modulesThe output end of the power conversion module is respectively connected with the BSG and the signal measurement module and controls the voltage VsControl the operation of BSG and control the voltage VsInput into a signal measurement module. The output end of the signal measurement module is connected with the noise elimination module, and the signal measurement module detects the voltage of the final control voltage Vs at a certain moment, namely the feedback phase voltage vs0And applying the feedback phase voltage vs0And inputting the data into a denoising module.
Collecting instantaneous current i of BSG at a certain moments0And apply the instantaneous current is0Input to a noise elimination module which is used for feeding back phase voltage vs0And instantaneous current is0Processing and outputting magnetic linkage psistAnd current ist. The output end of the denoising module is respectively connected with the prediction module and the control module, the output end of the prediction module is also connected with the control module, and the denoising module is used for enabling the magnetic linkage psi to be generatedstAnd current istThe current i is input into a prediction module in commonstInput into the control module.
Finite element modeling is carried out on switch magnetic resistance BSG, and phase current i is acquired1Of phase current i1Input into a prediction module, and the prediction module performs prediction on the input phase current i1Magnetic linkage psistAnd current istProcessing the position to obtain a predicted rotor position angle thetastAnd predicting the rotor position angle thetastInput to the control module. Control module predicts position of rotor for inputAngle thetastCurrent istAnd the rotating speed error delta e are processed to obtain a voltage signal V.
As shown in fig. 2, the prediction module is composed of a simulation module, a data processing module, a least squares support vector machine model, and an ant colony optimization module. The simulation module carries out finite element simulation modeling on the BSG to obtain phase current i1Magnetic linkage psi1And rotor angle theta1. Data processing module for flux linkage psi1And rotor angle theta1Processing the data to eliminate interference data and obtain the rotor angle theta after eliminating the interference datatAnd flux linkage psit. Phase current i1Angle theta of rotortAnd flux linkage psitAnd flux linkage psi output by the noise removing modulestAnd current istThe least square support vector machine model is input together, and the output of the least square support vector machine model is the real-time predicted position angle theta of the rotorst. The ant colony algorithm optimization module optimizes the regularization parameter gamma and the width sigma of the radial basis function in the least square support vector machine to obtain the regularization parameter gamma of the optimal parameter1And the width σ of the radial basis kernel function1Inputting a least squares support vector machine model.
Referring to fig. 1-2, when the BSG position sensorless control system for a hybrid electric vehicle of the present invention operates, a prediction module is first learned, a finite element modeling is first performed on the BSG, and after the size and material of the BSG are determined, electromagnetic distribution and torque under a loaded or unloaded condition are calculated. Acquiring flux linkage data of switched reluctance BSG (switched reluctance generator) of a hybrid electric vehicle at different currents and rotor positions, wherein the rotor positions are calculated once every 1 DEG from a non-aligned position to an aligned position, the acquired data are input into a prediction module, and after finite element simulation of a simulation module, phase current i is obtained1Magnetic linkage psi1And rotor angle theta1Three-dimensional data of relationships.
The flux linkage psi to be obtained subsequently1And rotor angle theta1Input into a data processing module to obtain a phase current i1Inputting a least squares support vector machine model.The data processing module is used for eliminating interference data, because the sampling data frequency is high enough, the difference between adjacent sampling values should be small, if the estimated value of the first-order difference is larger than a certain threshold value and has a larger difference with the sampling value, the sampling value is judged to be an interference value, and the estimated value is used for replacing the sampling value. The method not only improves the accuracy of the data, but also has simple data processing mode and high efficiency. Using a first order difference method for each flux linkage psi1Data vs. rotor angle θ1Data is processed by magnetic linkage psi1For example, the mth flux linkage is predicted by applying equation (1):
Figure BDA0001643406340000041
in the formula (I), the compound is shown in the specification,
Figure BDA0001643406340000042
is an estimate of the mth flux linkage. Predicting the magnetic linkage
Figure BDA0001643406340000043
Flux linkage psi after corresponding finite element analysismAnd comparing to judge whether the interference value is as follows:
Figure BDA0001643406340000044
when the formula (2) is satisfied, the flux linkage psi calculated by finite elementmIf the difference between the estimated value of the first order difference and the estimated value of the first order difference is larger than a threshold epsilon (the threshold epsilon is set according to a certain rule and is usually an integral multiple of the standard deviation), the point data is represented as an interference value, the point data can be eliminated, and the estimated value is used
Figure BDA0001643406340000045
Replacement is performed. The flux linkage after interference elimination is marked as psit. The rotor angle theta is adjusted in the same way1Processing to obtain the rotor position theta after eliminating the interferencet. Magnetic linkage psi after interference eliminationtAnd the rotor position theta after eliminating the interferencetInput to a minimum of twoMultiplying the support vector machine model by the rotor position thetatAnd inputting the data into an ant colony algorithm optimization module.
Phase current i1Angle theta of rotortAnd flux linkage psitThe data of (1) is averagely divided into L groups of prediction sample data and L groups of training sample data. L sets of training samples are (x)1,y1),…,(x2，y2),…，(xL，yL) Is an input sample, where xj＝(i1,ψtj),j＝1.2.3…L，ψtjEliminating the interference jth training sample flux linkage; y isj＝θtj，θtjIn order to eliminate the j training sample rotor position after interference, in the feature space, the least square support vector machine adopts the following function:
f(x)＝ωTφ(x)+b (3)
where ω is the weight vector, T is the transpose, b is the offset, and Φ is a nonlinear mapping. Defining:
Figure BDA0001643406340000046
yj＝ωTφ(xj)+b+τj(5)
where J is the optimization objective function, γ is the regularization parameter, τjIs a relaxation factor.
The lagrange function is constructed as follows:
Figure BDA0001643406340000051
wherein a isjIs a lagrange multiplier.
The conditions were optimized according to KKT (Karush-Kuhn-Tucker) to give:
Figure BDA0001643406340000052
Figure BDA0001643406340000053
Figure BDA0001643406340000054
Figure BDA0001643406340000055
elimination of ω and τjSolving the optimization problem can then be converted to solving the following system of linear equations:
Figure BDA0001643406340000056
wherein A ═ a1a2… aL]T,Y＝[y1y2… yL]T，IL*1＝[1 1 1… 1]TI ═ diag {1,1,1, …,1}, k ═ 1,2,3, …, L. Taking a kernel function:
Figure BDA0001643406340000057
where K is the kernel function and σ is the width of the radial basis kernel function.
The final decision function to obtain the least squares support vector machine regression is:
Figure BDA0001643406340000058
wherein a isjAnd b can be solved by formula (11).
I.e. the prediction model of the initial rotor position can be given as:
Figure BDA0001643406340000059
i, psi is the current variable of the actual input and the flux linkage variable of the actual input.
And after a prediction model of the initial rotor position is obtained, performing parameter optimization on the initial rotor position by adopting an ant colony algorithm, and optimizing a normalized parameter gamma and the width sigma of the radial basis kernel function. During optimization, firstly, an optimization target is established:
Figure BDA0001643406340000061
min Q represents the minimum value of the objective function, θt2jTo predict the output of sample data, θojRepresenting the output quantity when the input quantity of prediction sample data is input into the model.
Taking ant number as 30, pheromone residual degree as 0.7, and cycle number as 500. Secondly, setting the initial positions of ants, wherein each position corresponds to a group of parameters (gamma, sigma) of a least square support vector machine, calculating the fitness value of each ant by a defined objective function, and then obtaining the fitness value of each ant by the aid of delta epsilon-e-Q(γ，σ)And calculating the pheromone concentration of each ant. Where Δ ε represents the initial pheromone concentration for each ant.
Randomly drawing 25 ants from the population, and finding out the position of the optimal ant according to the pheromone concentration of the position of each ant, wherein the position is set as XbestIt is taken as a target individual Xobj. And carrying out global search on the non-optimal ants in the population according to the movement to the target ant position. And ants in the optimal solution search in the adjacent area according to the following formula. After one circulation, each ant position moves to the optimal ant position, and meanwhile, the pheromone concentration epsilon (j +1) of each moved ant is updated to be (1-rho) epsilon (j) + delta epsilon, wherein rho is the pheromone volatilization number, epsilon (j +1) is the pheromone concentration after updating, and epsilon (j) is the pheromone concentration before updating. And storing the optimal ant position at the most concentrated pheromone position.
In order to prevent the optimal solution searched by the ant colony algorithm from being only a local optimal solution, the traditional ant colony algorithm is improved. The optimal ant positions are saved after each pheromone concentration updating, and in order to prevent the optimal positions from being only local optimal positions at the moment and not global optimal positions, the positions of non-optimal ants are reset in a global range with a probability of fifty percent after each pheromone concentration updating. And updating the pheromone concentrations of all ants in the next iteration, and setting the position where the pheromone is most concentrated as the optimal ant position again. The method is simple and easy to implement, and the optimal solution has higher convergence rate and higher precision.
Judging whether the iteration number is 500 or the target function formula (11) is less than 0.001, if so, ending the iteration and outputting the optimal parameter (gamma)1，σ1) And substituting the formula (11) to calculate the optimized offset b' and the optimized Lagrange multiplier aj' then the final model of rotor position is:
Figure BDA0001643406340000062
learning of the prediction module is accomplished as described above.
After the learning of the prediction module is completed, collecting the instantaneous current i of the BSG at a certain moments0And applying an instantaneous current i at a certain moments0And processing by a denoising module. The processing method is shown in fig. 3, and the specific principle and flow are as follows:
phase current i obtained through finite element simulation and processed flux linkage psitThe following relationships exist:
{minψt，maxψt}＝P(i) (17)
p (i) represents the relationship between the phase current and the magnetic chain, min ψtIndicating the flux linkage psitMinimum value, max ψtIndicating the flux linkage psitThe maximum value, and the flux linkage in the actual control process is obtained by the following formula (18).
ψs＝∫(vs-Ris)dt (18)
Wherein psisFor the flux linkage calculated on-line, R is the phase resistance, vs0And isRespectively, the feedback phase voltage and the feedback phase current measured on line.
Instantaneous current i at a certain moments0Substituting equation (17) can obtain the theoretical range { min psi ] of flux linkage at a certain time0，maxψ0Feeding back the phase voltage v at a certain moment output by the signal measurement modules0And a time instant current is0Substituting (18) can obtain the online calculation of the magnetic linkage psis0If atFlux linkage psi of line calculations0In the theoretical range of flux linkage min psi0，maxψ0In represents the feedback phase current isWhen the voltage is normal, the denoising module outputs the phase current i at the moments0With magnetic linkage psis0. If the flux linkage psi is calculated on lines0Not in the theoretical range of flux linkage min psi0，maxψ0Within the unit, to reduce the error, the feedback current i at the previous time is outputl0With magnetic linkage psil0The final output current is istThe final output flux linkage is psist。
The final output current is istThe final output flux linkage is psistThe real-time rotor predicted position angle theta can be obtained by substituting the formula (16)st。
Predicted rotor position angle thetastCurrent i after noise removalstAnd the rotating speed error delta e is input into a control module, the control module obtains a control voltage signal V of the switched reluctance BSG by taking D2P as a carrier, the control voltage signal V is input into a power converter to realize the rotating speed control of the motor without a position sensor, and finally, the final control voltage V output by the power convertersAnd controlling the switched reluctance BSG of the hybrid electric vehicle.

Claims (3)

1. A switched reluctance BSG position sensorless control system of a hybrid electric vehicle is characterized in that: the device comprises a control module, a power conversion module, a signal measurement module, a noise elimination module, a prediction module and a rotating speed measurement module;
the noise removing module inputs the acquired instantaneous current i of the BSGs0And a feedback phase voltage vs0The output is the flux linkage psistAnd current istThe output end of the denoising module is respectively connected with the prediction module and the control module, and the output end of the prediction module is connected with the control module;
the input of the prediction module is the magnetic linkage psistCurrent istAnd phase current i of BSG1Outputs the predicted position angle thetast；
Measuring BSG instantaneous speed by using speed measuring modulen, the instantaneous speed n is compared with a reference speed nrefComparing to obtain a rotating speed error delta e;
the control module inputs a rotating speed error delta e and a rotor predicted position angle thetastAnd current istThe control module outputs a voltage signal V, and the voltage signal V outputs a control voltage V through the power conversion modulesThe output end of the power conversion module is respectively connected with the BSG and the signal measurement module, and the signal measurement module detects the feedback phase voltage vs0；
The prediction module consists of a simulation module, a data processing module, a least square support vector machine model and an ant colony algorithm optimization module;
the simulation module carries out finite element simulation modeling on the BSG to obtain phase current i1Magnetic linkage psi1And rotor angle theta1；
The data processing module is used for aligning the magnetic linkage psi1And rotor angle theta1Processing to obtain rotor angle theta after eliminating interference datatAnd flux linkage psit；
The phase current i1Angle theta of rotortMagnetic linkage psitMagnetic linkage psistCurrent istAre used as the input of least square support vector machine model which outputs the predicted position angle thetast；
The ant colony algorithm optimization module optimizes a regularization parameter gamma and a width sigma of a radial basis function in a least square support vector machine.
2. A control method of a switched reluctance BSG position sensorless control system for a hybrid vehicle according to claim 1, comprising the steps of:
A. finite element simulation modeling is carried out on BSG to obtain phase current i1Magnetic linkage psi1And rotor angle theta1The flux linkage psi obtained1And rotor angle theta1Eliminating interference data in the data processing module;
B. phase current i1Rotor, and method of manufacturing the sameAngle thetatAnd flux linkage psitThe data of the method is averagely divided into prediction sample data and training sample data, the training sample is an input sample of a least square support vector machine, and a prediction model of the least square support vector machine is obtained; optimizing a normalized parameter gamma and the width sigma of the radial basis function in the model by adopting an ant colony algorithm to obtain a final model; the least square support vector machine prediction model is
Figure FDA0002117987060000011
i. Psi is the current variable of actual input and flux linkage variable of actual input; l is the number of training sample sets, j is 1.2.3 … L; a isjIs a lagrange multiplier; k is a kernel function; psitjEliminating the interference jth training sample flux linkage; b is an offset;
the final model of the least squares support vector machine is:
Figure FDA0002117987060000021
b' is the offset after optimization; b' aj' is an optimized Lagrange multiplier;
C. collecting the instantaneous current i of BSGs0Processing the output current i through a noise removing modulestAnd the flux linkage is psistWill current istAnd flux linkage psistSubstituting the obtained position angle into the final model to calculate the predicted position angle thetast(ii) a Instantaneous current is0And a feedback phase voltage vs0Warp formula { min psit，maxψtP (i) calculating the theoretical range of flux linkage { min ψ0，maxψ0Phi, phi-phis＝∫(vs-Ris) dt calculation to obtain the calculated flux linkage psis0If the flux linkage psi is on-lines0In the theoretical range of flux linkage min psi0，maxψ0In the previous step, the denoising module outputs the current and the magnetic flux linkage at the current time, otherwise, the current and the magnetic flux linkage at the previous time are output; p (i) is the relationship between phase current and magnetic chain, max ψt、minψtRespectively a flux linkage psitMaximum and minimum values of;
D. power ofConverter output final control voltage VsAnd the rotation speed control without a position sensor is realized for the BSG.
3. The control method according to claim 2, wherein: when the ant colony algorithm is adopted to optimize the normalization parameter gamma and the width sigma of the radial basis function, the initial positions of the ants are set, and each position corresponds to a group of parameters (gamma and sigma).
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