CN112600473A - Permanent magnet synchronous motor rotor position estimation system and method - Google Patents
Permanent magnet synchronous motor rotor position estimation system and method Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
- H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
- H02P21/0003—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control
- H02P21/001—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control using fuzzy control
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
- H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
- H02P21/13—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
- H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
- H02P21/14—Estimation or adaptation of machine parameters, e.g. flux, current or voltage
- H02P21/18—Estimation of position or speed
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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
- H02P25/00—Arrangements or methods for the control of AC motors characterised by the kind of AC motor or by structural details
- H02P25/02—Arrangements or methods for the control of AC motors characterised by the kind of AC motor or by structural details characterised by the kind of motor
- H02P25/022—Synchronous motors
- H02P25/024—Synchronous motors controlled by supply frequency
- H02P25/026—Synchronous motors controlled by supply frequency thereby detecting the rotor position
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- H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
- H02P27/00—Arrangements or methods for the control of AC motors characterised by the kind of supply voltage
- H02P27/04—Arrangements or methods for the control of AC motors characterised by the kind of supply voltage using variable-frequency supply voltage, e.g. inverter or converter supply voltage
- H02P27/06—Arrangements or methods for the control of AC motors characterised by the kind of supply voltage using variable-frequency supply voltage, e.g. inverter or converter supply voltage using DC to AC converters or inverters
- H02P27/08—Arrangements or methods for the control of AC motors characterised by the kind of supply voltage using variable-frequency supply voltage, e.g. inverter or converter supply voltage using DC to AC converters or inverters with pulse width modulation
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Abstract
The invention discloses a system and a method for estimating the position of a rotor of a permanent magnet synchronous motor, which relate to the technical field of permanent magnet synchronous motors and comprise a motor driving module and a three-phase stator winding of the permanent magnet synchronous motor, wherein the motor driving module is connected with the three-phase stator winding and used for starting and maintaining the normal operation of the permanent magnet synchronous motor; the current sampling module is respectively connected with the motor driving module and the three-phase stator winding and is used for collecting the current of the three-phase stator winding and transmitting the current to the motor driving module after coordinate transformation; the improved Luenberger observer module is respectively connected with the current sampling module and the motor driving module, noise interference selected by a Luenberger gain value K is reduced through wavelet threshold denoising, the Luenberger observer and PI parameters in a phase-locked loop are adjusted through a set of fuzzy rules, and then accurate rotor position information is obtained, so that the robustness of the Luenberger observer is improved, and reliable rotor position and rotating speed information is provided for a control system.
Description
Technical Field
The invention relates to the technical field of permanent magnet synchronous motors, in particular to a system and a method for estimating the position of a rotor of a permanent magnet synchronous motor.
Background
The permanent magnet synchronous motor is used as a main component in a speed regulating system, has the advantages of high efficiency, high power density, small rotational inertia, fast dynamic response and the like, is generally applied to the field of industrial control in recent years, and has attracted extensive attention in research, popularization and application.
In the existing permanent magnet synchronous motor control method, the vector control method is distinguished by the advantages of high precision, high performance, low cost, good torque response and the like. In a traditional permanent magnet synchronous motor driving system, a position sensor is often used for detecting position information of a motor three-phase winding rotor in real time, however, the position sensor is low in detection precision, the cost of the system can be increased, and the reliability of the system is influenced, so that the control without the position sensor becomes an important research direction in the field of motor control, and an improved Luenberger observer can be used for acquiring accurate position information of the rotor in real time, so that the control system without the position sensor is more reliable.
Disclosure of Invention
The invention provides a system and a method for estimating the position of a rotor of a permanent magnet synchronous motor, which aim to solve the problem that a permanent magnet synchronous motor driving system in the existing position-sensor-free control technology cannot accurately acquire the position information of the rotor in real time.
The invention provides a rotor position estimation system of a permanent magnet synchronous motor, which comprises a PI control module, a current sampling module, a space vector pulse width modulation module, an improved Luenberger observer driving module, a motor driving module and a three-phase stator winding of the permanent magnet synchronous motor, wherein the current PI control module is respectively connected with the improved Luenberger observer driving module and the connected space vector pulse width modulation module and is used for adjusting a feedback value so as to enable the feedback value to follow a given value; the current sampling module is respectively connected with the improved Luenberger observer driving module and a three-phase stator winding of the permanent magnet synchronous motor, and is used for sampling the direct-current bus voltage and the three-phase current of the motor, calculating the counter electromotive force by using the observer and further obtaining the position information of the rotor; the motor driving module is respectively connected with the space vector pulse width modulation module and a three-phase stator winding of the permanent magnet synchronous motor and is used for starting and maintaining the normal operation of the permanent magnet synchronous motor.
Optionally, the current PI control module includes: a rotating speed loop PI regulator and a current loop PI regulator; the rotating speed loop PI regulator is connected with a position estimation unit in the improved Luenberger observer driving module and used for calculating the difference value between the given rotating speed and the feedback rotating speed, and a torque current component, namely a quadrature axis reference current i, is generated by the rotating speed loop PI regulatorqrefThe current loop PI regulator is connected with the space vector pulse width modulation module and is used for controlling iqTracing iqref、idTracing idrefObtain a voltage component ud、 uq。
Optionally, the current sampling module comprises: a current amplifying unit and a coordinate transformation unit; the current amplification unit is connected with a three-phase stator winding of the permanent magnet synchronous motor and is used for acquiring and amplifying current signals in the three-phase stator winding: the coordinate transformation unit is connected with the current amplification unit and is used for transforming the phase current measured by the current amplification unit into a current component i under a two-phase rotating coordinate systemd、 iq。
Optionally, the coordinate transformation unit includes: park transformation and Clarke transformation; the Park transformation is used for transforming the two-phase stationary coordinate system to the two-phase rotating coordinate system; the Clarke transformation is used to transform the three-phase stationary coordinate system to the two-phase stationary coordinate system.
Optionally, the modified Luenberger observer drive module comprises: the device comprises a position calculation unit, a rotating speed calculation unit, a wavelet denoising unit and a fuzzy parameter setting unit; the position calculation unit is connected with the coordinate transformation unit and is used for converting i intoα、iβAnd rotor position
Combining and transforming the two phases of the three-phase images to a two-phase rotating coordinate system; the rotating speed calculating unit is connected with the coordinate transformation unit and is used for obtaining the position information of the permanent magnet synchronous motor; the wavelet denoising unit is used for reducing noise with a large fixed influence factor on the gain K value of the Luenberger observer; the fuzzy parameter setting unit is used for setting the Luenberger observer and the PI parameter in the phase-locked loop.
The invention also provides a permanent magnet synchronous motor rotor position estimation method which is characterized in that a Luenberger observer is designed by using wavelet threshold denoising, a fuzzy PI controller is adopted to improve a phase-locked loop, and finally the accurate permanent magnet synchronous motor rotor speed and position are obtained.
Optionally, the specific method for designing the Luenberger observer by using wavelet threshold denoising is as follows: firstly, current and voltage signals are obtained by resistance sampling reconstruction, and wavelet denoising is carried out on the obtained current signals and voltage signals. The input current and voltage signals, namely original signals, are respectively subjected to convolution decomposition with a low-pass filter bank constructed by dym5 wavelets, and a trous porous algorithm is utilized to perform tree decomposition of j scales to obtain a scale function and a wavelet function of j scales. And then respectively carrying out threshold denoising on the decomposed signals by utilizing a maximum and minimum threshold criterion and a soft threshold method, and then carrying out inverse trous algorithm reconstruction on the denoised signals to obtain original signals. And comparing the signal with the original signal, observing the convergence speed of the Lun berger observer before and after denoising, and obtaining the value of the adopted K value.
Optionally, the specific method for improving the phase-locked loop by using the fuzzy PI controller includes: the method combines the traditional PI control and the fuzzy control, gives full play to the advantages of the two algorithms, establishes a fuzzy set, a membership function and a fuzzy control rule table of input and output variables, and continuously modifies and adjusts the PI parameters on line to realize the self-tuning of the PI parameters. The online modification method can improve the dynamic and static characteristics of the controlled object to a great extent, strengthen the performance of the controller and enable the controller to have good adaptability and robustness.
The invention has the beneficial effects that:
1. the permanent magnet synchronous motor rotor position estimation system in the technical scheme of the invention can accurately estimate the information such as the position and the rotating speed of the motor rotor in the range of medium and high rotating speeds, and has small rotating speed estimation error and high rotating speed estimation precision in the process of accelerating or decelerating the motor; under the condition of sudden load addition or sudden load unloading, the fluctuation of the estimated value of the rotating speed is small, and the estimated value can be quickly recovered to the rated rotating speed, which shows that the system has good robustness and can enable the system to respond faster.
2. In the technical scheme of the invention, the method for estimating the position and the speed of the rotor by the fuzzy Luneberg observer based on wavelet threshold denoising replaces a low-pass filter by a comprehensive filter bank based on wavelet threshold denoising, well removes partial noise before estimating the position and the speed of the rotor, then improves a PI module in a phase-locked loop, designs a fuzzy control strategy, designs a fuzzy control rule table, constructs a membership function, and calculates Kp、KiSo as to adjust the parameters of the PI in real time and improve the precision of the observer.
Drawings
The features and advantages of the present invention will be more clearly understood by reference to the accompanying drawings, which are illustrative and not to be construed as limiting the invention in any way, and in which:
FIG. 1 is a block diagram of a rotor position estimation system of a permanent magnet synchronous motor according to the present invention;
FIG. 2 is a block diagram of an improved Luenberger observer according to the present invention;
FIG. 3 is a flow chart of wavelet threshold denoising in the present invention;
FIG. 4 is a block diagram of a fuzzy self-tuning PI controller structure according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a permanent magnet synchronous motor rotor position estimation system, as shown in figure 1: by using idThe permanent magnet synchronous motor vector system is composed of a speed outer ring and a current inner ring in a 0 control mode, and mainly comprises a PI control module, a current sampling module, a space vector pulse width modulation module, an improved Luenberger observer driving module, a motor driving module and a three-phase stator winding of a permanent magnet synchronous motor.
The rotor position estimation control strategy specifically comprises the step of converting the phase current measured by the current reading module into a current component i under a two-phase static coordinate system through Clarkeα、iβI is toα、iβThe current component i under a two-phase rotating coordinate system is obtained by combining the rotor position theta and performing Park transformationd、iq(ii) a The difference value of the given speed and the feedback speed is used for generating a torque current component, namely a quadrature axis reference current i through a PI regulatorqref(ii) a Taking an exciting current component, namely a direct-axis reference current as idrefIs 0, i is controlled by two PI regulatorsqTracing iqref、 idTracing idrefObtain a voltage component ud、uq(ii) a Will voltage ud、uqAnd the voltage component u of the two-phase static coordinate system is obtained by combining the rotor position theta and performing inverse Park transformationα、uβ(ii) a Finally, the voltage u is measuredα、uβThe three-phase inverter is modulated into 6 paths of switching signals through the SVPWM modulation algorithm, the on-off of the three-phase inverter is controlled, and then the permanent magnet synchronous motor is started and maintained to normally run.
The invention provides a permanent magnet synchronous motor rotor position estimation method, as shown in figure 2, on the basis of a traditional Luenberger observer, a wavelet threshold denoising function is added, a fuzzy PI controller is adopted to improve a phase-locked loop, and the improved Luenberger observer can obtain more accurate permanent magnet synchronous motor rotor speed omegaeAnd rotor position
The flow chart of wavelet threshold denoising is shown in fig. 3, and the using method comprises the following steps:
the method comprises the following steps: defining an original linear signal:
f(t)＝x1φ2，0(t)+x2φ2，1(t)+x3φ2，2(t)+x4φ2，3(t)
wherein phi isj，k(t)＝φ(2jt-k)，k＝0，1，…，2j-1. Let phi (t) be a scaling function, meaning that a function f (t) can be implemented using a scaling function phij，k(t) a linear combination of expansion and contraction and translation. Where j is called the dimension 1/2jReferred to as resolution. If f (t) is regarded as the highest level of decomposition, the decomposition at the upper level can be g (t) ═ a1，0φ1，0(t)+a1，1φ1，1(t) shows that it is easy to find that g (t) ≠ f (t) obtained by solving the average value loses some detail information, and we need another function to describe the information, which is called wavelet function. Similar to the scale function, the wavelet function is defined as follows
ψj，k(t)＝ψ(2jt-k) k＝0，1，…，2j-1
Then
Performing multi-resolution representation on the original signal
f(t)＝x1φ2，0(t)+x2φ2，1(t)+x3φ2，2(t)+x4φ2，3(t)
＝a1，0φ1，0(t)+a1，1φ1，1(t)+d1，0ψ1，0(t)+d1，1ψ1，1(t)
＝a0，0φ0，0(t)+d0，0ψ0，0(t)+d1，0ψ1，0(t)+d1，1ψ1，1(t)
Step two: and analyzing the residual signals by adopting Discrete Wavelet Transform (DWT). First, the original signal is decomposed level by performing convolution calculation on the sequence. Known sequence
And a and b denote a new sequence obtained by convolving a and b. According to the convolution formula, the nth element in a b is (a b)nThen, then
l(a*b)＝l(a)+l(b)-1
The coefficients of the DWT are obtained by a pair of filters and down-sampling at a sampling rate of 2. The pair of filters (h (n), g (n)) is referred to as a filter bank, which includes a low pass h (n) and a high pass g (n) filter. The output of h (n) gives a low resolution approximation of the input signal, the missing details of this approximation being obtained at the output of the high pass filter g (n). The main condition of any wavelet transform is that it must have an inverse transform. To ensure its presence, the filter should satisfy two conditions given in the following equation.
Wherein H (ω) and G (ω) are Fourier transforms of the formulae H (n) and G (n), respectively. The outputs of h (n) and g (n) give the coefficients of the DWT expansion, which can be refined on many scales by repeating the filtering and downsampling operations a number of times at the output of the low-pass filter. The set of expansion coefficients is derived from the detail output and the final approximation output of each layer. It can thus be seen that the filter functions by convolving a fixed sequence of filter coefficients with the input signal.
Step three: carrying out nonlinear threshold processing on the wavelet coefficient, and reserving all low-frequency coefficients vi，k，k＝1，…，2jSo as to ensure that the overall shape of the signal is not changed. Taking a threshold value:
wherein, σ is MAD/0.6745, MAD is the median of the absolute value of the first-layer wavelet decomposition coefficient, 0.6745 is the adjustment coefficient of the standard deviation of gaussian noise, and N is the size or length of the signal. For each wavelet coefficient
Processing by a soft threshold method:
comparing wavelet coefficients containing noise signals with a threshold lambda selected by a formula one by one, and shrinking a point which is not lower than the threshold into a difference between the wavelet coefficients and the threshold lambda; shrinking points that are not more than an inverse number of the threshold to a sum of the two; the point whose value is lower than the threshold is shrunk to 0. Estimation of wavelet coefficients obtained by soft threshold shrinkage
The continuity of the whole is relatively good, the noise interference of high frequency is basically eliminated, and meanwhile, the effective signal of high frequency is kept, so that the estimation signal does not generate additional oscillation. A
Fig. 4 is a block diagram of a configuration of the fuzzy PI adaptive control system, in which an error e and an error change rate ec are input to the system, and a change amount Δ K of a proportional coefficient and an integral coefficientp、ΔKiAs output of the system, for adjusting Kp、KiThe parameters of the 2 outputs are modified on-line according to the 2 inputs. Make itThe method comprises the following steps:
the method comprises the following steps: because the system needs to meet the conditions that the function is easy to realize, the data can be quickly processed and the like in practical application, the triangular membership function is selected as the membership function of the fuzzy PI controller, and the mathematical expression is as follows:
where m, σ are the center and width of the fuzzy set, respectively.
Step two: determining the universe of discourse of the input and output variables and fuzzy subsets thereof, defined as follows: e. ec are all { -3, -3, -1, 0, 1, 2, 3}, Δ Ki、ΔKpThe domain of discourse is {1, 2, 3, 4, 5, 6, 7}, and the fuzzy subsets are { NB, NM, NS, ZO, PS, PM, PB }. And then, designing a membership function, and designing the parameter characteristics of the main reference system and the motor.
Step three: according to the established fuzzy control rule table and membership function value, finding out P, I two correction parameter fuzzy output values outputted by the fuzzy PI controller, and calculating Kp、KiThe value of (c). The input variables e, ec and output variables Δ K are represented by the common 7 linguistic fuzzy setsp、ΔKi: NB (negative large), NM (negative medium), NS (negative small), ZO (zero), PS (positive small), PM (positive medium), PB (positive large). The proportional and integral regulating formula of the controller is shown as the formula (5.25). After adding fuzzy control, real-time improving correction quantity delta K of variable fuzzy controllerp、ΔKiAnd correcting the PI parameters in real time.
Wherein, Kp(0)、Ki(0) Is an initial value of the PI controller, Δ Kp、ΔKiAs a correction of the PI parameter, Kp、KiIs the corrected parameter.
Optionally, KpThe rule control table is as follows:
optionally, KiThe rule control table is as follows:
Claims (6)
1. a rotor position estimation system of a permanent magnet synchronous motor comprises a PI control module, a current sampling module, a space vector pulse width modulation module, a motor driving module and a three-phase stator winding of the permanent magnet synchronous motor, the motor driving module is respectively connected with the space vector pulse width modulation module and the three-phase stator winding of the permanent magnet synchronous motor and is used for starting and maintaining the normal operation of the permanent magnet synchronous motor, the device is characterized by further comprising an improved Luenberger observer driving module, the improved Luenberger observer driving module is respectively connected with the current sampling module and the PI control module, a wavelet de-noising unit is used for reducing noise with larger fixed response factors for a gain K value of the Luenberger observer, a fuzzy parameter setting unit is used for setting PI parameters in the Luenberger observer and a phase-locked loop, therefore, the robustness of the Luenberger observer is improved, and reliable rotor position and rotating speed information is provided for a control system.
2. The permanent magnet synchronous motor rotor position estimation system of claim 1, wherein the modified Luenberger observer drive module comprises: the device comprises a position calculation unit, a rotating speed calculation unit, a wavelet denoising unit and a fuzzy parameter setting unit; the position calculating unit is connected with the coordinate transforming unit and is used forWill iα、iβAnd rotor position
Combining and transforming the two phases of the three-phase images to a two-phase rotating coordinate system; the rotating speed calculating unit is connected with the coordinate transformation unit and is used for obtaining the position information of the permanent magnet synchronous motor; the wavelet denoising unit is used for reducing noise with a large fixed influence factor on the gain K value of the Luenberger observer; the fuzzy parameter setting unit is used for setting the Luenberger observer and the PI parameter in the phase-locked loop.
3. The system of claim 1, wherein the current PI control module comprises: a rotating speed loop PI regulator and a current loop PI regulator; the rotating speed loop PI regulator is connected with a position estimation unit in the improved Luenberger observer driving module and used for calculating the difference value between the given rotating speed and the feedback rotating speed, and a torque current component, namely a quadrature axis reference current i, is generated by the rotating speed loop PI regulatorqref(ii) a The current loop PI regulator is connected with the space vector pulse width modulation module and is used for controlling iqTracing iqref、idTracing idrefObtaining a voltage component ud、uq。
4. A permanent magnet synchronous motor rotor position estimation method is characterized in that a wavelet threshold denoising function is added on the basis of a traditional Luenberger observer, a fuzzy PI controller is adopted to improve a phase-locked loop, and the improved Luenberger observer can obtain more accurate permanent magnet synchronous motor rotor speed omegaeAnd rotor position
5. The method for estimating the rotor position of the permanent magnet synchronous motor according to claim 4, wherein the wavelet threshold denoising function comprises the following specific steps:
the method comprises the following steps: defining an original linear signal:
f(t)＝x1φ2，0(t)+x2φ2，1(t)+x3φ2，2(t)+x4φ2，3(t)
wherein phi isj，k(t)＝φ(2jt-k)，k＝0，1，…，2j-1. Let phi (t) be a scaling function, meaning that a function f (t) can be implemented using a scaling function phij，k(t) a linear combination of expansion and contraction and translation. Where j is called the dimension 1/2jReferred to as resolution. If f (t) is regarded as the highest level of decomposition, the decomposition at the upper level can be g (t) ═ a1，0φ1，0(t)+a1，1φ1，1(t) shows that it is easy to find that g (t) ≠ f (t) obtained by solving the average value loses some detail information, and we need another function to describe the information, which is called wavelet function. Similar to the scale function, the wavelet function is defined as follows
ψj，k(t)＝ψ(2jt-k)k＝0，1，…，2j-1
Then
Performing multi-resolution representation on the above, wherein the original signal is as follows:
f(t)＝x1φ2，0(t)+x2φ2，1(t)+x3φ2，2(t)+x4φ2，3(t)
＝a1，0φ1，0(t)+a1，1φ1，1(t)+d1，0ψ1，0(t)+d1，1ψ1，1(t)
＝a0，0φ0，0(t)+d0，0ψ0，0(t)+d1，0ψ1，0(t)+d1，1ψ1，1(t)
step two: the method comprises the steps of analyzing residual signals by Discrete Wavelet Transform (DWT), and decomposing original signals step by performing convolution calculation on sequences. Known sequence
And a and b denote a new sequence obtained by convolving a and b. According to the convolution formula, the nth element in a b is (a b)nThen, then
l(a*b)＝l(a)+l(b)-1
The coefficients of the DWT are obtained by down-sampling a pair of filters (h (n), g (n)) called a filter bank, with a low pass h (n) and a high pass g (n) filter, and a sampling rate of 2. The output of h (n) gives a low resolution approximation of the input signal, the missing details of this approximation being obtained at the output of the high pass filter g (n). The main condition of any wavelet transform is that it must have an inverse transform, and to ensure its existence, the filter should satisfy two conditions given in the following equation:
wherein the fourier transforms of H (ω) and G (ω) respectively of the formulae H (n) and G (n), the output of H (n) and the output of G (n) giving the coefficients of the DWT expansion, can be refined over many scales by repeating the filtering and down-sampling operations at the output of the low-pass filter a number of times, and by the detail output and the final approximation output of each layer, a set of expansion coefficients is obtained, from which it can be found that the filter functions by convolving a fixed sequence of filter coefficients with the input signal;
step three: the wavelet coefficients are subjected to a non-linear thresholding,retaining all low frequency coefficients vj，k，k＝1，…，2jTo ensure that the overall shape of the signal does not change, a threshold value is taken:
wherein, sigma is MAD/0.6745, MAD is the intermediate value of the absolute value of the first layer wavelet decomposition coefficient, 0.6745 is the adjusting coefficient of the standard variance of Gaussian noise, N is the size or length of the signal, and for each wavelet coefficient
Processing by a soft threshold method:
comparing wavelet coefficients containing noise signals with a threshold lambda selected by a formula one by one, and shrinking a point which is not lower than the threshold into a difference between the wavelet coefficients and the threshold lambda; shrinking points that are not more than an inverse number of the threshold to a sum of the two; the point with the value lower than the threshold value is shrunk to 0, and the obtained wavelet coefficient is estimated by a soft threshold value shrinkage method
The continuity of the whole is relatively good, the noise interference of high frequency is basically eliminated, and meanwhile, the effective signal of high frequency is kept, so that the estimation signal does not generate additional oscillation.
6. The method for estimating the rotor position of the permanent magnet synchronous motor according to claim 4, wherein the fuzzy PI controller comprises the following specific steps:
the method comprises the following steps: because the system needs to meet the conditions that the function is easy to realize, the data can be quickly processed and the like in practical application, the triangular membership function is selected as the membership function of the fuzzy PI controller, and the mathematical expression is as follows:
wherein m and sigma are the center and the width of the fuzzy set respectively;
step two: determining the universe of discourse of the input and output variables and fuzzy subsets thereof, defined as follows: e. ec are all { -3, -3, -1, 0, 1, 2, 3}, Δ Ki、ΔKpThe domain of discourse is {1, 2, 3, 4, 5, 6, 7}, fuzzy subsets are { NB, NM, NS, ZO, PS, PM, PB }, then membership function design is carried out, and main reference system characteristics and parameter characteristics of the motor are designed; (ii) a
Step three: according to the established fuzzy control rule table and membership function value, finding out P, I two correction parameter fuzzy output values outputted by the fuzzy PI controller, and calculating Kp、KiThe values of (a) represent the input variables e, ec and the output variables Δ K with the common set of 7 linguistic ambiguitiesp、ΔKi: NB (negative large), NM (negative medium), NS (negative small), ZO (zero), PS (positive small), PM (positive medium), PB (positive large); the proportional and integral regulation formula of the controller is shown as the formula (5.25), and after the fuzzy control is added, the correction quantity delta K of the variable fuzzy controller is improved in real timep、ΔKiAnd correcting the PI parameters in real time:
wherein, Kp(0)、Ki(0) Is an initial value of the PI controller, Δ Kp、ΔKiAs a correction of the PI parameter, Kp、KiIs the corrected parameter.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113241985A (en) * | 2021-05-26 | 2021-08-10 | 北京航空航天大学 | Current self-correction control device and method for magnetic suspension flywheel without position sensor |
| CN113809969A (en) * | 2021-07-30 | 2021-12-17 | 中国电力科学研究院有限公司 | Synchronous motor rotor position assessment method and device |
| CN117335695A (en) * | 2023-12-01 | 2024-01-02 | 深圳市好盈科技股份有限公司 | Processing method and device for phase-locked loop control parameters for non-inductive FOC control |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9036940B1 (en) * | 2010-10-21 | 2015-05-19 | The Boeing Company | Methods and systems for video noise reduction |
| CN104864274A (en) * | 2015-05-26 | 2015-08-26 | 北京化工大学 | Enhancement method for pipeline leakage acoustic signals |
| CN108444471A (en) * | 2018-05-03 | 2018-08-24 | 天津大学 | A kind of accelerometer signal denoising method based on particle filter and wavelet transformation |
| CN111371356A (en) * | 2020-04-03 | 2020-07-03 | 上海至哲智控技术有限公司 | PMSM rotor observation method based on variable parameter PI control |
| CN111769779A (en) * | 2019-06-11 | 2020-10-13 | 沈阳工业大学 | PMSM direct torque control method based on improved Luenberger observer |
-
2020
- 2020-11-23 CN CN202011325118.3A patent/CN112600473B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9036940B1 (en) * | 2010-10-21 | 2015-05-19 | The Boeing Company | Methods and systems for video noise reduction |
| CN104864274A (en) * | 2015-05-26 | 2015-08-26 | 北京化工大学 | Enhancement method for pipeline leakage acoustic signals |
| CN108444471A (en) * | 2018-05-03 | 2018-08-24 | 天津大学 | A kind of accelerometer signal denoising method based on particle filter and wavelet transformation |
| CN111769779A (en) * | 2019-06-11 | 2020-10-13 | 沈阳工业大学 | PMSM direct torque control method based on improved Luenberger observer |
| CN111371356A (en) * | 2020-04-03 | 2020-07-03 | 上海至哲智控技术有限公司 | PMSM rotor observation method based on variable parameter PI control |
Non-Patent Citations (8)
| Title |
|---|
| M. BOUROGAOUI ET AL.: "An effective encoder fault detection in PMSM drives at different speed ranges", 《2015 IEEE 10TH INTERNATIONAL SYMPOSIUM ON DIAGNOSTICS FOR ELECTRICAL MACHINES, POWER ELECTRONICS AND DRIVES (SDEMPED)》 * |
| M. BOUROGAOUI ET AL.: "An effective encoder fault detection in PMSM drives at different speed ranges", 《2015 IEEE 10TH INTERNATIONAL SYMPOSIUM ON DIAGNOSTICS FOR ELECTRICAL MACHINES, POWER ELECTRONICS AND DRIVES (SDEMPED)》, 26 October 2015 (2015-10-26), pages 90 - 96 * |
| 凌鑫明: "永磁同步电机无位置传感器矢量控制系统的研究与设计", 《中国优秀硕士学位论文全文数据库 工程科技II辑》 * |
| 凌鑫明: "永磁同步电机无位置传感器矢量控制系统的研究与设计", 《中国优秀硕士学位论文全文数据库 工程科技II辑》, no. 6, 15 June 2016 (2016-06-15), pages 042 - 90 * |
| 张宇慧 等: "基于滑模观测器与模糊PID的PMSM无位置传感器控制系统", 《电子测量技术》 * |
| 张宇慧 等: "基于滑模观测器与模糊PID的PMSM无位置传感器控制系统", 《电子测量技术》, vol. 41, no. 5, 31 March 2018 (2018-03-31), pages 6 * |
| 朱高中 等: "一种改进的小波阈值法在信号消噪中的研究", 《继电器》 * |
| 朱高中 等: "一种改进的小波阈值法在信号消噪中的研究", 《继电器》, vol. 35, no. 18, 16 September 2007 (2007-09-16), pages 41 - 45 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113241985A (en) * | 2021-05-26 | 2021-08-10 | 北京航空航天大学 | Current self-correction control device and method for magnetic suspension flywheel without position sensor |
| CN113809969A (en) * | 2021-07-30 | 2021-12-17 | 中国电力科学研究院有限公司 | Synchronous motor rotor position assessment method and device |
| CN113809969B (en) * | 2021-07-30 | 2023-10-20 | 中国电力科学研究院有限公司 | Synchronous motor rotor position evaluation method and device |
| CN117335695A (en) * | 2023-12-01 | 2024-01-02 | 深圳市好盈科技股份有限公司 | Processing method and device for phase-locked loop control parameters for non-inductive FOC control |
| CN117335695B (en) * | 2023-12-01 | 2024-01-30 | 深圳市好盈科技股份有限公司 | Processing method and device for phase-locked loop control parameters for non-inductive FOC control |
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