CN106357192A - Method and system for lowering torque pulsation of switched reluctance motor by current adaptive control - Google Patents
Method and system for lowering torque pulsation of switched reluctance motor by current adaptive control Download PDFInfo
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- CN106357192A CN106357192A CN201610805072.2A CN201610805072A CN106357192A CN 106357192 A CN106357192 A CN 106357192A CN 201610805072 A CN201610805072 A CN 201610805072A CN 106357192 A CN106357192 A CN 106357192A
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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
- H02P23/00—Arrangements or methods for the control of AC motors characterised by a control method other than vector control
- H02P23/12—Observer control, e.g. using Luenberger observers or Kalman filters
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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
- H02P23/00—Arrangements or methods for the control of AC motors characterised by a control method other than vector control
- H02P23/0004—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
- H02P23/00—Arrangements or methods for the control of AC motors characterised by a control method other than vector control
- H02P23/0004—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control
- H02P23/0018—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control using neural networks
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Abstract
The invention relates to a method and system for lowering torque pulsation of a switched reluctance motor by current adaptive control; the method comprises: preprocessing a deviation: subjecting the torque deviation to nonlinear conversion; solving torque estimated output and adaptive PID (proportion integration differentiation) control coefficient by using double-weight neural network; acquiring current set total current by PID control calculation, and acquiring each phase control current via current allocation; predicting current feed-forward compensation control by finite difference extended Kalman filter, and effectively inhibiting and lowering torque pulsation of the switched reluctance motor by joint action of adaptive PID control and prediction-based current feed-forward compensation control. Current, torque and rotor position sensors are connected with a signal processor, the signal processor executes modules of the method, compensated three-phase reference current is output to control a power converter of the motor via a current hysteresis controller, and torque pulsation of the switched reluctance motor is significantly and effective inhibited.
Description
Technical field
The present invention relates to the motor power of electric automobile drives field, specially a kind of current automatic adaptation controls and reduces switch
The method and system of reluctance motor torque pulsation.
Background technology
Switched reluctance machines (switched reluctance motor, abbreviation srm) both no winding also no permanent magnets, tool
Have that structure is simple, efficiency high, stable and reliable operation, with low cost, easy to maintain many advantages, such as.But, switched reluctance machines
For special double-salient-pole structure, take the power supply mode of switching regulator, electromagnetic property is in nonlinearity and strong coupling, leads to it
During operating, torque pulsation is larger, and the noise being led to by torque pulsation and vibration problem seriously restrict its application and develop, therefore
The research method reducing switched reluctance machines torque pulsation is extremely important.
In order to suppress and reducing the torque pulsation of switched reluctance machines srm, Chinese scholars have carried out many research,
Also achieved with many achievements in research.
Torque partition function method is to propose a kind of control strategy with reference to srm feature, is applied to suppress more and reduces srm torque
Pulsation, this strategy passes through suitable partition function, so that torque is seamlessly transitted during commutation, is then further added by suitably controlling plan
Motor total output torque steady tracking is slightly made to give torque reference.
A kind of offline torque distribution control strategy that another document proposes, and with traditional line style, sinusoidal pattern, Cubic,
Exponential type torque distribution control strategy compares.It is the accuracy improving model, proposes on the basis of linear model
The nonlinear model of torque-current, but do not provide the concrete identification process of unknown parameter.
The document also having proposes electric current distribution on the basis of torque distribution and magnetic linkage distributes control strategy.Both modes
Avoid directly finding the non-linear relation of torque-current, control sensitivity higher, but be only limited to the theoretical research emulating at present.
Research is separately had to introduce iterative learning controller on the basis of traditional torque distribution control strategy, with torque deviation
Little reference current is iterated learn control, output current variable quantity compensates reference current for control targe, and in tradition only
Have 1 and -1 two states hystersis controller on the basis of, devise a subregion with 1,0 and -1 three kind of working condition thin
Change hysteresis current controller, it is torque pulsation inhibited more effective for simulation results show.
Add speed ring in addition with traditional Direct Torque Control, and allow little fluctuating error for Hysteresis control
Shortcoming, proposes Direct Torque pid (proportion integral derivative) and controls to adjust voltage duty cycle, realize
The direct torque of srm.
But, due to the nonlinear characteristic that switched reluctance machines are extremely strong, the above-mentioned existing electricity based on simplification mathematical model
Machine method for controlling torque, is all easily caused violent torque pulsation it is impossible to practical application.
Content of the invention
The purpose of the present invention is that a kind of current automatic adaptation of design controls the method reducing switched reluctance machines torque pulsation, first
Carry out deviation pretreatment, torque deviation is carried out non-linear conversion;Try to achieve the ginseng of self adaptation pid control with two-weight neural network
Number;The current feedforward compensa-tion adopting the prediction of finite difference extended Kalman filter again controls, and improves control system one-step prediction
Ability.Self adaptation pid controls and controls collective effect with the current feedforward compensa-tion based on prediction, effectively suppresses and reduces srm torque
Pulsation.
It is another object of the present invention to control according to a kind of current automatic adaptation of the invention described above reducing switched reluctance machines
The method of torque pulsation, a kind of current automatic adaptation of design controls the system reducing switched reluctance machines torque pulsation, by self adaptation
Pid controls and the current feedforward compensa-tion based on prediction controls and melts and be harmonious, and constitutes the control system of switched reluctance machines.
The current automatic adaptation of present invention design controls the method reducing switched reluctance machines torque pulsation, and key step is such as
Under:
, torque deviation pretreatment
Present invention introduces nonlinear function carries out pretreatment to torque deviation, carry out little error large gain, the little increasing of big error
The pretreatment of benefit.Concrete pretreatment is as follows:
Wherein δ represents the transformation range of feedback deviation, span 0.01td~0.1td, tdFor setting torque, fal represents
Preconditioned functions, sign represents sign function, and that is, δ t is more than zero, and value is 1, and less than zero, value is -1.δ t is to set torque
tdTransient torque t with actual measurementeBetween deviation.α is regulation coefficient scope 0~1.
Self adaptation pid direct torque based on two-weight neural network
- 1 two-weight neural network
Srm is the strongly non-linear system of structure changes, variable element it is difficult to obtain accurate mathematical model.The present invention is led to
Cross two-weight neural network (double weight neural network, dwnn) to pid control (ratio, integration and differential
Control) three parameter on-line tuning, realize srm self adaptation pid control.
Two-weight neural network dwnn includes input layer, hidden layer and output layer.The torque installed on switched reluctance machines
Instantaneous torque t of sensor detectione, instantaneous torque t of previous momente_1And setting electric current idIncrement △ idPrevious moment valueAs the input quantity of dwnn, set torque tdAs the desired value of dwnn, after its study, dwnn predicts that obtaining torque estimates
Meter output toutAnd the proportionality coefficient k that pid controlsp, integral coefficient kiiAnd differential coefficient kd.
Pid controls torque deviation pre-processed results fal (δ t) obtaining according to step i and two-weight neural network dwnn
The proportionality coefficient k obtainingp, integral coefficient kiiAnd differential coefficient kdIt is calculated current setting total current i through pidd, divide through electric current
Join and obtain each phase control electric currentWith
The present invention adds the self-adaptative adjustment of power, and the dwnn after improvement is described as follows:
Wherein h (j) is hidden layer output function, and f (x) is activation primitive, toutTorque estimation output for dwnn, wzjFor
Directional weighting, qzjFor core weights, vjFor exporting weights, 0 < a < 1, m span 1~10, bzjFor self-adaptative adjustment power, e
Bottom for natural logrithm.Hidden layer is z layer, z=1,2,3;Output layer is j layer, j=1,2,3.
In order to obtain the parameter in formula (2), the performance index function is taken to be:
Wherein, △ t1It is defined as the transient torque t surveyingeExport t with two-weight neural network dwnn torque estimationoutBetween
Deviation.
According to gradient descent method, directional weighting wzj, core weights qzj, output weights vjAnd power bzjIncrement iteration
Algorithm is as follows:
η is the learning rate of weights, value 0~1;ηbFor power learning rate, scope 0~1.
Self adaptation pid of -2 two-weight neural networks controls
Self adaptation pid controls the setting electric current of output as follows:
id(k)=id(k-1)+kpxc(1)+kiixc(2)+kdxc(3) (5)
Wherein,
Choose performance index function as follows,
According to gradient descent method, kp,kii,kdIterative algorithm as follows:
ηkp, ηkz, ηkdIt is the learning coefficient of ratio, integration and differential coefficient respectively, span 0~1, all it is taken as 0.2.Its value is obtained by two-weight neural network identification.
It is idIncrement △ idPrevious moment value, approximately take:
Wherein toutFor the torque estimation output of two-weight neural network identification, w1jIt is to be connected to hidden layer from jth output layer
In z=1 layer neuron directional weighting, q1jFor its core weights, b1jAdjust power for it.These parameters pass through formula (4)
Obtain.
Pid is output as setting total current id.Three-phase control electric current is respectively ia *And ib *And ic *, below with b phase for turning off
Phase, c phase is for, as a example opening phase, the electric current of biphase commutation is allocated as follows:
Wherein: partition function:
Wherein θ is motor rotor position angle, θonFor turn-on angle, 10~15 degree of span;θovOverlapping for rotor-position
Angle, 2~5 degree of span.
Current feedforward compensa-tion based on Kalman prediction controls
The current forecasting of -1 finite difference EKF
The current feedforward compensa-tion of the present invention controls, and first passes through finite difference EKF one-step prediction motor
Output current, then by the difference real-Time Compensation reference current of current forecasting value and reference current, makes reference current arrive in error
Before, carry back and make correction, indirectly suppress the torque pulsation of srm, the pre- electric current of finite difference EKF of the present invention
Compensate and include two parts, finite difference extended Kalman filter fdekf and relative error processing module.Switched reluctance machines n
=1,2,3 phases, i.e. n-th voltage u in a, b, c three-phasen, rotor position angle θ and compensate after output three-phase current ia、ib
And icInput for fdekf, the three-phase current i of fdekf predictiona *、ib *And ic *Input relative error processing module, double power simultaneously
Value neutral net dwnn self adaptation pid controls the three-phase current i of outputa *、ib *And ic *Also relative error processing module, phase are inputted
Three-phase current deviation to be compensated e is exported to Error processing moduleia *、eib *And eic *, three-phase current deviation to be compensated eia *、eib *With
eic *Control the three-phase current i of output with dwnn self adaptation pida *、ib *And ic *Sum is the three-phase reference current i after compensatinga、ib
And ic.
The n-th electric current i of switched reluctance machinesn, rotating speed w and rotor position angle θ state equation as follows:
Wherein,ψ is switched reluctance motor flux linkage, and t is the sampling period, and scope is 1~3
Second, value is 1 second.J is to select rotary inertia, teFor transient torque, tlFor load torque, unFor n-th voltage, r is switch magnetic
The winding resistance value of resistance motor.
The present invention adopts the fdekf of technology maturation to realize to the output three-phase current i after compensatinga、ibAnd icNext step pre-
Survey, obtain ia *、ib *And ic *.Resistance r in formula (9), parameter aa and c are to estimate by fdekf to obtain.
The Front feedback control of -2 electric currents
The three-phase current i of fdekf predictiona *And ib *And ic *Control gained three-phase reference current i with dwnn self adaptation pida *With
ib *And ic *, obtaining three-phase current error to be compensated through relative error after processing is eia *And eib *And eic *, for feedforward compensation, obtain
Three-phase reference current i to after compensateaAnd ibAnd ic.
Its relative error processing procedure is as follows:
Three-phase reference current i after feedforward compensationa、ibAnd icAs follows:
Three-phase reference current after feedforward compensation setting as the current hysteresis-band control device of technology maturation is obtained by formula (11)
Definite value, the output of current hysteresis-band control device, through power inverter driving switch reluctance motor, effectively suppresses switched reluctance machines
Torque pulsation.
The present invention controls, according to a kind of above-mentioned current automatic adaptation, the method reducing switched reluctance machines torque pulsation, designs one
Plant current automatic adaptation and control the system reducing switched reluctance machines torque pulsation, including signal processor, analog-to-digital conversion module, electricity
Stream hystersis controller, power inverter, three-phase current sensor, torque sensor and rotor-position sensor.
Three current sensors are respectively arranged in the three-phase power line of switched reluctance machines, detect each phase current, torque passes
Sensor is installed on the output shaft of switched reluctance machines, the output torque of detection motor, and rotor-position sensor is installed on switch magnetic
The rotor of resistance motor, detects rotor position angle.Position sensor, torque sensor and current sensor are through analog-to-digital conversion module even
Connect signal processor.
Signal processor contains torque deviation pretreatment module, the pid self-adaptive control module of two-weight neural network and
Current feedforward compensa-tion module based on limited spread Kalman prediction.
Torque deviation pretreatment module is to setting torque tdInstantaneous torque t with torque sensor detectioneTorque deviation enter
Row nonlinear function pretreatment, its result fal (δ t) is as the input of pid self-adaptive control module.
Two-weight neural network dwnn in the pid self-adaptive control module of two-weight neural network is examined with torque sensor
Instantaneous torque t surveyede, instantaneous torque t of previous momente_1And setting electric current idIncrement △ idPrevious moment valueAs
Input quantity, sets torque tdAs the desired value of dwnn, prediction after its study obtains torque estimation output toutAnd pid
The proportionality coefficient k controllingp, integral coefficient kiiAnd differential coefficient kd.
Pid controls the ratio obtaining according to result fal (δ t) and the two-weight neural network of torque deviation pretreatment module
Coefficient kp, integral coefficient kiiAnd differential coefficient kdIt is calculated current setting total current i through pidd, obtain each phase through electric current distribution
Control electric currentWith
The filter of finite difference spreading kalman is included based on the current feedforward compensa-tion module of limited spread Kalman prediction
Ripple device fdekf and relative error processing module.Fdekf is with phase voltage un, rotor position angle θ and compensate after output three-phase electricity
Stream ia、ibAnd icFor input, the three-phase current i of predictiona *、ib *And ic *Input relative error processing module, current forecasting value and pid
The difference of the control electric current of output is as three-phase current deviation to be compensated eia *、eib *And eic *, three-phase current deviation to be compensated eia *、
eib *And eic *Control the three-phase current i of output with dwnn self adaptation pida *、ib *And ic *Sum is the three-phase reference current after compensating
ia、ibAnd ic.Three-phase reference current input current hystersis controller accesses as its setting value, the output of current hysteresis-band control device
Power inverter, power inverter is output as driving switch reluctance motor three phase mains, effectively suppresses its torque pulsation.
The system, according to current torque and current value, suppresses switched reluctance machines torque by the current automatic adaptation of the present invention
Pulse methods, real-Time Compensation reference current, before error arrives, carry back and reference current is made with correction, indirectly suppress srm
Torque pulsation.
Signal processor is connected with display screen, the current electric current of Real time displaying, moment information and torque pulsation rate information.
Signal processor is furnished with can interface, to be connected with the can controller of automobile and the connection of other electrical communications.
Compared with prior art, a kind of current automatic adaptation of the present invention controls the method reducing switched reluctance machines torque pulsation
Advantage with system is: 1, carries out the pretreatment of nonlinear transformation to the torque deviation of switched reluctance machines, to adapt to the non-of srm
Linear characteristic;2nd, during self adaptation pid controls, parameter passes through improved two-weight neural network on-line tuning;3rd, pass through limited spread
The predicted current of Kalman filtering, realizes current feedforward compensa-tion control, improves the predictive ability of control system;4th, dwnn self adaptation
The direct torque of pid with reference to fdekf prediction transient current, realize current feedforward compensa-tion, two kinds of controls act on, effectively simultaneously
The torque pulsation of suppression suppression switched reluctance machines;Distributed using traditional torque open loop and control, the pulsation rate of switched reluctance machines reaches
46.42%, and drop to 1.65% using the pulsation rate that the present invention suppresses switched reluctance machines.
Brief description
Fig. 1 is the step -1 that this current automatic adaptation controls the embodiment of the method reducing switched reluctance machines torque pulsation
Structural representation is adjusted based on two-weight neural network current automatic adaptation;
Fig. 2 is the step -1 that this current automatic adaptation controls the embodiment of the method reducing switched reluctance machines torque pulsation
Two-weight neural network learning structure schematic diagram;
Fig. 3 is the step -1 that this current automatic adaptation controls the embodiment of the method reducing switched reluctance machines torque pulsation
Current feedforward compensa-tion structure chart based on limited spread Kalman prediction;
Fig. 4 is the structural representation that this current automatic adaptation controls the system embodiment reducing switched reluctance machines torque pulsation
Figure;
Fig. 5 is the operational flow diagram that this current automatic adaptation controls the system reducing switched reluctance machines torque pulsation.
Specific embodiment
Current automatic adaptation controls the embodiment of the method reducing switched reluctance machines torque pulsation
This current automatic adaptation controls the embodiment of the method reducing switched reluctance machines torque pulsation, and key step is as follows:
, torque deviation pretreatment
It is as follows that the introducing nonlinear function of this example carries out pretreatment to torque deviation:
Wherein δ represents the transformation range of feedback deviation, span 0.01td~0.1td, tdFor setting torque, fal represents
Preconditioned functions, sign represents sign function, and that is, δ t is more than zero, and value is 1, and less than zero, value is -1.δ t is to set torque
tdTransient torque t with actual measurementeBetween deviation.α is regulation coefficient, and this example takes 1.
Self adaptation pid direct torque based on two-weight neural network
- 1 two-weight neural network
As shown in Fig. 2 two-weight neural network dwnn includes input layer, hidden layer and output layer.As shown in figure 1, switch
Instantaneous torque t of the torque sensor detection installed on reluctance motore, instantaneous torque t of previous momente_1And setting electric current id
Increment △ idPrevious moment valueAs the input quantity of dwnn, set torque tdAs the desired value of dwnn, through its study
Dwnn prediction obtains torque estimation output t afterwardsoutAnd the proportionality coefficient k that pid controlsp, integral coefficient kiiAnd differential coefficient kd.
Pid controls torque deviation pre-processed results fal (δ t) obtaining according to step i and two-weight neural network dwnn
The proportionality coefficient k obtainingp, integral coefficient kiiAnd differential coefficient kdIt is calculated current setting total current i through pidd, divide through electric current
Join and obtain each phase control electric currentWith
Dwnn after adding the self-adaptative adjustment of power to improve is described as follows:
Wherein h (j) is hidden layer output function, and f (x) is activation primitive, toutTorque estimation output for dwnn, wzjFor
Directional weighting, qzjFor core weights, vjFor exporting weights, this example a=0.1, m=1, bzjFor self-adaptative adjustment power, e is nature
The bottom of logarithm.Hidden layer is z layer, z=1,2,3;Output layer is j layer, j=1,2,3.
In order to obtain the parameter in formula (2), the performance index function is taken to be:
Wherein, △ t1It is defined as the transient torque t surveyingeExport t with two-weight neural network dwnn torque estimationoutBetween
Deviation.
According to gradient descent method, directional weighting wzj, core weights qzj, output weights vjAnd power bzjIncrement iteration
Algorithm is as follows:
η is the learning rate of weights, and this example takes η=0.2, ηbFor power learning rate, this example takes ηb=0.4.
Self adaptation pid of -2 two-weight neural networks controls
Self adaptation pid controls the setting electric current of output as follows:
id(k)=id(k-1)+kpxc(1)+kiixc(2)+kdxc(3) (5)
Wherein,
Choose performance index function as follows,
According to gradient descent method, kp,kii,kdIterative algorithm as follows:
ηkp, ηkz, ηkdIt is the learning coefficient of ratio, integration and differential coefficient respectively, this example is all taken as 0.2.Its value
Obtained by two-weight neural network identification.
It is idIncrement △ idPrevious moment value, approximately take:
Wherein toutFor the torque estimation output of two-weight neural network identification, w1jIt is to be connected to hidden layer from jth output layer
In z=1 layer neuron directional weighting, q1jFor its core weights, b1jAdjust power for it.These parameters pass through formula (4)
Obtain.
Pid is output as setting total current id.Three-phase control electric current is respectively ia *And ib *And ic *, below with b phase for turning off
Phase, c phase is for, as a example opening phase, the electric current of biphase commutation is allocated as follows:
Wherein: partition function:
Wherein θ is motor rotor position angle, θonFor turn-on angle, this example θon=11.25 degree, θovFor rotor-position angle overlap,
This example θov=3.5 degree.
Current feedforward compensa-tion based on Kalman prediction controls
The current forecasting of -1 finite difference EKF
As shown in figure 3, the pre- current compensation of this example finite difference EKF includes two parts, finite difference extends
Kalman filter fdekf and relative error processing module.Switched reluctance machines n=1,2,3 phases, i.e. n-th in a, b, c three-phase
Phase voltage un, rotor position angle θ and compensate after output three-phase current ia、ibAnd icInput for fdekf, fdekf prediction
Three-phase current ia *、ib *And ic *Input relative error processing module, two-weight neural network dwnn self adaptation pid control simultaneously is defeated
The three-phase current i going outa *、ib *And ic *Also input relative error processing module, relative error processing module exports three-phase electricity to be compensated
Stream deviation eia *、eib *And eic *, three-phase current deviation to be compensated eia *、eib *And eic *Control the three of output with dwnn self adaptation pid
Phase current ia *、ib *And ic *Sum is the three-phase reference current i after compensatinga、ibAnd ic.
The n-th electric current i of switched reluctance machinesn, rotating speed w and rotor position angle θ state equation as follows:
Wherein,ψ is switched reluctance motor flux linkage, and t is the sampling period, this example t=1 second.J is choosing
Select rotary inertia, teFor transient torque, tlFor load torque, unFor n-th voltage, r is the winding resistance value of switched reluctance machines.
This example adopts fdekf to realize to the output three-phase current i after compensatinga、ibAnd icNext step prediction, obtain ia *、
ib *And ic *.Resistance r in formula (9), parameter aa and c are estimated by fdekf and obtain.
The Front feedback control of -2 electric currents
The three-phase current i of fdekf predictiona *And ib *And ic *Control gained three-phase reference current i with dwnn self adaptation pida *With
ib *And ic *, obtaining three-phase current error to be compensated through relative error after processing is eia *And eib *And eic *, for feedforward compensation, obtain
Three-phase reference current i to after compensateaAnd ibAnd ic.
Its relative error processing procedure is as follows:
Three-phase reference current i after feedforward compensationa、ibAnd icAs follows:
Three-phase reference current after feedforward compensation setting as the current hysteresis-band control device of technology maturation is obtained by formula (11)
Definite value, the output of current hysteresis-band control device, through power inverter driving switch reluctance motor, effectively suppresses switched reluctance machines
Torque pulsation.
Current automatic adaptation controls the system embodiment reducing switched reluctance machines torque pulsation
This controls the embodiment of the method reducing switched reluctance machines torque pulsation, design current according to above-mentioned current automatic adaptation
Self Adaptive Control reduces the system embodiment of switched reluctance machines torque pulsation, as shown in Figures 4 and 5, including signal processor, mould
Number modular converter, current hysteresis-band control device, power inverter, three-phase current sensor, torque sensor and rotor position sensing
Device.
Three current sensors are respectively arranged in the three-phase power line of switched reluctance machines, detect each phase current, torque passes
Sensor is installed on the output shaft of switched reluctance machines, the transient torque of detection motor, and rotor-position sensor is installed on switch magnetic
The rotor of resistance motor, detects rotor position angle.Rotor-position sensor, torque sensor and current sensor are through analog digital conversion mould
Block connects signal processor.
Signal processor contains torque deviation pretreatment module, the pid self-adaptive control module of two-weight neural network and
Current feedforward compensa-tion module based on limited spread Kalman prediction.
Torque deviation pretreatment module is to setting torque tdInstantaneous torque t with torque sensor detectioneTorque deviation enter
Row nonlinear function pretreatment, its result fal (δ t) is as the input of pid self-adaptive control module.
Two-weight neural network dwnn in the pid self-adaptive control module of two-weight neural network is examined with torque sensor
Instantaneous torque t surveyede, instantaneous torque t of previous momente_1And setting electric current idIncrement △ idPrevious moment valueAs defeated
Enter amount, set torque tdAs the desired value of dwnn, prediction after its study obtains torque estimation output toutAnd pid control
The proportionality coefficient k of systemp, integral coefficient kiiAnd differential coefficient kd.
Pid controls the ratio obtaining according to result fal (δ t) and the two-weight neural network of torque deviation pretreatment module
Coefficient kp, integral coefficient kiiAnd differential coefficient kdIt is calculated current setting total current i through pidd, obtain each phase through electric current distribution
Control electric currentWith
The filter of finite difference spreading kalman is included based on the current feedforward compensa-tion module of limited spread Kalman prediction
Ripple device fdekf and relative error processing module.Fdekf is with phase voltage un, rotor position angle θ and compensate after output three-phase electricity
Stream ia、ibAnd icFor input, the three-phase current i of predictiona *、ib *And ic *Input relative error processing module, current forecasting value and pid
The difference of the control electric current of output is as three-phase current deviation to be compensated eia *、eib *And eic *, three-phase current deviation to be compensated eia *、
eib *And eic *Control the three-phase current i of output with dwnn self adaptation pida *、ib *And ic *Sum is the three-phase reference current after compensating
ia、ibAnd ic.Three-phase reference current input current hystersis controller accesses as its setting value, the output of current hysteresis-band control device
Power inverter, power inverter is output as driving switch reluctance motor three phase mains, effectively suppresses its torque pulsation.
The system, according to current torque and current value, suppresses switched reluctance machines torque by the current automatic adaptation of the present invention
Pulse methods, real-Time Compensation reference current, before error arrives, carry back and reference current is made with correction, indirectly suppress srm
Torque pulsation.
Signal processor is connected with display screen, the current electric current of Real time displaying, moment information and torque pulsation rate information.
This example signal processor is furnished with can interface, is connected with the can controller of automobile and other electrical communications connect.
Above-described embodiment, only the purpose of the present invention, technical scheme and beneficial effect are further described is concrete
Individual example, the present invention is not limited to this.All any modifications made within the scope of disclosure of the invention, equivalent, change
Enter, be all contained within protection scope of the present invention.
Claims (4)
1. a kind of current automatic adaptation controls the method reducing switched reluctance machines torque pulsation, and key step is as follows:
, torque deviation pretreatment
Introduce nonlinear function and torque deviation carried out with pretreatment:
Wherein δ represents the transformation range of feedback deviation, span 0.01td~0.1td, tdFor setting torque, fal represents pre- place
Reason function, sign represents sign function, and that is, δ t is more than zero, and value is 1, and less than zero, value is -1;δ t is to set torque tdWith
The transient torque t of actual measurementeBetween deviation;α is regulation coefficient scope 0~1;
Self adaptation pid direct torque based on two-weight neural network
- 1 two-weight neural network
Two-weight neural network dwnn includes input layer, hidden layer and output layer;The torque sensing installed on switched reluctance machines
Instantaneous torque t of device detectione, instantaneous torque t of previous momente_1And setting electric current idIncrement △ idPrevious moment valueMake
Input quantity for dwnn, sets torque tdAs the desired value of dwnn, after its study, to obtain torque estimation defeated for dwnn prediction
Go out toutAnd the proportionality coefficient k that pid controlsp, integral coefficient kiiAnd differential coefficient kd;
Torque deviation pre-processed results fal (δ t) that pid control foundation step i obtains and two-weight neural network dwnn obtain
Proportionality coefficient kp, integral coefficient kiiAnd differential coefficient kdIt is calculated current setting total current i through pidd, distribute through electric current
To each phase control electric currentWith
The present invention adds the self-adaptative adjustment of power, and the two-weight neural network dwnn after improvement is described as follows:
Wherein h (j) is hidden layer output function, and f (x) is activation primitive, toutTorque estimation output for dwnn, wzjFor direction
Weights, qzjFor core weights, vjFor export weights, 0 < a < 1, take a=0.1, m span 1~10, take m=1, bzjFor adaptive
Power should be adjusted, e is the bottom of natural logrithm;Hidden layer is z layer, z=1,2,3;Output layer is j layer, j=1,2,3;
The performance index function is taken to be:
Wherein, △ t1It is defined as the transient torque t surveyingeExport t with two-weight neural network dwnn torque estimationoutBetween inclined
Difference;
According to gradient descent method, directional weighting wzj, core weights qzj, output weights vjAnd power bzjIncrement iterative algorithm such as
Under:
η is the learning rate of weights, value 0~1;ηbFor power learning rate, span 0~1;
Self adaptation pid of -2 two-weight neural networks controls
Self adaptation pid controls the setting electric current of output as follows:
id(k)=id(k-1)+kpxc(1)+kiixc(2)+kdxc(3) (5)
Wherein,
Choose performance index function as follows,
According to gradient descent method, kp,kii,kdIterative algorithm as follows:
ηkp, ηkz, ηkdIt is the learning coefficient of ratio, integration and differential coefficient respectively, span 0~1,Its value is by double power
Value neural network identification obtains;
It is idIncrement △ idPrevious moment value, approximately take:
Wherein toutFor the torque estimation output of two-weight neural network identification, w1jIt is to be connected to hidden layer from jth output layer
The directional weighting of z=1 layer neuron, q1jFor its core weights, b1jAdjust power for it;These parameters are passed through formula (4) and are obtained
Arrive;
Pid is output as setting total current id;Three-phase control electric current is respectively ia *And ib *And ic *, below with b phase for turning off phase, c
Mutually for, as a example opening phase, the electric current of biphase commutation is allocated as follows:
Wherein: partition function:
Wherein θ is motor rotor position angle, θonFor turn-on angle, 10~15 degree of span, θovFor rotor-position angle overlap, value
2~5 degree of scope;
Current feedforward compensa-tion based on Kalman prediction controls
The current forecasting of -1 finite difference EKF
The pre- current compensation of described finite difference EKF includes two parts, finite difference extended Kalman filter
Fdekf and relative error processing module;Switched reluctance machines n=1,2,3 phases, i.e. n-th voltage u in a, b, c three-phasen, turn
Output three-phase current i after sub- angular position theta and compensationa、ibAnd icInput for fdekf, the three-phase current i of fdekf predictiona *、
ib *And ic *Input relative error processing module, the three-phase current of the output of two-weight neural network dwnn self adaptation pid control simultaneously
ia *、ib *And ic *Also input relative error processing module, relative error processing module exports three-phase current deviation to be compensated eia *、
eib *And eic *, three-phase current deviation to be compensated eia *、eib *And eic *Control the three-phase current i of output with dwnn self adaptation pida *、
ib *And ic *Sum is the three-phase reference current i after compensatinga、ibAnd ic;
The n-th electric current i of switched reluctance machinesn, rotating speed w and rotor position angle θ state equation as follows:
Wherein,ψ is switched reluctance motor flux linkage, and t is the sampling period, and value is 1~3 second, and j is to select
Rotary inertia, teFor transient torque, tlFor load torque, unFor n-th voltage, r is the winding resistance value of switched reluctance machines;Electricity
Resistance r, parameter aa and c are to estimate by fdekf to obtain;
The Front feedback control of -2 electric currents
The three-phase current i of fdekf predictiona *And ib *And ic *Control gained three-phase reference current i with dwnn self adaptation pida *And ib *And
ic *, obtaining three-phase current error to be compensated through relative error after processing is eia *And eib *And eic *, for feedforward compensation, mended
Three-phase reference current i after repayingaAnd ibAnd ic;
Its relative error processing procedure is as follows:
Three-phase reference current i after feedforward compensationa、ibAnd icAs follows:
Three-phase reference current after feedforward compensation is obtained as the setting value of current hysteresis-band control device, Hysteresis Current control by formula (11)
The output of device processed, through power inverter driving switch reluctance motor, the torque pulsation of effect suppression switched reluctance machines.
2. current automatic adaptation according to claim 1 controls the method reducing switched reluctance machines torque pulsation, design
A kind of current automatic adaptation controls the system reducing switched reluctance machines torque pulsation, including signal processor, analog-to-digital conversion module,
Current hysteresis-band control device, power inverter, three-phase current sensor, torque sensor and rotor-position sensor;Its feature exists
In:
Three current sensors are respectively arranged in the three-phase power line of switched reluctance machines, detect each phase current, torque sensor
It is installed on output shaft, the output torque of detection motor of switched reluctance machines, rotor-position sensor is installed on switching magnetic-resistance electricity
The rotor of machine, detects rotor position angle;Position sensor, torque sensor and current sensor connect letter through analog-to-digital conversion module
Number processor;
Signal processor contains torque deviation pretreatment module, the pid self-adaptive control module of two-weight neural network and being based on
The current feedforward compensa-tion module of limited spread Kalman prediction;
Torque deviation pretreatment module is to setting torque tdInstantaneous torque t with torque sensor detectioneTorque deviation carry out non-
Linear function pretreatment, its result fal (δ t) is as the input of pid self-adaptive control module;
Two-weight neural network dwnn in the pid self-adaptive control module of two-weight neural network is detected with torque sensor
Instantaneous torque te, instantaneous torque t of previous momente_1And setting electric current idIncrement △ idPrevious moment valueAs input
Amount, sets torque tdAs the desired value of dwnn, prediction after its study obtains torque estimation output toutAnd pid controls
Proportionality coefficient kp, integral coefficient kiiAnd differential coefficient kd;
Pid controls the proportionality coefficient obtaining according to result fal (δ t) and the two-weight neural network of torque deviation pretreatment module
kp, integral coefficient kiiAnd differential coefficient kdIt is calculated current setting total current i through pidd, obtain each phase control through electric current distribution
Electric currentWith
Finite difference extended Kalman filter is included based on the current feedforward compensa-tion module of limited spread Kalman prediction
Fdekf and relative error processing module;Fdekf is with phase voltage un, rotor position angle θ and compensate after output three-phase current ia、
ibAnd icFor input, the three-phase current i of predictiona *、ib *And ic *Input relative error processing module, current forecasting value is exported with pid
Control electric current difference as three-phase current deviation to be compensated eia *、eib *And eic *, three-phase current deviation to be compensated eia *、eib *With
eic *Control the three-phase current i of output with dwnn self adaptation pida *、ib *And ic *Sum is the three-phase reference current i after compensatinga、ib
And ic;Three-phase reference current input current hystersis controller is as its setting value, the output access power of current hysteresis-band control device
Changer, power inverter is output as driving switch reluctance motor three phase mains.
3. current automatic adaptation according to claim 2 controls the system reducing switched reluctance machines torque pulsation, its feature
It is:
Described signal processor is connected with display screen, the current electric current of Real time displaying, moment information and torque pulsation rate information.
4. current automatic adaptation according to claim 2 controls the system reducing switched reluctance machines torque pulsation, its feature
It is:
Described signal processor is furnished with can interface.
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