CN106026817A - Speed sensorless control system based on sliding-mode observer of Kalman filter - Google Patents

Abstract

The invention discloses a speed sensorless control system based on a sliding-mode observer of a Kalman filter. The system comprises an inverter module, a PMSM module, a first Clark transformation module, a Park transformation module, a second Clark transformation module, a Kalman observer module, a first comparator module, a first PI adjusting module, a second comparator module, a second PI adjusting module, a third comparator module, a third PI adjusting module, a Park inverse transformation module and an SVPWM module; the revolving speed and the rotor position are estimated by adopting the sliding-mode observer of the Kalman filter, and speed regulation of the motor is controlled by estimating the rotor position and the rotor speed. According to the speed sensorless control system, the rotor position and the revolving speed information of the motor are acquired by replacing a mechanical sensor with a speed sensorless control algorithm, therefore, errors in closed loop feedback information are reduced, the calculation amount of a sliding-mode observer control method is decreased, and the system is easily achieved on engineering.

Description

A kind of Speedless sensor control based on the sliding mode observer using Kalman filter System processed

Technical field

The present invention relates to Speedless sensor velocity measuring technique field, particularly to a kind of based on using Kalman filter The senseless control system of sliding mode observer.

Background technology

Permagnetic synchronous motor because of its compact conformation, dependable performance and in fields such as wind-power electricity generation, electric automobile, boats and ships drivings It is widely used.Because the control of permagnetic synchronous motor generally completes under rotor rotating coordinate system, so in order to complete The control of permagnetic synchronous motor, needs to obtain the angle of its rotor and speed.Wherein, angle and velocity sensor is used to obtain This information is a kind of directly mode, but in many applications, setting angle and velocity sensor add installation, safeguard Cost, simultaneously because site environment is more severe, the precision of sensor is easily subject to vibrations, dust and the impact of greasy dirt so that System is easily disturbed by external environment condition, reduces the reliability of system.

The control system of Speedless sensor, without detecting hardware, eliminates all troubles that velocity sensor brings, carries The high reliability of system, reduces the cost of system；On the other hand so that the volume of system reduces, weight, and subtracts Lack the line of motor and controller.And the rotor angle of permagnetic synchronous motor of based on Speedless sensor, speed estimate side Method only need to detect the stator current of motor, voltage, in conjunction with the model of motor, can therefrom extract angle and the speed letter of rotor Breath, thus eliminate angle and velocity sensor, reach to improve the reliability of system, reduce the purpose of cost.

Summary of the invention

In order to overcome deficiency of the prior art, the present invention propose a kind of be prone to Project Realization based on use Kalman The senseless control system of the sliding mode observer of wave filter estimates position and the spinner velocity of rotor, and for vector Control in closed loop system, it is to avoid the information that mechanical pick-up device provides under the working environment that some are special is inaccurate.

In order to reach foregoing invention purpose, solve the technical scheme that its technical problem used as follows:

A kind of senseless control system based on the sliding mode observer using Kalman filter, including inverter Module, PMSM module, a Clark conversion module, Park conversion module, the 2nd Clark conversion module, Kalman's observer mould Block, the first comparator module, a PI adjustment module, the second comparator module, the 2nd PI adjustment module, the 3rd comparator mould Block, the 3rd PI adjustment module, Park inverse transform block and SVPWM module, wherein:

Described PMSM module, is used for detecting output three-phase current I_a、I_bAnd I_c；

A described Clark conversion module, for the three-phase current I described PMSM module exported_a、I_bAnd I_cPass through The biphase stator current i under biphase static rectangular coordinate system alpha-beta is exported after Clark conversion_αAnd i_β；

Described Park conversion module, for the biphase stator current i by a described Clark conversion module output_αAnd i_βLogical The biphase current I under biphase synchronous rotating frame d-q is exported after crossing Park conversion_dAnd I_q；

Described 2nd Clark conversion module, for the three-phase voltage U exported by described inverter module_a、U_bAnd U_cPass through Biphase stator voltage u under biphase static rectangular coordinate system alpha-beta is exported after Clark conversion_αAnd u_β；

Described Kalman's observer module, for the biphase stator current i by a described Clark conversion module output_α And i_βBiphase stator voltage u with described 2nd Clark conversion module output_αAnd u_βCarry out estimation process, estimate rotor speed Estimated valueEstimated value with rotor-position

Described first comparator module, for estimating the estimated value of rotor speed in described Kalman's observer moduleIt is multiplied by a constant and obtains rotor speed n of estimation, and rotor speed n of estimation is made with actual rotor speed n* Difference operation；

A described PI adjustment module, is exported after the difference that described first comparator module compares being regulated by PI Q axle reference current

Described second comparator module, for exporting q axle reference current after a described PI adjustment module regulationWith The biphase current I of described Park conversion module output_qCarry out making difference operation；

Described 2nd PI adjustment module, is exported after the difference that described second comparator module compares being regulated by PI Q axle reference voltage

Described 3rd comparator module, for by d axle reference currentElectric current I with the output of described Park conversion module_dEnter Row makees difference operation；

Described 3rd PI adjustment module, is exported after the difference that described 3rd comparator module compares being regulated by PI D axle reference voltage

Described Park inverse transform block, for the q axle reference voltage by described 2nd PI adjustment module outputWith described The d axle reference voltage of the 3rd PI adjustment module outputBy exporting after Park inverse transformation under biphase static rectangular coordinate system alpha-beta Two phase control voltagesWith

Described SVPWM module, for by two phase control voltagesWithCarry out space vector pulse width modulation, export PWM ripple Shape inputs three-phase voltage U to described inverter module, described inverter module to described PMSM module_a、U_bAnd U_c, thus control Described PMSM module.

Concrete, described Kalman's observer module specifically includes SMO optimized algorithm submodule, the 4th comparator submodule Block, saturation function calculating sub module, sliding formwork gain submodule, low pass filter submodule, Kalman filter submodule, rotating speed Estimation submodule, position compensation submodule, position estimation submodule and summation module, wherein:

Described SMO optimized algorithm submodule, for biphase stator voltage u by described 2nd Clark conversion module output_α And u_βThe counter electromotive force e of output after processing with described sliding formwork gain module_αAnd e_βElectric current is exported after SMO optimized algorithm calculates Estimated valueWith

Described 4th comparator submodule, for the electric current estimated value exported by described SMO optimized algorithm submoduleWith Biphase stator current i with a described Clark conversion module output_αAnd i_βCarry out making difference operation, obtain the electric current on α β axle by mistake DifferenceWith

Described saturation function calculating sub module, for by the electric current on the α β axle of described 4th comparator submodule output by mistake DifferenceWithCounter electromotive force e is obtained after saturation function computing and described sliding formwork gain module process_αAnd e_β；

Described low pass filter submodule, the counter electromotive force e of output after described sliding formwork gain module is processed_αAnd e_β By obtaining the counter electromotive force estimated value of sliding mode observer estimation after low-pass filteringWith

Described Kalman filter submodule, for the sliding formwork that will obtain after described low pass filter submodule low-pass filtering The counter electromotive force estimated value of observer estimationWithThe anti-electricity after Kalman filtering has been obtained after Kalman filtering Kinetic potential estimated valueWith

Described turn count submodule, for being passed through after described Kalman filter submodule Kalman filtering Counter electromotive force estimated value after Kalman filteringWithThe estimated value of rotor speed is obtained by turn count

Described position estimation submodule, for being passed through after described Kalman filter submodule Kalman filtering Counter electromotive force estimated value after Kalman filteringWithThe estimated value before rotor-position does not compensates is obtained by position estimation

Described position compensation submodule, for by phase place is carried out lag compensation, draws after Kalman filtering Phase compensation amount

Described summation module, the estimated value before the rotor-position that described position estimation submodule obtains is not compensated The phase compensation amount obtained with described position compensation submoduleSue for peace, obtain the estimated value of rotor-position

As an embodiment, the SMO optimized algorithm in described SMO optimized algorithm submodule specifically includes step calculated below Rapid:

First, AC permanent magnet synchronous motor mathematical model in biphase static rectangular coordinate system alpha-beta is set up:

$\stackrel{\·}{i_{\mathrm{\α}}}=-\frac{R_s}{L_s}{i}_{\mathrm{\α}}-\frac{1}{L_s}{e}_{\mathrm{\α}}+\frac{u_{\mathrm{\α}}}{L_s}---\left(1\right)$

$\stackrel{\·}{i_{\mathrm{\β}}}=-\frac{R_s}{L_s}{i}_{\mathrm{\β}}-\frac{1}{L_s}{e}_{\mathrm{\β}}+\frac{u_{\mathrm{\β}}}{L_s}---\left(2\right)$

Wherein,For electric current i current value i on α axle_αDerivative,For electric current i current value i on β axle_βLead Number, R_SFor stator winding resistance, Ls is equivalent inductance, e_αFor sliding mode observer counter electromotive force on α axle, e_βObserve for sliding formwork Device counter electromotive force on β axle, u_αFor voltage U magnitude of voltage on α axle, u_βFor voltage U magnitude of voltage on β axle；

Secondly, counter electromotive force equation is substituted into:

e_α=-ψ_fω_rsinθ (3)

e_β=ψ_fω_rcosθ (4)

Wherein, ψ_fThe magnetic linkage produced for permanent magnet on rotor, ω_rFor synchronous rotational speed, θ is rotor angle location；

Furthermore, AC permanent magnet synchronous motor SMO in biphase static rectangular coordinate system alpha-beta optimizes accounting equation and is:

$\stackrel{\·}{\widehat{i_{\mathrm{\α}}}}=-\frac{R_s}{L_s}\widehat{i_{\mathrm{\α}}}+\frac{u_{\mathrm{\α}}}{L_s}-\frac{k}{L_s}sat(\widehat{i_{\mathrm{\α}}}-i_{\mathrm{\α}})---\left(5\right)$

$\stackrel{\·}{\widehat{i_{\mathrm{\β}}}}=-\frac{R_s}{L_s}\widehat{i_{\mathrm{\β}}}+\frac{u_{\mathrm{\β}}}{L_s}-\frac{k}{L_s}sat(\widehat{i_{\mathrm{\β}}}-i_{\mathrm{\β}})---\left(6\right)$

Wherein,It is respectively i_α、i_βEstimated value, k is sliding formwork handoff gain；

Finally, by the above-mentioned current estimation error equation that obtains:

$\stackrel{\·}{\widetilde{i_{\mathrm{\α}}}}=-\frac{R_s}{L_s}\widetilde{i_{\mathrm{\α}}}+\frac{e_{\mathrm{\α}}}{L_s}-\frac{k}{L_s}sat\left(\widetilde{i_{\mathrm{\α}}}\right)---\left(7\right)$

$\stackrel{\·}{\widetilde{i_{\mathrm{\β}}}}=-\frac{R_s}{L_s}\widetilde{i_{\mathrm{\β}}}+\frac{e_{\mathrm{\β}}}{L_s}-\frac{k}{L_s}sat\left(\widetilde{i_{\mathrm{\β}}}\right)---\left(8\right)$

Wherein,For the current error value on α axle,For the current error value on β axle.

As an embodiment, the current error value in described 4th comparator submoduleWithAccounting equation be:

$\widetilde{i_{\mathrm{\α}}}={\hat{i}}_{\mathrm{\α}}-i_{\mathrm{\α}}---\left(9\right)$

$\widetilde{i_{\mathrm{\β}}}={\hat{i}}_{\mathrm{\β}}-i_{\mathrm{\β}}---\left(10\right)$

Wherein,And i_αFor the current error value on α axle, electric current estimated value and current value,And i_βFor β axle On current error value, electric current estimated value and current value.

As an embodiment, the counter electromotive force e in described saturation function calculating sub module_αAnd e_βCalculating process wrap respectively Include following steps:

First, choosing sat is that saturation function carries out saturation function computing, it may be assumed that

$sat=\left\{\begin{array}{cc}1& x>h\\ x/h& \left|x\right|\&le;h\\ -1& x<h\end{array}\right.---\left(11\right)$

Secondly, liapunov function is chosen:To V derivation, work as k > max (| e_α|,|e_β|) time, thenV ＞ 0, is known by Lyapunov theorem of stability, and electric current sliding mode observer is stable；

Furthermore, choosing current error is sliding formwork diverter surface, then, when entering sliding mode, haveWithTime,

$e_{\mathrm{\&alpha;}}=ksat\left(\widetilde{i_{\mathrm{\&alpha;}}}\right)---\left(12\right)$

$e_{\mathrm{\&beta;}}=ksat\left(\widetilde{i_{\mathrm{\&beta;}}}\right)---\left(13\right)$

Wherein, e_αAnd e_βFor the counter electromotive force of sliding mode observer,For the current error value on α axle,For the electricity on β axle Stream error value, k is sliding formwork handoff gain.

As an embodiment, described low pass filter submodule obtains sliding mode observer estimation by low pass filter Counter electromotive force estimated valueWithCalculating process include:

Using low pass filter, discontinuous switching signal is converted to the continuous signal of equivalence, corresponding computing formula is such as Under:

${\hat{z}}_{\mathrm{\&alpha;}}=\frac{{\mathrm{\&omega;}}_c}{s+{\mathrm{\&omega;}}_c}{e}_{\mathrm{\&alpha;}}---\left(14\right)$

${\hat{z}}_{\mathrm{\&beta;}}=\frac{{\mathrm{\&omega;}}_c}{s+{\mathrm{\&omega;}}_c}{e}_{\mathrm{\&beta;}}---\left(15\right)$

Wherein,WithFor the counter electromotive force estimated value of sliding mode observer estimation, ω_cCutoff frequency for low pass filter Rate, s is Laplace operator, e_αAnd e_βCounter electromotive force for sliding mode observer.

As an embodiment, described low pass filter submodule also includes step calculated below:

First, the estimated value of rotor-position is tried to achieve by below equation:

$\widehat{\mathrm{\&theta;}}=-\arctan \left(\frac{\widehat{z_{\mathrm{\&alpha;}}}}{\widehat{z_{\mathrm{\&beta;}}}}\right)---\left(16\right)$

Wherein,For the estimated value of rotor-position,WithCounter electromotive force for sliding mode observer estimation；

Secondly as the use of low pass filter, its phase place has certain hysteresis quality, phase place must be carried out delayed benefit Repaying, its phase compensation amount is:

$\mathrm{\&Delta;}\widehat{\mathrm{\&theta;}}=-\arctan \left(\frac{\mathrm{\&omega;}}{{\mathrm{\&omega;}}_c}\right)---\left(17\right)$

Wherein,It is phase compensation amount, rotating speed when ω is stable state, ω_cCut-off frequency for low pass filter；

Furthermore, the estimated value of rotor speed is tried to achieve by below equation:

$\widehat{\mathrm{\&omega;}}=\frac{\sqrt{{\widehat{z_{\mathrm{\&alpha;}}}}^2+{\widehat{z_{\mathrm{\&beta;}}}}^2}}{{\mathrm{\&psi;}}_f}---\left(18\right)$

Wherein,For rotor speed estimated value,WithFor the counter electromotive force of sliding mode observer estimation, ψ_fFor on rotor forever The magnetic linkage that magnet produces.

As an embodiment, described Kalman filter submodule use Kalman filter will obtainWithFilter Counter electromotive force after rippleWithObtaining optimum observation from random noise signal, the state equation of Kalman filter is as follows:

${\stackrel{g}{\hat{e}}}_{\mathrm{\&alpha;}}=-{\widehat{\mathrm{\&omega;}}}_e{\hat{e}}_{\mathrm{\&beta;}}-K_k({\hat{e}}_{\mathrm{\&alpha;}}-{\hat{z}}_{\mathrm{\&alpha;}})---\left(19\right)$

${\stackrel{g}{\hat{e}}}_{\mathrm{\&beta;}}={\widehat{\mathrm{\&omega;}}}_e{\hat{e}}_{\mathrm{\&alpha;}}-K_k({\hat{e}}_{\mathrm{\&beta;}}-{\hat{z}}_{\mathrm{\&beta;}})---\left(20\right)$

${\stackrel{g}{\widehat{\mathrm{\&omega;}}}}_e=({\hat{e}}_{\mathrm{\&alpha;}}-{\hat{z}}_{\mathrm{\&alpha;}}){\hat{e}}_{\mathrm{\&beta;}}-({\hat{e}}_{\mathrm{\&beta;}}-{\hat{z}}_{\mathrm{\&beta;}}){\hat{e}}_{\mathrm{\&alpha;}}---\left(21\right)$

Wherein, K_kFor the gain of Kalman filter,For the rotor angular rate estimated value of Kalman filter,WithFor counter electromotive force e_αAnd e_βThe counter electromotive force estimated value of sliding mode observer estimation is obtained by low pass filter,WithFor cunning The counter electromotive force estimated value of mould observer estimationWithObtained after Kalman filtering after Kalman filtering is anti- Electromotive force estimated value.

As an embodiment, in described turn count submodule, the estimated value of rotor speed after Kalman filtering is led to Cross below equation to try to achieve:

${\widehat{\mathrm{\&omega;}}}_K=\frac{\sqrt{{{\hat{e}}^2}_{\mathrm{\&alpha;}}+{{\hat{e}}^2}_{\mathrm{\&beta;}}}}{{\mathrm{\&psi;}}_f}---\left(22\right)$

Wherein,For the rotor speed estimated value after Kalman filtering,WithFor after Kalman filtering The counter electromotive force of sliding mode observer estimation, ψ_fThe magnetic linkage produced for permanent magnet on rotor.

As an embodiment, in described position estimation submodule, the estimated value of rotor-position after Kalman filtering is led to Cross below equation to try to achieve:

${\widehat{\mathrm{\&theta;}}}_{Kc}=-\arctan \left(\frac{{\hat{e}}_{\mathrm{\&alpha;}}}{{\hat{e}}_{\mathrm{\&beta;}}}\right)---\left(23\right)$

Wherein,For the estimated value of the rotor-position after Kalman filtering,WithFor after Kalman filtering Sliding mode observer estimation counter electromotive force.

As an embodiment, in described position compensation submodule, due to the use of low pass filter, its phase place has necessarily Hysteresis quality, phase place must be carried out lag compensation, the phase compensation amount after Kalman filtering is:

$\mathrm{\&Delta;}{\widehat{\mathrm{\&theta;}}}_K=-\arctan \left(\frac{\mathrm{\&omega;}}{{\mathrm{\&omega;}}_c}\right)---\left(24\right)$

Wherein,It is phase compensation amount, rotating speed when ω is stable state, ω_cCut-off frequency for low pass filter.

Due to the fact that the above technical scheme of employing, be allowed to compared with prior art, have the following advantages that and actively imitate Really:

1, a kind of senseless control system based on the sliding mode observer using Kalman filter of the present invention, real Show the high accuracy senseless control of permagnetic synchronous motor, instead of traditional mechanical pick-up device, decrease system Volume and cost, add the reliability of system, and extend the range of application of permagnetic synchronous motor；

2, a kind of senseless control system based on the sliding mode observer using Kalman filter of the present invention, energy The high frequency that effectively suppression synovial membrane variable-structure control introduces is buffeted, and has Sliding mode variable structure control simultaneously concurrently and responds rapidly, without system The advantages such as identification and the anti-random disturbances of EKF and noise immune is strong, can the advantage such as real-time parameter renewal；

3, the present invention is the highest to the required precision of the mathematical model of control system for permanent-magnet synchronous motor, the most true to systematic parameter Qualitative, external disturbance has adaptivity and stronger robustness, has excellent dynamic and static in permagnetic synchronous motor control Step response；

4, the Kalman filter in the present invention is not only to the estimation error caused due to parameter of electric machine error, has very well Elimination effect, and the ripple component in counter electromotive force can be filtered, there is stronger robustness so that permagnetic synchronous motor Control system have more preferable steady state effect and dynamic response；

5, the present invention have that low cost, control algolithm be simple, rotating speed and the estimated speed of position and precision advantages of higher.

Accompanying drawing explanation

In order to be illustrated more clearly that the technical scheme of the embodiment of the present invention, required use in embodiment being described below Accompanying drawing be briefly described.It is clear that the accompanying drawing in describing below is only some embodiments of the present invention, for ability From the point of view of field technique personnel, on the premise of not paying creative work, it is also possible to obtain other accompanying drawing according to these accompanying drawings.Attached In figure:

Fig. 1 is a kind of senseless control system based on the sliding mode observer using Kalman filter of the present invention The motor process figure of middle Sliding Mode Variable Structure System；

Fig. 2 is a kind of senseless control system based on the sliding mode observer using Kalman filter of the present invention Middle senseless control block diagram；

Fig. 3 is a kind of senseless control system based on the sliding mode observer using Kalman filter of the present invention Middle Kalman's Observer Structure figure；

Fig. 4 is a kind of senseless control system based on the sliding mode observer using Kalman filter of the present invention Corresponding system emulation figure；

Fig. 5 is a kind of senseless control system based on the sliding mode observer using Kalman filter of the present invention Simulation waveform figure during medium speed sudden change；

Fig. 6 is a kind of senseless control system based on the sliding mode observer using Kalman filter of the present invention Simulation waveform figure during middle torque sudden change.

[primary symbols labelling]

1-inverter module；

2-PMSM module；

3-the oneth Clark conversion module；

4-Park conversion module；

5-the 2nd Clark conversion module；

6-Kalman's observer module；

7-the first comparator module；

8-the oneth PI adjustment module；

9-the second comparator module；

10-the 2nd PI adjustment module；

11-the 3rd comparator module；

12-the 3rd PI adjustment module；

13-Park inverse transform block；

14-SVPWM module；

61-SMO optimized algorithm submodule；

62-the 4th comparator submodule；

63-saturation function calculating sub module；

64-sliding formwork gain submodule；

65-low pass filter submodule；

66-Kalman filter submodule；

67-turn count submodule；

68-position compensation submodule；

69-position estimation submodule；

610-summation module.

Detailed description of the invention

Below with reference to the accompanying drawing of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete description And discussion, it is clear that a part of example of the only present invention as described herein, is not whole examples, based on the present invention In embodiment, the every other enforcement that those of ordinary skill in the art are obtained on the premise of not making creative work Example, broadly falls into protection scope of the present invention.

See Fig. 1, the situation that consideration is general now in patent of the present invention, there is diverter surface s (x)=s (x₁, x₂,···,x_n)=0, it is by x=f (x) (x ∈ Rⁿ) state space of this system is divided into upper and lower two parts s>0 and s<0. As it is shown in figure 1, there is the motor point of 3 kinds of situations on diverter surface.Point A is usual point, when arriving near diverter surface s=0, and motion Point passes through an A and mistake；Point B is starting point, and when arriving near diverter surface s=0, a B is left from diverter surface both sides in motor point；Point C is terminating point, and when arriving near diverter surface s=0, motor point levels off to a C from diverter surface both sides.

In sliding moding structure, terminating point has special meaning, and what meaning starting point and usual point do not have substantially. When being all terminating point in a certain section of region on diverter surface, the motor point when, and will be at this once trend towards this region Move in region.Now, this region is called " sliding mode " district i.e. " sliding formwork " district, and the system motion in this region is called " sliding formwork Motion ".

In conjunction with Fig. 2, the invention discloses a kind of speed sensorless based on the sliding mode observer using Kalman filter Device control system, including inverter module 1, PMSM (Permanent Magnet Synchronous Motor, permanent magnet synchronous electric Machine) module the 2, the oneth Clark conversion module 3, Park conversion module the 4, the 2nd Clark conversion module 5, Kalman's observer module 6, first comparator module the 7, the oneth PI adjustment module the 8, second comparator module the 9, the 2nd PI adjustment module the 10, the 3rd comparator Module the 11, the 3rd PI adjustment module 12, Park inverse transform block 13 and SVPWM (Space Vector Pulse Width Modulation, space vector pulse width modulation) module 14, wherein:

Described PMSM module 2, is used for detecting output three-phase current I_a、I_bAnd I_c；

A described Clark conversion module 3, for the three-phase current I described PMSM module 2 exported_a、I_bAnd I_cPass through The biphase stator current i under biphase static rectangular coordinate system alpha-beta is exported after Clark conversion_αAnd i_β；

Described Park conversion module 4, for the biphase stator current i exported by a described Clark conversion module 3_αAnd i_β The biphase current I under biphase synchronous rotating frame d-q is exported by Park after being converted_dAnd I_q；

Described 2nd Clark conversion module 5, for the three-phase voltage U exported by described inverter module 1_a、U_bAnd U_cWarp Biphase stator voltage u under biphase static rectangular coordinate system alpha-beta is exported after crossing Clark conversion_αAnd u_β；

Described Kalman's observer module 6, for the biphase stator current exported by a described Clark conversion module 3 i_αAnd i_βBiphase stator voltage u with described 2nd Clark conversion module 5 output_αAnd u_βCarry out estimation process, estimate rotor The estimated value of rotating speedEstimated value with rotor-position

Described first comparator module 7, for estimating the estimation of rotor speed in described Kalman's observer module 6 ValueIt is multiplied by a constant and obtains rotor speed n of estimation, and rotor speed n of estimation is made with actual rotor speed n* Difference operation；

A described PI adjustment module 8, defeated after the difference that described first comparator module 7 compares is regulated by PI Go out q axle reference current

Described second comparator module 9, exports q axle reference current after a described PI adjustment module 8 being regulated Biphase current I with the output of described Park conversion module 4_qCarry out making difference operation；

Described 2nd PI adjustment module 10, after regulating the difference that described second comparator module 9 compares by PI Output q axle reference voltage

Described 3rd comparator module 11, for by d axle reference currentElectric current with the output of described Park conversion module 4 I_dCarry out making difference operation；

Described 3rd PI adjustment module 12, after regulating the difference that described 3rd comparator module 11 compares by PI Output d axle reference voltage

Described Park inverse transform block 13, for the q axle reference voltage described 2nd PI adjustment module 10 exportedWith The d axle reference voltage of described 3rd PI adjustment module 12 outputBy exporting biphase static rectangular coordinate system after Park inverse transformation Two phase control voltages under alpha-betaWith

Described SVPWM module 14, for by two phase control voltagesWithCarry out space vector pulse width modulation, export PWM Waveform is to described inverter module 1, and described inverter module 1 inputs three-phase voltage U to described PMSM module 2_a、U_bAnd U_c, thus Control described PMSM module 2.

In a described Clark conversion module 3, by three-phase current I_a、I_bAnd I_cConvert through Clark, export biphase quiet The only biphase stator current i under rectangular coordinate system alpha-beta_αAnd i_βThe reduction formula being specifically related to is as follows:

$\left[\begin{array}{c}{i}_{\mathrm{\&alpha;}}\\ i_{\mathrm{\&beta;}}\end{array}\right]=\frac{2}{3}\left[\begin{array}{ccc}1& -1/2& -1/2\\ 0& \sqrt{3}/2& -\sqrt{3}/2\end{array}\right]\left[\begin{array}{c}{i}_a\\ i_b\\ i_c\end{array}\right]$

In described Park conversion module 4, by biphase stator current i_αAnd i_βConvert through Park, export two synchronised rotations Turn the biphase current I under coordinate system d-q_dAnd I_qThe reduction formula being specifically related to is as follows:

$\left[\begin{array}{c}{I}_d\\ I_q\end{array}\right]=\left[\begin{array}{cc}\cos \widehat{\mathrm{\&theta;}}& \sin \widehat{\mathrm{\&theta;}}\\ -\sin \widehat{\mathrm{\&theta;}}& \cos \widehat{\mathrm{\&theta;}}\end{array}\right]\left[\begin{array}{c}{i}_{\mathrm{\&alpha;}}\\ i_{\mathrm{\&beta;}}\end{array}\right]$

Wherein,Rotor angle for estimation.

In described 2nd Clark conversion module 5, the three-phase voltage U that described inverter module 1 is exported_a、U_bAnd U_cWarp Cross Clark conversion, export biphase stator voltage u under biphase static rectangular coordinate system alpha-beta_αAnd u_βThe reduction formula being specifically related to As follows:

$\left[\begin{array}{c}{u}_{\mathrm{\&alpha;}}\\ u_{\mathrm{\&beta;}}\end{array}\right]=\frac{2}{3}\left[\begin{array}{ccc}1& -1/2& -1/2\\ 0& \sqrt{3}/2& -\sqrt{3}/2\end{array}\right]\left[\begin{array}{c}{U}_a\\ U_b\\ U_c\end{array}\right]$

Further, in conjunction with Fig. 3, described Kalman's observer module 6 specifically includes SMO (Sliding mode Observer, sliding mode observer) optimized algorithm submodule the 61, the 4th comparator submodule 62, saturation function calculating sub module 63, Sliding formwork gain submodule 64, low pass filter submodule 65, Kalman filter submodule 66, turn count submodule 67, position Put compensation submodule 68, position estimation submodule 69 and summation module 610, wherein:

Described SMO optimized algorithm submodule 61, for the biphase stator electricity exported by described 2nd Clark conversion module 5 Pressure u_αAnd u_βThe counter electromotive force e of output after processing with described sliding formwork gain module 64_αAnd e^βThrough SMO optimized algorithm calculate after defeated Go out electric current estimated valueWith

Described 4th comparator submodule 62, for the electric current estimated value exported by described SMO optimized algorithm submodule 61WithBiphase stator current i with described Clark conversion module 3 output_αAnd i_βCarry out making difference operation, obtain on α β axle Current error valueWith

Described saturation function calculating sub module 63, the electricity on the α β axle that described 4th comparator submodule 62 is exported Stream error valueWithCounter electromotive force e is obtained after saturation function computing and described sliding formwork gain module 64 process_αAnd e_β；

Described low pass filter submodule 65, the counter electromotive force e of output after described sliding formwork gain module 64 is processed_α And e_βBy obtaining the counter electromotive force estimated value of sliding mode observer estimation after low-pass filteringWith

Described Kalman filter submodule 66, for obtaining after described low pass filter submodule 65 low-pass filtering The counter electromotive force estimated value of sliding mode observer estimationWithObtain after Kalman filtering after Kalman filtering Counter electromotive force estimated valueWith

Described turn count submodule 67, for obtaining after described Kalman filter submodule 66 Kalman filtering Counter electromotive force estimated value after Kalman filteringWithThe estimated value of rotor speed is obtained by turn count

Described position estimation submodule 69, for obtaining after described Kalman filter submodule 66 Kalman filtering Counter electromotive force estimated value after Kalman filteringWithThe estimation before rotor-position does not compensates is obtained by position estimation Value

Described position compensation submodule 68, for by phase place is carried out lag compensation, draws after Kalman filtering Phase compensation amount

Described summation module 610, estimating before the rotor-position that described position estimation submodule 69 obtains is not compensated EvaluationThe phase compensation amount obtained with described position compensation submodule 68Sue for peace, obtain the estimation of rotor-position Value

As an embodiment, the SMO optimized algorithm in described SMO optimized algorithm submodule 61 specifically includes step calculated below Rapid:

First, AC permanent magnet synchronous motor mathematical model in biphase static rectangular coordinate system alpha-beta is set up:

$\stackrel{\&CenterDot;}{i_{\mathrm{\&alpha;}}}=-\frac{R_s}{L_s}{i}_{\mathrm{\&alpha;}}-\frac{1}{L_s}{e}_{\mathrm{\&alpha;}}+\frac{u_{\mathrm{\&alpha;}}}{L_s}---\left(1\right)$

$\stackrel{\&CenterDot;}{i_{\mathrm{\&beta;}}}=-\frac{R_s}{L_s}{i}_{\mathrm{\&beta;}}-\frac{1}{L_s}{e}_{\mathrm{\&beta;}}+\frac{u_{\mathrm{\&beta;}}}{L_s}---\left(2\right)$

Wherein,For electric current i current value i on α axle_αDerivative,For electric current i current value i on β axle_βLead Number, R_SFor stator winding resistance, Ls is equivalent inductance, e_αFor sliding mode observer counter electromotive force on α axle, e_βObserve for sliding formwork Device counter electromotive force on β axle, u_αFor voltage U magnitude of voltage on α axle, u_βFor voltage U magnitude of voltage on β axle；

Secondly, counter electromotive force equation is substituted into:

e_α=-ψ_fω_rsinθ (3)

e_β=ψ_fω_rcosθ (4)

Wherein, ψ_fThe magnetic linkage produced for permanent magnet on rotor, ω_rFor synchronous rotational speed, θ is rotor angle location；

Furthermore, AC permanent magnet synchronous motor SMO in biphase static rectangular coordinate system alpha-beta optimizes accounting equation and is:

$\stackrel{\&CenterDot;}{\widehat{i_{\mathrm{\&alpha;}}}}=-\frac{R_s}{L_s}\widehat{i_{\mathrm{\&alpha;}}}+\frac{u_{\mathrm{\&alpha;}}}{L_s}-\frac{k}{L_s}sat(\widehat{i_{\mathrm{\&alpha;}}}-i_{\mathrm{\&alpha;}})---\left(5\right)$

$\stackrel{\&CenterDot;}{\widehat{i_{\mathrm{\&beta;}}}}=-\frac{R_s}{L_s}\widehat{i_{\mathrm{\&beta;}}}+\frac{u_{\mathrm{\&beta;}}}{L_s}-\frac{k}{L_s}sat(\widehat{i_{\mathrm{\&beta;}}}-i_{\mathrm{\&beta;}})---\left(6\right)$

Wherein,It is respectively i_α、i_βEstimated value, k is sliding formwork handoff gain；

Finally, by the above-mentioned current estimation error equation that obtains:

$\stackrel{\&CenterDot;}{\widetilde{i_{\mathrm{\&alpha;}}}}=-\frac{R_s}{L_s}\widetilde{i_{\mathrm{\&alpha;}}}+\frac{e_{\mathrm{\&alpha;}}}{L_s}-\frac{k}{L_s}sat\left(\widetilde{i_{\mathrm{\&alpha;}}}\right)---\left(7\right)$

$\stackrel{\&CenterDot;}{\widetilde{i_{\mathrm{\&beta;}}}}=-\frac{R_s}{L_s}\widetilde{i_{\mathrm{\&beta;}}}+\frac{e_{\mathrm{\&beta;}}}{L_s}-\frac{k}{L_s}sat\left(\widetilde{i_{\mathrm{\&beta;}}}\right)---\left(8\right)$

Wherein,For the current error value on α axle,For the current error value on β axle.

As an embodiment, the current error value in described 4th comparator submodule 62WithAccounting equation be:

$\widetilde{i_{\mathrm{\&alpha;}}}={\hat{i}}_{\mathrm{\&alpha;}}-i_{\mathrm{\&alpha;}}---\left(9\right)$

$\widetilde{i_{\mathrm{\&beta;}}}={\hat{i}}_{\mathrm{\&beta;}}-i_{\mathrm{\&beta;}}---\left(10\right)$

Wherein,And i_αFor the current error value on α axle, electric current estimated value and current value,And i_βFor β axle On current error value, electric current estimated value and current value.

As an embodiment, the counter electromotive force e in described saturation function calculating sub module 63_αAnd e_βCalculating process respectively Comprise the following steps:

First, choosing sat is that saturation function carries out saturation function computing, it may be assumed that

$sat=\left\{\begin{array}{cc}1& x>h\\ x/h& \left|x\right|\&le;h\\ -1& x<h\end{array}\right.---\left(11\right)$

Secondly, liapunov function is chosen:To V derivation, work as k > max (| e_α|,|e_β|) time, thenV ＞ 0, is known by Lyapunov theorem of stability, and electric current sliding mode observer is stable；

Furthermore, choosing current error is sliding formwork diverter surface, then, when entering sliding mode, haveWithTime,

$e_{\mathrm{\&alpha;}}=ksat\left(\widetilde{i_{\mathrm{\&alpha;}}}\right)---\left(12\right)$

$e_{\mathrm{\&beta;}}=ksat\left(\widetilde{i_{\mathrm{\&beta;}}}\right)---\left(13\right)$

Wherein, e_αAnd e_βFor the counter electromotive force of sliding mode observer,For the current error value on α axle,For the electricity on β axle Stream error value, k is sliding formwork handoff gain.

As an embodiment, described low pass filter submodule 65 obtains sliding mode observer by low pass filter and estimates Counter electromotive force estimated valueWithCalculating process include:

Using low pass filter, discontinuous switching signal is converted to the continuous signal of equivalence, corresponding computing formula is such as Under:

${\hat{z}}_{\mathrm{\&alpha;}}=\frac{{\mathrm{\&omega;}}_c}{s+{\mathrm{\&omega;}}_c}{e}_{\mathrm{\&alpha;}}---\left(14\right)$

${\hat{z}}_{\mathrm{\&beta;}}=\frac{{\mathrm{\&omega;}}_c}{s+{\mathrm{\&omega;}}_c}{e}_{\mathrm{\&beta;}}---\left(15\right)$

Wherein,WithFor the counter electromotive force estimated value of sliding mode observer estimation, ω_cCutoff frequency for low pass filter Rate, s is Laplace operator, e_αAnd e_βCounter electromotive force for sliding mode observer.

As an embodiment, described low pass filter submodule 65 also includes step calculated below:

First, the estimated value of rotor-position is tried to achieve by below equation:

$\widehat{\mathrm{\&theta;}}=-\arctan \left(\frac{\widehat{z_{\mathrm{\&alpha;}}}}{\widehat{z_{\mathrm{\&beta;}}}}\right)---\left(16\right)$

Wherein,For the estimated value of rotor-position,WithCounter electromotive force for sliding mode observer estimation；

Secondly as the use of low pass filter, its phase place has certain hysteresis quality, phase place must be carried out delayed benefit Repaying, its phase compensation amount is:

$\mathrm{\&Delta;}\widehat{\mathrm{\&theta;}}=-\arctan \left(\frac{\mathrm{\&omega;}}{{\mathrm{\&omega;}}_c}\right)---\left(17\right)$

Wherein,It is phase compensation amount, rotating speed when ω is stable state, ω_cCut-off frequency for low pass filter；

Furthermore, the estimated value of rotor speed is tried to achieve by below equation:

$\widehat{\mathrm{\&omega;}}=\frac{\sqrt{{\widehat{z_{\mathrm{\&alpha;}}}}^2+{\widehat{z_{\mathrm{\&beta;}}}}^2}}{{\mathrm{\&psi;}}_f}---\left(18\right)$

Wherein,For rotor speed estimated value,WithFor the counter electromotive force of sliding mode observer estimation, ψ_fFor on rotor forever The magnetic linkage that magnet produces.

Owing to system is with the presence of high frequency ripple, utilize low pass filter that counter electromotive force is filtered, it is impossible to well to filter Except estimation error and ripple component, and Kalman filter is not only to the estimation error caused due to parameter of electric machine error, has Well elimination effect, and the ripple component in counter electromotive force can be filtered, there is stronger robustness so that permanent-magnet synchronous The control system of motor has more preferable steady state effect and dynamic response.Utilize low-pass first order filter that it is carried out low-pass filtering, Obtaining continuous print counter electromotive force isWithIn high performance motor application,WithCan not directly utilize, because The counter electromotive force of estimationWithIn containing measuring noise, thus use Kalman filter to obtainWithAfter filtering Counter electromotive forceWithOptimum observation is obtained from random noise signal.As an embodiment, described Kalman filter Module 66 use Kalman filter will obtainWithFiltered counter electromotive forceWithFrom random noise signal In obtain optimum observation, the state equation of Kalman filter is as follows:

${\stackrel{g}{\hat{e}}}_{\mathrm{\&alpha;}}=-{\widehat{\mathrm{\&omega;}}}_e{\hat{e}}_{\mathrm{\&beta;}}-K_k({\hat{e}}_{\mathrm{\&alpha;}}-{\hat{z}}_{\mathrm{\&alpha;}})---\left(19\right)$

${\stackrel{g}{\hat{e}}}_{\mathrm{\&beta;}}={\widehat{\mathrm{\&omega;}}}_e{\hat{e}}_{\mathrm{\&alpha;}}-K_k({\hat{e}}_{\mathrm{\&beta;}}-{\hat{z}}_{\mathrm{\&beta;}})---\left(20\right)$

${\stackrel{g}{\widehat{\mathrm{\&omega;}}}}_e=({\hat{e}}_{\mathrm{\&alpha;}}-{\hat{z}}_{\mathrm{\&alpha;}}){\hat{e}}_{\mathrm{\&beta;}}-({\hat{e}}_{\mathrm{\&beta;}}-{\hat{z}}_{\mathrm{\&beta;}}){\hat{e}}_{\mathrm{\&alpha;}}---\left(21\right)$

Wherein, K_kFor the gain of Kalman filter,For the rotor angular rate estimated value of Kalman filter, WithFor counter electromotive force e_αAnd e_βThe counter electromotive force estimated value of sliding mode observer estimation is obtained by low pass filter,WithFor The counter electromotive force estimated value of sliding mode observer estimationWithObtain after Kalman filtering after Kalman filtering Counter electromotive force estimated value.

As an embodiment, the estimated value of rotor speed after Kalman filtering in described turn count submodule 67 Tried to achieve by below equation:

${\widehat{\mathrm{\&omega;}}}_K=\frac{\sqrt{{{\hat{e}}^2}_{\mathrm{\&alpha;}}+{{\hat{e}}^2}_{\mathrm{\&beta;}}}}{{\mathrm{\&psi;}}_f}---\left(22\right)$

Wherein,For the rotor speed estimated value after Kalman filtering,WithFor after Kalman filtering The counter electromotive force of sliding mode observer estimation, ψ_fThe magnetic linkage produced for permanent magnet on rotor.

As an embodiment, the estimated value of rotor-position after Kalman filtering in described position estimation submodule 69 Tried to achieve by below equation:

${\widehat{\mathrm{\&theta;}}}_{Kc}=-\arctan \left(\frac{{\hat{e}}_{\mathrm{\&alpha;}}}{{\hat{e}}_{\mathrm{\&beta;}}}\right)---\left(23\right)$

Wherein,For the estimated value of the rotor-position after Kalman filtering,WithFor after Kalman filtering Sliding mode observer estimation counter electromotive force.

As an embodiment, in described position compensation submodule 68, due to the use of low pass filter, its phase place has one Fixed hysteresis quality, must carry out lag compensation to phase place, and the phase compensation amount after Kalman filtering is:

$\mathrm{\&Delta;}{\widehat{\mathrm{\&theta;}}}_K=-\arctan \left(\frac{\mathrm{\&omega;}}{{\mathrm{\&omega;}}_c}\right)---\left(24\right)$

Wherein,It is phase compensation amount, rotating speed when ω is stable state, ω_cCut-off frequency for low pass filter.

In described first comparator module 7, described Kalman's observer module 6 estimates the estimated value of rotor speedWith Relation between rotor speed n of estimation is:

$n=\frac{60\widehat{\mathrm{\&omega;}}}{2\mathrm{\&pi;}}=9.55\widehat{\mathrm{\&omega;}}$

That is, described constant is 9.55.

In described Park inverse transform block 13, by the q axle reference voltage of output in described 2nd PI adjustment module 10 With the d axle reference voltage of output in described 3rd PI adjustment module 12Through Park inverse transformation, export biphase static right angle and sit Two phase control voltages under mark system alpha-betaWithIt is specifically related to following reduction formula:

$\left[\begin{array}{c}{u}_{\mathrm{\&alpha;}}^{*}\\ u_{\mathrm{\&beta;}}^{*}\end{array}\right]=\left[\begin{array}{cc}\cos \widehat{\mathrm{\&theta;}}& -\sin \widehat{\mathrm{\&theta;}}\\ \sin \widehat{\mathrm{\&theta;}}& \cos \widehat{\mathrm{\&theta;}}\end{array}\right]\left[\begin{array}{c}{u}_d^{*}\\ u_q^{*}\end{array}\right]$

Wherein,Rotor angle for estimation.

Fig. 5 and Fig. 6 is to emulate, by Fig. 4, the experimental result reached.When test result indicate that rotating speed sudden change or load changing Angular errors is almost 0, and the error of rotating speed is between-6.5 3, and the pulsation of torque is between 2.5 3.3.Indicate this invention The sliding mode observer of the fusion card Germania designed by patent, in rotating speed sudden change or in the case of load changing, can be followed the tracks of very well The rotating speed of motor and corner change, control accuracy is high, and dynamic property is good, has certain practicality.

The above, the only present invention preferably detailed description of the invention, but protection scope of the present invention is not limited thereto, Any those familiar with the art in the technical scope that the invention discloses, the change that can readily occur in or replacement, All should contain within protection scope of the present invention.Therefore, protection scope of the present invention should be with scope of the claims It is as the criterion.

Claims (11)

1. a senseless control system based on the sliding mode observer using Kalman filter, it is characterised in that Including inverter module, PMSM module, a Clark conversion module, Park conversion module, the 2nd Clark conversion module, karr Graceful observer module, the first comparator module, a PI adjustment module, the second comparator module, the 2nd PI adjustment module, the 3rd Comparator module, the 3rd PI adjustment module, Park inverse transform block and SVPWM module, wherein:

Described PMSM module, is used for detecting output three-phase current I_a、I_bAnd I_c；

A described Clark conversion module, for the three-phase current I described PMSM module exported_a、I_bAnd I_cBecome by Clark The biphase stator current i under biphase static rectangular coordinate system alpha-beta is exported after changing_αAnd i_β；

Described Park conversion module, for the biphase stator current i by a described Clark conversion module output_αAnd i_βPass through The biphase current I under biphase synchronous rotating frame d-q is exported after Park conversion_dAnd I_q；

Described 2nd Clark conversion module, for the three-phase voltage U exported by described inverter module_a、U_bAnd U_cThrough Clark Biphase stator voltage u under biphase static rectangular coordinate system alpha-beta is exported after conversion_αAnd u_β；

Described Kalman's observer module, for the biphase stator current i by a described Clark conversion module output_αAnd i_βWith Biphase stator voltage u of described 2nd Clark conversion module output_αAnd u_βCarry out estimation process, estimate the estimation of rotor speed ValueEstimated value with rotor-position

Described first comparator module, for estimating the estimated value of rotor speed in described Kalman's observer moduleTake advantage of Obtain rotor speed n of estimation with a constant, and carry out making difference fortune by rotor speed n of estimation and actual rotor speed n* Calculate；

A described PI adjustment module, exports q axle by PI after the difference that described first comparator module compares being regulated Reference current

Described second comparator module, for exporting q axle reference current after a described PI adjustment module regulationWith described The biphase current I of Park conversion module output_qCarry out making difference operation；

Described 2nd PI adjustment module, exports q axle by PI after the difference that described second comparator module compares being regulated Reference voltage

Described 3rd comparator module, for by d axle reference currentElectric current I with the output of described Park conversion module_dMake Difference operation；

Described 3rd PI adjustment module, exports d axle by PI after the difference that described 3rd comparator module compares being regulated Reference voltage

Described Park inverse transform block, for the q axle reference voltage by described 2nd PI adjustment module outputWith the described 3rd The d axle reference voltage of PI adjustment module outputBy exporting two under biphase static rectangular coordinate system alpha-beta after Park inverse transformation Phase control voltageWith

Described SVPWM module, for by two phase control voltagesWithCarrying out space vector pulse width modulation, output PWM waveform is to institute Stating inverter module, described inverter module inputs three-phase voltage U to described PMSM module_a、U_bAnd U_c, thus control described PMSM module.

A kind of Speedless sensor control based on the sliding mode observer using Kalman filter the most according to claim 1 System processed, it is characterised in that described Kalman's observer module specifically includes SMO optimized algorithm submodule, the 4th comparator Module, saturation function calculating sub module, sliding formwork gain submodule, low pass filter submodule, Kalman filter submodule, turn Speed estimation submodule, position compensation submodule, position estimation submodule and summation module, wherein:

Described SMO optimized algorithm submodule, for biphase stator voltage u by described 2nd Clark conversion module output_αAnd u_β The counter electromotive force e of output after processing with described sliding formwork gain module_αAnd e_βElectric current estimation is exported after SMO optimized algorithm calculates ValueWith

Described 4th comparator submodule, for the electric current estimated value exported by described SMO optimized algorithm submoduleWithWith institute State the biphase stator current i of a Clark conversion module output_αAnd i_βCarry out making difference operation, obtain the current error value on α β axleWith

Described saturation function calculating sub module, for by the current error value on the α β axle of described 4th comparator submodule outputWithCounter electromotive force e is obtained after saturation function computing and described sliding formwork gain module process_αAnd e_β；

Described low pass filter submodule, the counter electromotive force e of output after described sliding formwork gain module is processed_αAnd e_βPass through The counter electromotive force estimated value of sliding mode observer estimation is obtained after low-pass filteringWith

Described Kalman filter submodule, for the sliding formwork observation that will obtain after described low pass filter submodule low-pass filtering The counter electromotive force estimated value of device estimationWithThe counter electromotive force after Kalman filtering has been obtained after Kalman filtering Estimated valueWith

Described turn count submodule, for having obtained through karr after described Kalman filter submodule Kalman filtering Graceful filtered counter electromotive force estimated valueWithThe estimated value of rotor speed is obtained by turn count

Described position estimation submodule, for having obtained through karr after described Kalman filter submodule Kalman filtering Graceful filtered counter electromotive force estimated valueWithThe estimated value before rotor-position does not compensates is obtained by position estimation

Described position compensation submodule, for by phase place is carried out lag compensation, draws the phase place after Kalman filtering Compensation dosage

Described summation module, the estimated value before the rotor-position that described position estimation submodule obtains is not compensatedAnd institute State the phase compensation amount that position compensation submodule obtainsSue for peace, obtain the estimated value of rotor-position

A kind of Speedless sensor control based on the sliding mode observer using Kalman filter the most according to claim 2 System processed, it is characterised in that the SMO optimized algorithm in described SMO optimized algorithm submodule specifically includes step calculated below:

First, AC permanent magnet synchronous motor mathematical model in biphase static rectangular coordinate system alpha-beta is set up:

$\stackrel{.}{i_{\mathrm{\&alpha;}}}=-\frac{R_s}{L_s}{i}_{\mathrm{\&alpha;}}-\frac{1}{L_s}{e}_{\mathrm{\&alpha;}}+\frac{u_{\mathrm{\&alpha;}}}{L_s}---\left(1\right)$

$\stackrel{.}{i_{\mathrm{\&beta;}}}=-\frac{R_s}{L_s}{i}_{\mathrm{\&beta;}}-\frac{1}{L_s}{e}_{\mathrm{\&beta;}}+\frac{u_{\mathrm{\&beta;}}}{L_s}---\left(2\right)$

Wherein,For electric current i current value i on α axle_αDerivative,For electric current i current value i on β axle_βDerivative, R_S For stator winding resistance, Ls is equivalent inductance, e_αFor sliding mode observer counter electromotive force on α axle, e_βFor sliding mode observer at β Counter electromotive force on axle, u_αFor voltage U magnitude of voltage on α axle, u_βFor voltage U magnitude of voltage on β axle；

Secondly, counter electromotive force equation is substituted into:

e_α=-ψ_fω_rsinθ (3)

e_β=ψ_fω_rcosθ (4)

Wherein, ψ_fThe magnetic linkage produced for permanent magnet on rotor, ω_rFor synchronous rotational speed, θ is rotor angle location；

Furthermore, AC permanent magnet synchronous motor SMO in biphase static rectangular coordinate system alpha-beta optimizes accounting equation and is:

$\stackrel{.}{\widehat{i_{\mathrm{\&alpha;}}}}=-\frac{R_s}{L_s}\widehat{i_{\mathrm{\&alpha;}}}+\frac{u_{\mathrm{\&alpha;}}}{L_s}-\frac{k}{L_s}sat(\widehat{i_{\mathrm{\&alpha;}}}-i_{\mathrm{\&alpha;}})---\left(5\right)$

$\stackrel{.}{\widehat{i_{\mathrm{\&beta;}}}}=-\frac{R_s}{L_s}\widehat{i_{\mathrm{\&beta;}}}+\frac{u_{\mathrm{\&beta;}}}{L_s}-\frac{k}{L_s}sat(\widehat{i_{\mathrm{\&beta;}}}-i_{\mathrm{\&beta;}})---\left(6\right)$

Wherein,It is respectively i_α、i_βEstimated value, k is sliding formwork handoff gain；

Finally, by the above-mentioned current estimation error equation that obtains:

$\stackrel{.}{\widetilde{i_{\mathrm{\&alpha;}}}}=-\frac{R_s}{L_s}\widetilde{i_{\mathrm{\&alpha;}}}+\frac{e_{\mathrm{\&alpha;}}}{L_s}-\frac{k}{L_s}sat\left(\widetilde{i_{\mathrm{\&alpha;}}}\right)---\left(7\right)$

$\stackrel{.}{\widetilde{i_{\mathrm{\&beta;}}}}=-\frac{R_s}{L_s}\widetilde{i_{\mathrm{\&beta;}}}+\frac{e_{\mathrm{\&beta;}}}{L_s}-\frac{k}{L_s}sat\left(\widetilde{i_{\mathrm{\&beta;}}}\right)---\left(8\right)$

Wherein,For the current error value on α axle,For the current error value on β axle.

A kind of Speedless sensor control based on the sliding mode observer using Kalman filter the most according to claim 2 System processed, it is characterised in that the current error value in described 4th comparator submoduleWithAccounting equation be:

$\widetilde{i_{\mathrm{\&alpha;}}}={\hat{i}}_{\mathrm{\&alpha;}}-i_{\mathrm{\&alpha;}}---\left(9\right)$

$\widetilde{i_{\mathrm{\&beta;}}}={\hat{i}}_{\mathrm{\&beta;}}-i_{\mathrm{\&beta;}}---\left(10\right)$

Wherein,And i_αFor the current error value on α axle, electric current estimated value and current value,And i_βFor on β axle Current error value, electric current estimated value and current value.

A kind of Speedless sensor control based on the sliding mode observer using Kalman filter the most according to claim 2 System processed, it is characterised in that counter electromotive force e α and e in described saturation function calculating sub module_βCalculating process include respectively Following steps:

First, choosing sat is that saturation function carries out saturation function computing, it may be assumed that

$sat=\left\{\begin{array}{cc}1& x>h\\ x/h& \left|x\right|\&le;h\\ -1& x<h\end{array}\right.---\left(11\right)$

Secondly, liapunov function is chosen:To V derivation, work as k > max (| e_α|,|e_β|) time, thenBeing known by Lyapunov theorem of stability, electric current sliding mode observer is stable；

Furthermore, choosing current error is sliding formwork diverter surface, then, when entering sliding mode, haveWith Time,

$e_{\mathrm{\&alpha;}}=ksat\left(\widetilde{i_{\mathrm{\&alpha;}}}\right)---\left(12\right)$

$e_{\mathrm{\&beta;}}=ksat\left(\widetilde{i_{\mathrm{\&beta;}}}\right)---\left(13\right)$

Wherein, e_αAnd e_βFor the counter electromotive force of sliding mode observer,For the current error value on α axle,For the current error on β axle Value, k is sliding formwork handoff gain.

A kind of Speedless sensor control based on the sliding mode observer using Kalman filter the most according to claim 2 System processed, it is characterised in that obtain the anti-of sliding mode observer estimation by low pass filter in described low pass filter submodule Electromotive force estimated valueWithCalculating process include:

Using low pass filter, discontinuous switching signal is converted to the continuous signal of equivalence, corresponding computing formula is as follows:

${\hat{z}}_{\mathrm{\&alpha;}}=\frac{{\mathrm{\&omega;}}_c}{s+{\mathrm{\&omega;}}_c}{e}_{\mathrm{\&alpha;}}---\left(14\right)$

${\hat{z}}_{\mathrm{\&beta;}}=\frac{{\mathrm{\&omega;}}_c}{s+{\mathrm{\&omega;}}_c}{e}_{\mathrm{\&beta;}}---\left(15\right)$

Wherein,WithFor the counter electromotive force estimated value of sliding mode observer estimation, ω_cFor the cut-off frequency of low pass filter, s is Laplace operator, e_αAnd e_βCounter electromotive force for sliding mode observer.

A kind of Speedless sensor control based on the sliding mode observer using Kalman filter the most according to claim 6 System processed, it is characterised in that also include step calculated below in described low pass filter submodule:

First, the estimated value of rotor-position is tried to achieve by below equation:

$\widehat{\mathrm{\&theta;}}=-\arctan \left(\frac{\widehat{z_{\mathrm{\&alpha;}}}}{\widehat{z_{\mathrm{\&beta;}}}}\right)---\left(16\right)$

Wherein,For the estimated value of rotor-position,WithCounter electromotive force for sliding mode observer estimation；

Secondly as the use of low pass filter, its phase place has certain hysteresis quality, phase place must be carried out lag compensation, its Phase compensation amount is:

$\mathrm{\&Delta;}\widehat{\mathrm{\&theta;}}=-\arctan \left(\frac{\mathrm{\&omega;}}{{\mathrm{\&omega;}}_c}\right)---\left(17\right)$

Wherein,It is phase compensation amount, rotating speed when ω is stable state, ω_cCut-off frequency for low pass filter；

Furthermore, the estimated value of rotor speed is tried to achieve by below equation:

$\widehat{\mathrm{\&omega;}}=\frac{\sqrt{{\widehat{z_{\mathrm{\&alpha;}}}}^2+{\widehat{z_{\mathrm{\&beta;}}}}^2}}{{\mathrm{\&psi;}}_f}---\left(18\right)$

Wherein,For rotor speed estimated value,WithFor the counter electromotive force of sliding mode observer estimation, ψ^fFor permanent magnet on rotor The magnetic linkage produced.

A kind of Speedless sensor control based on the sliding mode observer using Kalman filter the most according to claim 2 System processed, it is characterised in that use in described Kalman filter submodule Kalman filter to obtainWithFiltering After counter electromotive forceWithObtaining optimum observation from random noise signal, the state equation of Kalman filter is as follows:

${\stackrel{g}{\hat{e}}}_{\mathrm{\&alpha;}}=-{\widehat{\mathrm{\&omega;}}}_e{\hat{e}}_{\mathrm{\&beta;}}-K_k({\hat{e}}_{\mathrm{\&alpha;}}-{\hat{z}}_{\mathrm{\&alpha;}})---\left(19\right)$

${\stackrel{g}{\hat{e}}}_{\mathrm{\&beta;}}={\widehat{\mathrm{\&omega;}}}_e{\hat{e}}_{\mathrm{\&alpha;}}-K_k({\hat{e}}_{\mathrm{\&beta;}}-{\hat{z}}_{\mathrm{\&beta;}})---\left(20\right)$

${\stackrel{g}{\hat{e}}}_e=({\hat{e}}_{\mathrm{\&alpha;}}-{\hat{z}}_{\mathrm{\&alpha;}}){\hat{e}}_{\mathrm{\&beta;}}-({\hat{e}}_{\mathrm{\&beta;}}-{\hat{z}}_{\mathrm{\&beta;}}){\hat{e}}_{\mathrm{\&alpha;}}---\left(21\right)$

Wherein, K_kFor the gain of Kalman filter,For the rotor angular rate estimated value of Kalman filter,WithFor Counter electromotive force e_αAnd e_βThe counter electromotive force estimated value of sliding mode observer estimation is obtained by low pass filter,WithSee for sliding formwork Survey the counter electromotive force estimated value of device estimationWithObtained after Kalman filtering after Kalman filtering is the most electronic Gesture estimated value.

A kind of Speedless sensor control based on the sliding mode observer using Kalman filter the most according to claim 2 System processed, it is characterised in that in described turn count submodule, the estimated value of rotor speed after Kalman filtering is passed through Below equation is tried to achieve:

${\widehat{\mathrm{\&omega;}}}_K=\frac{\sqrt{{{\hat{e}}^2}_{\mathrm{\&alpha;}}+{{\hat{e}}^2}_{\mathrm{\&beta;}}}}{{\mathrm{\&psi;}}_f}---\left(22\right)$

Wherein,For the rotor speed estimated value after Kalman filtering,WithFor the sliding formwork after Kalman filtering The counter electromotive force of observer estimation, ψ_fThe magnetic linkage produced for permanent magnet on rotor.

A kind of Speedless sensor based on the sliding mode observer using Kalman filter the most according to claim 2 Control system, it is characterised in that in described position estimation submodule, the estimated value of rotor-position after Kalman filtering is led to Cross below equation to try to achieve:

${\widehat{\mathrm{\&theta;}}}_{Kc}=-\arctan \left(\frac{{\hat{e}}_{\mathrm{\&alpha;}}}{{\hat{e}}_{\mathrm{\&beta;}}}\right)---\left(23\right)$

Wherein,For the estimated value of the rotor-position after Kalman filtering,WithFor the cunning after Kalman filtering The counter electromotive force of mould observer estimation.

11. a kind of Speedless sensors based on the sliding mode observer using Kalman filter according to claim 2 Control system, it is characterised in that in described position compensation submodule, due to the use of low pass filter, its phase place has necessarily Hysteresis quality, phase place must be carried out lag compensation, the phase compensation amount after Kalman filtering is:

$\mathrm{\&Delta;}{\widehat{\mathrm{\&theta;}}}_K=-\arctan \left(\frac{\mathrm{\&omega;}}{{\mathrm{\&omega;}}_e}\right)---\left(24\right)$

Wherein,It is phase compensation amount, rotating speed when ω is stable state, ω_cCut-off frequency for low pass filter.