CN117040352A - PMLSM motor thrust fluctuation suppression method, system, chip and equipment - Google Patents

Abstract

The application discloses a method, a system, a chip and equipment for suppressing thrust fluctuation of a PMLSM motor, which are used for establishing a voltage model of a vector permanent magnet synchronous linear motor, and obtaining a voltage prediction model of the vector permanent magnet synchronous linear motor after Euler discretization; a variable gain Luenberger disturbance observer is established based on a vector permanent magnet synchronous linear motor voltage prediction model, and disturbance voltage at the k moment is obtained through calculation according to the collected voltage and current at the k moment; establishing a fuzzy unscented Kalman filter, and calculating the current at the moment k+1 by the fuzzy unscented Kalman filter by using the current at the moment k while eliminating the static error and noise of the current; and calculating the disturbance voltage at the moment k+1 according to the disturbance voltage at the moment k, calculating a voltage vector at the moment k+1 according to a voltage prediction model of the vector permanent magnet synchronous linear motor, and compensating the voltage vector at the moment k+1 by combining the disturbance voltage at the moment k+1. The noise immunity of the motor is improved, and the effect of reducing the running noise of the motor can be achieved while the current noise is reduced.

Description

PMLSM motor thrust fluctuation suppression method, system, chip and equipment

Technical Field

The application belongs to the technical field of permanent magnet synchronous linear motor control, and particularly relates to a PMLSM motor thrust fluctuation suppression method, a PMLSM motor thrust fluctuation suppression system, a PMLSM motor thrust fluctuation suppression chip and PMLSM motor thrust fluctuation suppression equipment.

Background

The permanent magnet synchronous linear motor (Permanent Magnet Liner Synchronous Motor, PMLSM) has high thrust density and high response speed; the reliability is good, and the efficiency is high; the method has the remarkable advantages of good controllability, high precision and the like, and is widely applied to industries such as high-grade numerical control machine tools, semiconductor processing equipment, high-speed logistics and the like. Because of thrust fluctuation during PMLSM operation, on one hand, vibration and noise can be caused, mechanical and internal electric elements are damaged, and the performance and reliability of the linear motor are affected; on the other hand, the thrust fluctuation can cause the running of the linear motor to fluctuate, thereby influencing the motion precision and stability of the linear motor, and making the linear motor difficult to meet the high-precision motion control requirement.

Meanwhile, the PMLSM application environment requires that the motor have both high dynamic and strong noise immunity. At present, dead-beat current predictive control (Deadbeat Predictive Current Control, DPCC) is applied to a current loop to meet the requirement of PMLSM high dynamic property, but depending on system parameters, system stability is sensitive to parameter change, particularly motor inductance change, current static difference can be generated when the motor model parameters of a controller are inconsistent with actual motor parameters, so that the system efficiency is reduced, rated torque cannot be output, and the system cannot work in a torque control mode and the like. Meanwhile, the DPCC is highly dependent on an accurate motor model, so that the motor robustness is poor due to parameter change, and further the noise immunity is reduced.

Disclosure of Invention

The application aims to solve the technical problems of providing a PMLSM motor thrust fluctuation suppression method, a PMLSM motor thrust fluctuation suppression system, a PMLSM motor thrust fluctuation suppression chip and PMLSM motor thrust fluctuation suppression equipment aiming at the defects in the prior art. On the basis of improving the precision, the disturbance observer compensates the output of the current loop, so that the disturbance resistance of the motor can be greatly improved, and the motor can meet various application occasions; meanwhile, for the problem of current static difference, by adopting an improved unscented Kalman filter, the collected current is reduced in noise while the current static difference is improved, and the control effect is comprehensively improved.

The application adopts the following technical scheme:

a PMLSM motor thrust fluctuation suppression method comprises the following steps:

s1, establishing a voltage model of a vector permanent magnet synchronous linear motor, and obtaining a voltage prediction model of the vector permanent magnet synchronous linear motor after Euler discretization;

s2, establishing a variable gain Luenberger disturbance observer based on the vector permanent magnet synchronous linear motor voltage prediction model obtained in the step S1, and calculating to obtain disturbance voltage at the k moment according to the acquired voltage and current at the k moment;

s3, establishing a fuzzy unscented Kalman filter, and calculating the current at the moment k+1 by the fuzzy unscented Kalman filter by using the current at the moment k while eliminating the static error and noise of the current; and (3) calculating the disturbance voltage at the moment k+1 according to the disturbance voltage at the moment k obtained in the step (S2), calculating the voltage vector at the moment k+1 according to the vector permanent magnet synchronous linear motor voltage prediction model obtained in the step (S1), and compensating the voltage vector at the moment k+1 by combining the disturbance voltage at the moment k+1.

Specifically, in step S1, a continuous domain vector PMLSM model is built under a rotation coordinate system; according to a continuous domain vector PMLSM model, a motor state equation is constructed, a voltage prediction model is obtained after Euler discretization, and a control voltage vector is obtained through voltage back calculation as follows:

u(k+1)＝H ^-1 {i _ref -G[Gi _k +Hu _k +K]-K}

where u (k+1) is the predicted output voltage at time k, i (k) is the acquisition current at time k, and u (k) is the acquisition at time kVoltage, G is a current matrix, H is a voltage matrix, K is a back emf coefficient matrix, T _S For the controller period, i _ref Reference current for the rotor.

Further, the running speed v of the motor is unchanged between the two sampling intervals kT to (k+1) T; setting voltage value u in continuous domain under d-q axis _d (t)、u _q Sample voltage values u at (t) and kT times _d (kT)、u _q (kT) is equal, the continuous domain vector PMLSM model is as follows:

wherein u is _d 、u _q 、i _d 、i _q D-q axis voltage and d-q axis current in a two-phase rotation d-q coordinate system; r is R _s 、L _s 、ψ _f And v, tau and p are respectively stator winding resistance, stator winding inductance, permanent magnet flux linkage, running speed of a rotor relative to a stator, polar distance and differential operator.

Specifically, in step S2, the gain coefficient k of the variable gain Luenberger disturbance observer ₂ The following are provided:

wherein c and a are constants, i _d As a true value of the current flow,for the current estimate, e is the difference between the current true and estimate, and δ is a constant.

Specifically, the step S3 specifically includes:

s301, determining a discrete equation as follows:

wherein, X (k+1) is k+1 predicted by the equivalent modelThe current of the d-q axis and the disturbance voltage are carved, T is a state output matrix, G is a state variable coefficient matrix, H is an output coefficient matrix, w (k) is process noise, v (k) is measurement noise, and u= [ u ] _d u _q ]，u _d 、u _q The voltage is d-q axis voltage under a two-phase rotation d-q coordinate system, and Z (k) is output current and disturbance voltage at k moment;

s302, selecting a group of sampling data and the weight value occupied by the sampling data according to the value of the Sigma point set;

s303, solving a covariance matrix and a prediction equation of the system state quantity according to the prediction value of the Sigma point set obtained in the weighted summation step S302;

s304, predicting a mean matrix X (k+ 1|k) and a covariance matrix P (k+ 1|k) at the moment k+1 according to the discrete voltage equation prediction system obtained in the step S301;

s305, according to the prediction mean matrix X (k+ 1|k) and the covariance matrix P (k+ 1|k), generating new 9 Sigma point sets by using UT transformation again;

s306, calculating a predicted state quantity at the time of k+1 to obtain 5 predicted results, wherein the predicted results are specifically:

Z ⁽ⁱ⁾ (k+1|k)＝TX(k+1|k)+v(k)

Z ⁽ⁱ⁾ (k+ 1|k) is the system estimated output quantity at the time of k+1 of the system, T is a state output matrix, v (k) is measurement noise, and i is 0, 1, 2, 3 and 4;

s307, Z obtained according to step S306 ⁽ⁱ⁾ (k+ 1|k) calculating the weights of the prediction mean and covariance matrix at time k+1, when the difference DeltaN between the actual value and the theoretical value _k At > 0, the measurement noise covariance R (k) is reduced, when the difference DeltaN between the actual and theoretical values _k When < 0, the measurement noise covariance R (k) is increased, and when the difference DeltaN between the actual value and the theoretical value is smaller _k When=0, the measurement noise covariance R (k) is kept unchanged according to the different moments Δn _k Value-determining adjustment factor alpha of (2) _k Dynamic adjustment is realized;

s308, calculating a gain matrixP _xkzk For a priori covariance matrix +.>Is the inverse matrix of posterior covariance;

s309, updating a state matrix and a covariance matrix of the unscented Kalman filtering system, predicting the acquisition current and the disturbance voltage at the moment k+1, and realizing compensation voltage disturbance while performing unscented current prediction control.

Further, in step S304, the prediction mean matrix and covariance matrix of the prediction system k+1 time are as follows:

wherein M= [ X ] ⁽ⁱ⁾ (k+1|k)-X ⁽ⁱ⁾ (k+1|k)]X (k+ 1|k) is a prediction mean matrix at k+1, P (k+ 1|k) is a covariance matrix at k+1, and Q (k) is a process noise covariance matrix at k.

Further, in step S309, the state matrix and covariance matrix of the updated system are as follows:

wherein X (k+ 1|k) is a prediction average matrix at time k+1, K (k+1) is a gain matrix, K ^T (k+1) is a transposed matrix of K (k+1), Z (k+1) is an output current and a disturbance voltage at the time of k+1,the average value of the predicted output current and the predicted output disturbance voltage at the time k+1, P (k+ 1|k) is the predicted covariance at the time k+1, and P _zkzk Is a posterior covariance matrix.

In a second aspect, an embodiment of the present application provides a PMLSM motor thrust ripple suppression system, including:

the discrete module is used for establishing a vector permanent magnet synchronous linear motor voltage model, and then obtaining a vector permanent magnet synchronous linear motor voltage prediction model after Euler discretization;

the calculation module is used for establishing a variable gain Luenberger disturbance observer based on the vector permanent magnet synchronous linear motor voltage prediction model obtained by the discrete module, and calculating the disturbance voltage at the k moment according to the acquired voltage and current at the k moment;

the compensation module is used for establishing a fuzzy unscented Kalman filter, and the fuzzy unscented Kalman filter calculates the current at the moment k+1 by using the current at the moment k while eliminating the current static error and noise; and calculating the disturbance voltage at the moment k+1 according to the disturbance voltage at the moment k obtained by the calculation module, calculating the voltage vector at the moment k+1 according to the vector permanent magnet synchronous linear motor voltage prediction model obtained by the discrete module, and compensating the voltage vector at the moment k+1 by combining the disturbance voltage at the moment k+1.

In a third aspect, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the PMLSM motor thrust ripple suppression method described above when the computer program is executed.

In a fourth aspect, an embodiment of the present application provides a computer readable storage medium including a computer program, which when executed by a processor, implements the steps of the PMLSM motor thrust ripple suppression method described above.

Compared with the prior art, the application has at least the following beneficial effects:

a PMLSM motor thrust fluctuation suppression method introduces a new fuzzy control law to improve a traditional unscented Kalman filter; compared with the traditional unscented Kalman filter, the novel fuzzy unscented Kalman filter can improve the current noise suppression condition, can improve the effective suppression of current dead energy, and has better reliability and practicability.

Further, the running speed v of the motor is unchanged between the two sampling intervals kT to (k+1) T; setting voltage value u in continuous domain under d-q axis _d (t)、u _q Sample voltage values u at (t) and kT times _d (kT)、u _q (kT) is equal, established in a rotating coordinate systemA continuous domain vector PMLSM model; according to the continuous domain vector PMLSM model, a motor state equation is constructed, a current prediction model is obtained after Euler discretization, and then a control voltage vector is obtained through voltage back calculation, so that the voltage output value of each computer period can be accurately obtained, and the operation precision of the motor is greatly improved.

Further, the disturbance observer gain coefficient k is adjusted for self-adaption ₂ When the difference between the observed current and the actual current is larger, the fact that the estimated disturbance voltage and the actual disturbance voltage are larger at the moment is indicated, the high gain coefficient can quickly reduce the error, but at the same time, the high gain coefficient can generate oscillation with larger disturbance voltage estimated value when noise exists in the sampled current, so that after the error is reduced to a set range, the gain coefficient is timely reduced, the oscillation can be effectively reduced, and the design of the disturbance observer gain coefficient is adaptively adjusted, so that the disturbance voltage can be quickly and accurately estimated.

Further, in the whole system, by comparing the estimated current with the actual current, the estimation of the voltage disturbance is realized, which requires that the acquired current value is close enough to the actual current in the motor. However, a plurality of links for collecting the current can influence the collected current; through the unscented Kalman filtering algorithm, the estimated deviation of disturbance voltage caused by current noise can be avoided, meanwhile, due to the randomness of the current noise, the measurement noise covariance matrix can change the weight according to the average value of the current value in real time, and the calculation error caused by the randomness of the noise is reduced.

It will be appreciated that the advantages of the second to fourth aspects may be found in the relevant description of the first aspect and are not repeated here.

In summary, the dead-beat current prediction control is designed, so that the operation precision of the motor is greatly improved, the variable gain disturbance observer can greatly improve the noise immunity of the motor, and meanwhile, the fuzzy unscented Kalman filter is used for processing the acquired current, so that the effect of reducing the operation noise of the motor can be achieved while the current noise is reduced.

The technical scheme of the application is further described in detail through the drawings and the embodiments.

Drawings

FIG. 1 is a PMLSM vector control block diagram of the present application;

FIG. 2 is a flowchart of an improved fuzzy Kalman filtering algorithm;

fig. 3 is a block diagram of a chip according to an embodiment of the present application.

Detailed Description

The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.

In the description of the present application, it will be understood that the terms "comprises" and "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

It is also to be understood that the terminology used in the description of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.

It should be further understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations, e.g., a and/or B, may represent: a exists alone, A and B exist together, and B exists alone. In the present application, the character "/" generally indicates that the front and rear related objects are an or relationship.

It should be understood that although the terms first, second, third, etc. may be used to describe the preset ranges, etc. in the embodiments of the present application, these preset ranges should not be limited to these terms. These terms are only used to distinguish one preset range from another. For example, a first preset range may also be referred to as a second preset range, and similarly, a second preset range may also be referred to as a first preset range without departing from the scope of embodiments of the present application.

Depending on the context, the word "if" as used herein may be interpreted as "at … …" or "at … …" or "in response to a determination" or "in response to detection". Similarly, the phrase "if determined" or "if detected (stated condition or event)" may be interpreted as "when determined" or "in response to determination" or "when detected (stated condition or event)" or "in response to detection (stated condition or event), depending on the context.

Various structural schematic diagrams according to the disclosed embodiments of the present application are shown in the accompanying drawings. The figures are not drawn to scale, wherein certain details are exaggerated for clarity of presentation and may have been omitted. The shapes of the various regions, layers and their relative sizes, positional relationships shown in the drawings are merely exemplary, may in practice deviate due to manufacturing tolerances or technical limitations, and one skilled in the art may additionally design regions/layers having different shapes, sizes, relative positions as actually required.

The application provides a PMLSM motor thrust fluctuation suppression method, which comprises the steps of establishing a vector permanent magnet synchronous linear motor model, obtaining a current prediction model after Euler discretization, predicting current after two periods forwards, and obtaining a control voltage vector through voltage back calculation; establishing a variable gain Luenberger disturbance observer, and dynamically adjusting a gain coefficient of the disturbance observer according to the difference value of the estimated current and the actual current; a fuzzy unscented Kalman filter is established, and the current and disturbance of the next beat are predicted while eliminating the static error and noise of the current.

Referring to fig. 1, the method for suppressing thrust fluctuation of a PMLSM motor of the present application includes the following steps:

s1, establishing a vector permanent magnet synchronous linear motor model, obtaining a current prediction model after Euler discretization, predicting current after one period forwards, and obtaining a control voltage vector through voltage back calculation;

s101, establishing a continuous domain vector PMLSM model under a rotating coordinate system;

wherein u is _d 、u _q 、i _d 、i _q D-q axis voltage and d-q axis current in a two-phase rotation d-q coordinate system; r is R _s 、L _s 、ψ _f And v, tau and p are respectively stator winding resistance, stator winding inductance, permanent magnet flux linkage, running speed of a rotor relative to a stator, polar distance and differential operator.

S102, constructing a motor state equation according to a formula (1):

wherein, u _d 、u _q 、i _d 、i _q d-q axis voltage and d-q axis current in a two-phase rotation d-q coordinate system; r is R _s 、L _s 、ψ _f And v, tau and p are respectively stator winding resistance, stator winding inductance, permanent magnet flux linkage, running speed of a rotor relative to a stator, polar distance and differential operator.

The initial condition is time t ₀ Current i at time instant _d (t ₀ )、i _q (t ₀ ) Obtaining a current response i corresponding to the time t moment _d (t)、i _q (t) Motor rotor-statorOperating speed v and voltage u of (2) _d 、u _q Is time-varying, for v and u, to simplify the integral operation in equation (1) _d 、u _q The following limitations are made:

1. between the two sampling intervals kT and (k+1) T, the running speed v of the motor is unchanged;

2. setting voltage value u in continuous domain under d-q axis _d (t)、u _q Sample voltage values u at (t) and kT times _d (kT)、u _q (kT) is equal, i.e.:

the current prediction model after further Euler discretization is as follows:

i(k+1)＝Gi(k)+Hu(k)+K (3)

wherein i (k+1) is a predicted current at time k+1,i (k) is the acquisition current at time k, < >>u (k) is the control voltage at time k, < >>G is the current matrix of the current matrix,h is a voltage matrix, ">K is the back emf coefficient matrix,T _S for the controller period.

The control voltage vector is obtained through voltage back calculation:

u(k+1)＝H ^-1 {i _ref -G[Gi _k +Hu _k +K]-K} (4)

where u (k+1) is the predicted voltage at time k,i _ref reference current for the rotor.

S2, establishing a variable gain Luenberger disturbance observer, and dynamically adjusting a gain coefficient of the disturbance observer according to the difference value of the estimated current and the actual current;

the dead beat current prediction method has the advantages of easiness in implementation, quick dynamic response and the like, but has the same obvious disadvantages that the dead beat current prediction method is too sensitive to motor parameter changes; in the actual application process, parameters of a motor are mismatched, and the control performance is reduced due to interference such as dynamic state of an unmodeled system, so that an accurate disturbance model needs to be built; the method comprises the following steps:

s201, according to the motor voltage equation in the formula (1) obtained in the step S101, there are:

due to the presence of disturbances, the motor model is reconstructed as follows:

from this, the amount of the d-q axis disturbance is deduced to be:

s202, reconstructing a motor model according to the deduced disturbance equation, wherein the motor model is as follows:

selecting i _d 、i _q 、f _d 、f _q For observer state quantity, a state equation containing disturbance is constructed by combining a motor model as follows:

wherein x= [ x ] _d x _q f _d f _q ] ^T ，y＝[i _d i _q i _d i _q ] ^T ，u＝[u _d u _q ] ^T ，

S203, introducing the established state space expression into a disturbance observer to obtain the following formula:

wherein,

discretizing the formula to obtain a disturbance observer model:

wherein,

s204, the gain coefficient of the disturbance observer is adaptively adjusted according to the difference value between the observed current and the actual current, and a variable gain function is introduced for the self-adaptive adjustment:

wherein c and a are constants, i _d As a true value of the current flow,for the current estimate, e is the difference between the current true and estimate, and δ is a constant.

Current difference compensation gain k ₁ And the disturbance difference compensation gain is set as a constant, and the effect of small error and small gain and large error and large gain is realized as shown in the formula.

S3, establishing a fuzzy unscented Kalman filter, and predicting the current and disturbance of the next beat while eliminating the static error and noise of the current.

Referring to fig. 2, because the motor model parameters of the controller and the actual motor parameters are different, the current static difference of the shaft current is caused, the control effect is affected, the noise is processed by adopting a fuzzy unscented kalman filter, the noise is reduced on the collected current, and the disturbance can be predicted while the current is processed, and the specific steps are as follows:

s301, a discrete equation obtained according to a motor voltage equation has the following relation:

wherein the method comprises the steps of u＝[u _d u _q ]，/>

Wherein X (k+1) is equalThe effective model predicts the d-q axis current at the moment of k+1 and the disturbance voltage, T is a state output matrix, G is a state variable coefficient matrix, H is an output coefficient matrix, T _S For the controller period, w (k) is the process noise, v (k) is the measurement noise, and Z (k) is the output current and the disturbance voltage at time k.

S302, selecting a group of sampling data and weight values occupied by the sampling data according to the value of the Sigma point set:

wherein L is the covariance matching windowing size,for the covariance matrix of the current state (updated in real time in each step), Q is the process noise covariance, R is the measurement noise covariance, lambda is the scaling parameter for reducing the total prediction error; alpha is a positive constant, determining the distribution range of Sigma spots, < >>Square value of the ith column number of matrix square root, X ⁽ⁱ⁾ Is the ith column element of the matrix.

The formula (15) is arranged into:

wherein X is ⁽ⁱ⁾ (k|k) is the current correction value, X (k|k) is the current time estimate, and P (k|k) is the updated corrected covariance.

S303, solving a prediction equation of a covariance matrix and a system state quantity according to the prediction value of the Sigma point set obtained in the weighted summation step S302, wherein the weight is obtained by a formula:

where α is the distribution of sampling points for changing Sigma points andthe distance between them, beta is the state distribution parameter, omega ⁽ⁱ⁾ For predictive equation weights, +.>Is variance weight->Is covariance weight.

S304, predicting a mean matrix and a covariance matrix at time k+1 according to the discrete voltage equation prediction system obtained in the step S301:

wherein M= [ X ] ⁽ⁱ⁾ (k+1|k)-x ⁽ⁱ⁾ (k+1|k)]X (k+ 1|k) is a prediction mean matrix at k+1, P (k+ 1|k) is a covariance matrix at k+1, and Q (k) is a process noise covariance matrix at k.

S305, according to the prediction mean matrix X (k+ 1|k) and the covariance matrix P (k+ 1|k), generating new 9 Sigma point sets by using UT transformation again;

wherein X is ⁽ⁱ⁾ (k+ 1|k) is an estimated value at time k+1, P (k+ 1|k) is a covariance predicted at time k+1, and i is 0, 1, 2, 3, and 4.

S306, bringing the formula (19) into the formula (14), and calculating a predicted state quantity at the time of k+1:

Z ⁽ⁱ⁾ (k+1|k)＝TX(k+1|k)+v(k) (18)

Z ⁽ⁱ⁾ (k+ 1|k) is the system estimated output at time k+1 of the system, T is the state output matrix, and v (k) is the measurement noise.

S307, Z obtained according to step S306 ⁽ⁱ⁾ (k+ 1|k) calculating weights of the k+1 time prediction mean and covariance matrix:

wherein P is _xkxk For a priori covariance, P _zkzk R (k+1) is the k+1 time noise covariance, which is the posterior covariance.

The process noise Q (k) is known, and the covariance R (k) of the measured noise is automatically adjusted by adopting a fuzzy reasoning covariance matching technology, wherein the idea of covariance matching is to keep the actual value and the theoretical value of the measured noise equal.

Measuring the actual value of noise:

measuring a noise theoretical value:

difference between actual and theoretical values:

ΔN _k ＝N _k -M _k (22)

according to delta N _k R (k) is sized to N _k And M _k Keeping consistency; when DeltaN _k At > 0, R (k) is reduced, when ΔN _k When < 0, R (k) is increased, when DeltaN _k When=0, R (k) is kept unchanged according to different moments ΔN _k Value-determining adjustment factor alpha of (2) _k Dynamic adjustment is realized.

S308, calculating a gain matrix:

wherein P is _xkzk For the a priori covariance matrix,is the inverse of the posterior covariance.

S309, updating a state matrix and a covariance matrix of the unscented Kalman filtering system:

wherein X (k+ 1|k) is a prediction average matrix at time k+1, K (k+1) is a gain matrix, K ^T (k+1) is a transposed matrix of K (k+1), Z (k+1) is an output current and a disturbance voltage at the time of k+1,the average value of the predicted output current and the predicted output disturbance voltage at the time k+1, P (k+ 1|k) is the predicted covariance at the time k+1, and P _zkzk Is a posterior covariance matrix.

By unscented Kalman filtering, the collected current and disturbance voltage at the moment k+1 are predicted while the current dead difference is eliminated, the dead current prediction control is realized, meanwhile, the voltage disturbance is compensated in time, and the PMLSM thrust disturbance is effectively reduced.

In still another embodiment of the present application, a PMLSM motor thrust ripple suppression system is provided, which can be used to implement the PMLSM motor thrust ripple suppression method described above, and in particular, the PMLSM motor thrust ripple suppression system includes a discrete module, a calculation module, and a compensation module.

The discrete module establishes a voltage model of the vector permanent magnet synchronous linear motor, and then obtains a voltage prediction model of the vector permanent magnet synchronous linear motor after Euler discretization;

the calculation module is used for establishing a variable gain Luenberger disturbance observer based on the vector permanent magnet synchronous linear motor voltage prediction model obtained by the discrete module, and calculating the obtained disturbance voltage at the k moment according to the acquired voltage and current at the k moment;

the compensation module is used for establishing a fuzzy unscented Kalman filter, and the fuzzy unscented Kalman filter calculates the current at the moment k+1 by using the current at the moment k while eliminating the current static error and noise; and calculating the disturbance voltage at the moment k+1 according to the disturbance voltage at the moment k obtained by the calculation module, calculating the voltage vector at the moment k+1 according to the vector permanent magnet synchronous linear motor voltage prediction model obtained by the discrete module, and compensating the voltage vector at the moment k+1 by combining the disturbance voltage at the moment k+1.

In yet another embodiment of the present application, a terminal device is provided, the terminal device including a processor and a memory, the memory for storing a computer program, the computer program including program instructions, the processor for executing the program instructions stored by the computer storage medium. The processor may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processor, digital signal processor (Digital Signal Processor, DSP), application specific integrated circuit (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, etc., which are the computational core and control core of the terminal adapted to implement one or more instructions, in particular to load and execute one or more instructions to implement the corresponding method flow or corresponding functions; the processor of the embodiment of the application can be used for the operation of a PMLSM motor thrust fluctuation suppression method, and comprises the following steps:

establishing a vector permanent magnet synchronous linear motor voltage model, and then obtaining a vector permanent magnet synchronous linear motor voltage prediction model after Euler discretization; a variable gain Luenberger disturbance observer is established based on a vector permanent magnet synchronous linear motor voltage prediction model, and disturbance voltage at the k moment is obtained through calculation according to the collected voltage and current at the k moment; establishing a fuzzy unscented Kalman filter, and calculating the current at the moment k+1 by the fuzzy unscented Kalman filter by using the current at the moment k while eliminating the static error and noise of the current; and calculating the disturbance voltage at the moment k+1 according to the disturbance voltage at the moment k, calculating a voltage vector at the moment k+1 according to a voltage prediction model of the vector permanent magnet synchronous linear motor, and compensating the voltage vector at the moment k+1 by combining the disturbance voltage at the moment k+1.

Referring to fig. 3, the chip 600 includes a processor 622, which may be one or more in number, and a memory 632 for storing computer programs executable by the processor 622. The computer program stored in memory 632 may include one or more modules each corresponding to a set of instructions. Further, the processor 622 may be configured to execute the computer program to perform the PMLSM motor thrust ripple suppression method described above.

In addition, chip 600 may further include a power supply component 626 and a communication component 650, where power supply component 626 may be configured to perform power management of chip 600, and communication component 650 may be configured to enable communication of chip 600, e.g., wired or wireless communication. In addition, the chip 600 may also include an input/output (I/O) interface 658. Chip 600 may operate based on an operating system stored in memory 632.

In another exemplary embodiment, a computer readable storage medium is also provided that includes program instructions that, when executed by a processor, implement the steps of the PMLSM motor thrust ripple suppression method described above. For example, the non-transitory computer readable storage medium may be the memory 632 described above that includes program instructions that are executable by the processor 622 of the chip 600 to perform the PMLSM motor thrust ripple suppression method described above.

In a further embodiment of the present application, the present application also provides a storage medium, in particular, a computer readable storage medium (Memory), which is a Memory device in a terminal device, for storing programs and data. It will be appreciated that the computer readable storage medium herein may include both a built-in storage medium in the terminal device and an extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space storing an operating system of the terminal. Also stored in the memory space are one or more instructions, which may be one or more computer programs (including program code), adapted to be loaded and executed by the processor. The computer readable storage medium may be a high-speed RAM Memory or a Non-Volatile Memory (Non-Volatile Memory), such as at least one magnetic disk Memory.

One or more instructions stored in a computer-readable storage medium may be loaded and executed by a processor to implement the respective steps of the PMLSM motor thrust ripple suppression method of the above embodiments; one or more instructions in a computer-readable storage medium are loaded by a processor and perform the steps of:

establishing a vector permanent magnet synchronous linear motor voltage model, and then obtaining a vector permanent magnet synchronous linear motor voltage prediction model after Euler discretization; a variable gain Luenberger disturbance observer is established based on a vector permanent magnet synchronous linear motor voltage prediction model, and disturbance voltage at the k moment is obtained through calculation according to the collected voltage and current at the k moment; establishing a fuzzy unscented Kalman filter, and calculating the current at the moment k+1 by the fuzzy unscented Kalman filter by using the current at the moment k while eliminating the static error and noise of the current; and calculating the disturbance voltage at the moment k+1 according to the disturbance voltage at the moment k, calculating a voltage vector at the moment k+1 according to a voltage prediction model of the vector permanent magnet synchronous linear motor, and compensating the voltage vector at the moment k+1 by combining the disturbance voltage at the moment k+1.

In summary, according to the PMLSM motor thrust fluctuation suppression method, system, chip and device, through design of dead beat current prediction control, the operation precision of the motor is greatly improved, the noise immunity of the motor can be greatly improved by using the variable gain disturbance observer, meanwhile, the collected current is processed by using the fuzzy unscented Kalman filter, and the effect of reducing the operation noise of the motor can be achieved while the current noise is reduced.

It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, the specific names of the functional units and modules are only for distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.

In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

In the embodiments provided in the present application, it should be understood that the disclosed apparatus/terminal and method may be implemented in other manners. For example, the apparatus/terminal embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.

The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present application may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RandomAccess Memory, RAM), an electrical carrier wave signal, a telecommunications signal, a software distribution medium, etc., it should be noted that the computer readable medium may contain content that is appropriately increased or decreased according to the requirements of jurisdictions and patent practices, such as in certain jurisdictions, according to the jurisdictions and patent practices, the computer readable medium does not contain electrical carrier wave signals and telecommunications signals.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

The above is only for illustrating the technical idea of the present application, and the protection scope of the present application is not limited by this, and any modification made on the basis of the technical scheme according to the technical idea of the present application falls within the protection scope of the claims of the present application.

Claims (10)

1. The PMLSM motor thrust fluctuation suppression method is characterized by comprising the following steps of:

s1, establishing a voltage model of a vector permanent magnet synchronous linear motor, and obtaining a voltage prediction model of the vector permanent magnet synchronous linear motor after Euler discretization;

s2, establishing a variable gain Luenberger disturbance observer based on the vector permanent magnet synchronous linear motor voltage prediction model obtained in the step S1, and calculating to obtain disturbance voltage at the k moment according to the acquired voltage and current at the k moment;

s3, establishing a fuzzy unscented Kalman filter, and calculating the current at the moment k+1 by the fuzzy unscented Kalman filter by using the current at the moment k while eliminating the static error and noise of the current; and (3) calculating the disturbance voltage at the moment k+1 according to the disturbance voltage at the moment k obtained in the step (S2), calculating the voltage vector at the moment k+1 according to the vector permanent magnet synchronous linear motor voltage prediction model obtained in the step (S1), and compensating the voltage vector at the moment k+1 by combining the disturbance voltage at the moment k+1.

2. The PMLSM motor thrust ripple suppression method according to claim 1, wherein in step S1, a continuous domain vector PMLSM model is built under a rotational coordinate system; according to a continuous domain vector PMLSM model, a motor state equation is constructed, a voltage prediction model is obtained after Euler discretization, and a control voltage vector is obtained through voltage back calculation as follows:

u(k+1)＝H ^-1 {i _ref -G[Gi _k +Hu _k +K]-K}

wherein u (k+1) is the predicted output voltage at time K, i (K) is the acquisition current at time K, u (K) is the acquisition voltage at time K, G is the current matrix, H is the voltage matrix, K is the back EMF coefficient matrix, T _S For the controller period, i _ref Reference current for the rotor.

3. The PMLSM motor thrust ripple suppression method of claim 2, wherein an operation speed v of the motor is unchanged between a sampling interval kT to (k+1) T twice; setting voltage value u in continuous domain under d-q axis _d (t)、u _q Sample voltage values u at (t) and kT times _d (kT)、u _q (kT) equality, continuous domain vectorThe quantitative PMLSM model is as follows:

wherein u is _d 、u _q 、i _d 、i _q D-q axis voltage and d-q axis current in a two-phase rotation d-q coordinate system; r is R _s 、L _s 、ψ _f And v, tau and p are respectively stator winding resistance, stator winding inductance, permanent magnet flux linkage, running speed of a rotor relative to a stator, polar distance and differential operator.

4. The PMLSM motor thrust ripple suppression method of claim 1, wherein in step S2, a gain coefficient k of a variable gain Luenberger disturbance observer is obtained ₂ The following are provided:

wherein c and a are constants, i _d As a true value of the current flow,for the current estimate, e is the difference between the current true and estimate, and δ is a constant.

5. The PMLSM motor thrust ripple suppression method of claim 1, wherein step S3 specifically includes:

s301, determining a discrete equation as follows:

wherein X (k+1) is the current and disturbance voltage of d-q axes at k+1 moment predicted by the equivalent model, T is a state output matrix, G is a state variable coefficient matrix, H is an output coefficient matrix, and w (k) is process noiseAcoustic, v (k) is measurement noise, u= [ u ] _d u _q ]，u _d 、u _q The voltage is d-q axis voltage under a two-phase rotation d-q coordinate system, and Z (k) is output current and disturbance voltage at k moment;

s302, selecting a group of sampling data and the weight value occupied by the sampling data according to the value of the Sigma point set;

s303, solving a covariance matrix and a prediction equation of the system state quantity according to the prediction value of the Sigma point set obtained in the weighted summation step S302;

s304, predicting a mean matrix X (k+ 1|k) and a covariance matrix P (k+ 1|k) at the moment k+1 according to the discrete voltage equation prediction system obtained in the step S301;

s305, according to the prediction mean matrix X (k+ 1|k) and the covariance matrix P (k+ 1|k), generating new 9 Sigma point sets by using UT transformation again;

s306, calculating a predicted state quantity at the time of k+1 to obtain 5 predicted results, wherein the predicted results are specifically:

Z ⁽ⁱ⁾ (k+1|k)＝TX(k+1|k)+v(k)

Z ⁽ⁱ⁾ (k+ 1|k) is the system estimated output quantity at the time of k+1 of the system, T is a state output matrix, v (k) is measurement noise, and i is 0, 1, 2, 3 and 4;

s307, Z obtained according to step S306 ⁽ⁱ⁾ (k+ 1|k) calculating the weights of the prediction mean and covariance matrix at time k+1, when the difference DeltaN between the actual value and the theoretical value _k >0, the measurement noise covariance R (k) is reduced, when the difference deltan between the actual value and the theoretical value _k <0, the measurement noise covariance R (k) is increased, and the difference Δn between the actual value and the theoretical value is calculated _k When=0, the measurement noise covariance R (k) is kept unchanged according to the different moments Δn _k Value-determining adjustment factor alpha of (2) _k Dynamic adjustment is realized;

s308, calculating a gain matrixP _xkzk For a priori covariance matrix +.>Is the inverse matrix of posterior covariance;

s309, updating a state matrix and a covariance matrix of the unscented Kalman filtering system, predicting the acquisition current and the disturbance voltage at the moment k+1, and realizing compensation voltage disturbance while performing unscented current prediction control.

6. The PMLSM motor thrust ripple suppression method according to claim 5, wherein in step S304, a prediction mean matrix and a covariance matrix at a time of a prediction system k+1 are as follows:

wherein M= [ X ] ⁽ⁱ⁾ (k+1|k)-X ⁽ⁱ⁾ (k+1|k)]X (k+ 1|k) is a prediction mean matrix at k+1, P (k+ 1|k) is a covariance matrix at k+1, and Q (k) is a process noise covariance matrix at k.

7. The PMLSM motor thrust ripple suppression method according to claim 5, wherein in step S309, the state matrix and the covariance matrix of the update system are as follows:

wherein X (k+ 1|k) is a prediction average matrix at time k+1, K (k+1) is a gain matrix, K ^T (k+1) is a transposed matrix of K (k+1), Z (k+1) is an output current and a disturbance voltage at the time of k+1,the average value of the predicted output current and the predicted output disturbance voltage at the time k+1, P (k+ 1|k) is the predicted covariance at the time k+1, and P _zkzk Is a posterior covariance matrix.

8. A PMLSM motor thrust ripple suppression system, comprising:

the discrete module is used for establishing a vector permanent magnet synchronous linear motor voltage model, and then obtaining a vector permanent magnet synchronous linear motor voltage prediction model after Euler discretization;

the calculation module is used for establishing a variable gain Luenberger disturbance observer based on the vector permanent magnet synchronous linear motor voltage prediction model obtained by the discrete module, and calculating the disturbance voltage at the k moment according to the acquired voltage and current at the k moment;

the compensation module is used for establishing a fuzzy unscented Kalman filter, and the fuzzy unscented Kalman filter calculates the current at the moment k+1 by using the current at the moment k while eliminating the current static error and noise; and calculating the disturbance voltage at the moment k+1 according to the disturbance voltage at the moment k obtained by the calculation module, calculating the voltage vector at the moment k+1 according to the vector permanent magnet synchronous linear motor voltage prediction model obtained by the discrete module, and compensating the voltage vector at the moment k+1 by combining the disturbance voltage at the moment k+1.

9. A chip is characterized in that,

a memory having a computer program stored thereon;

a processor for executing the computer program in the memory to implement the steps of the method of any one of claims 1-7.

10. An electronic device, characterized in that,

comprising a chip as claimed in claim 9.