# Sensorless Stator Field-Oriented Controlled IM Drive at Low Speed withRr Estimator

Authors: Wen-Cheng Pu; Yung-Chang Luo; Po-Yan Chu
Journal: Mathematical Problems in Engineering
 (2014)

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## Abstract

This paper pertains to a technique of a sensorless indirect stator field-oriented induction motor control, which prevents the accumulative errors incurred by the integrator and the problem relating to the stability of the control system caused by the stator resistance susceptible to temperature variations while conducting the flux estimation directly and computing the synchronous rotary speed. The research adds an adaptive flux observer to estimate the speed of the rotor and uses the fixed trace algorithm (FTA) to execute an online estimation of the slip difference, thereby improving the system of stability under the low rotary speed at regenerating mode and the influence of the rotor resistance on the slip angle. Finally, the paper conducts simulations by Simulink of MATLAB and practices to verify the correctness of the result  the paper presents.


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## Body

## 1. Introduction

Induction motors are broadly applied to industrial machines due to their simple configuration and easy maintenance. However, the induction motor is the nonlinear and time-varying system, so the variable-transmission drive system of the early-stage induction motor has not been easily carried out until the field-oriented control theory was developed by Blaschke in the year 1972 and the rapid development of the algorithm technique for semiconductor came to overcome these problems [1–3]. To save the space and maintain and decrease the influence of promiscuous signals of the speed sensor on the reliability and the antinoise capability of the control system of the induction motor, researches on sensorless speed controller have been claimed incessantly [4–7]. It can be classified into the flux estimation mode and the model reference adaptive mode [4]. The former conducts the estimation by using an open-loop method, so it is susceptible to variations of motor parameters. The latter adopts the structure of the model adaptive mode in order to obtain a preferable system response and accuracy of estimation [1, 8]. However, when this estimation system is operated in the low rotary speed at regenerating mode, unstable areas still exist, which would affect the sturdiness of the entire sensorless speed control system [5]. In order to operate the entire estimation system in the low rotary speed at regenerating mode, this research adds the observer and uses the Routh Stability Criterion to obtain the observer feedback gain under the stabilized system [1, 6]. Further, the stator field-oriented control configuration is relatively simple, so it is often applied to the vehicle traction control system. The system can be classified into a direct control and an indirect control. The former adopts the integration induction voltage to estimate the flux directly and compute the synchronous speed. However, the integrator accumulates errors easily, and the generation of incorrect parameters of the stator resistance affects the estimation result as well. Therefore, this research adopts the latter. The shortcoming of the latter is that the accuracy of estimating the slip difference is affected as the slip estimation is susceptible to the rotor resistance which is subjected to temperature changes, and thence the conversion of coordinates and the stability of the system are also affected. Accordingly, the subject research adopts the rotor observer by the fixed trace algorithm (FTA) to estimate the rotor resistance [2, 9].Therefore, the subject research applies an indirect stator field-oriented structure to control the induction motor, adopts the adaptive observer to estimate the rotor speed of the motor for the demand for low rotary speed regenerating mode, and uses the fixed trace algorithm (FTA) to estimate the rotor resistance, thereby increasing the control stability and accuracy of the induction motor.


## 2. Methodology

### 2.1. Dynamic of the Induction Motor

Under the randomd-q coordinate system, the electrical equations for the stator side and the rotor side of the induction motor can be, respectively, expressed by
(1)[(Rs+Lsp)I+ωLsJ]isa⃑+[LmpI+ωLmJ]ira⃑=vsa⃑(2)[LmpI+Lm(ω-ωr)J]isa⃑+[(Rr+Lrp)I+Lr(ω-ωr)J]ira⃑=0.Under the random coordinate, the stator fluxϕsa⃑ and the rotor flux ϕra⃑ can be, respectively, expressed by
(3)ϕsa⃑=Lsisa⃑+Lmira⃑,(4)ϕra⃑=Lmisa⃑+Lrira⃑,
wherein the stator voltage is vsa⃑=[vdsavqsa]T, the stator current is isa⃑=[idsaiqsa]T, the rotor current is ira⃑=[idraiqra]T, the stator flux is ϕsa⃑=[ϕdsaϕqsa]T, the rotor flux is ϕra⃑=[ϕdraϕqra]T, the motor parameters, such as RS, Rr, LS, Lr, and Lm are, respectively, are denoted by the stator resistance, the rotor resistance, the stator inductance, the rotor inductance, and the mutual inductance, ω and ωr are denoted by the speed of the random angle and the speed of the rotor angle, p=d/dt, I=[1001], and J=[0-110].Formula (3) can be rewritten by ira⃑=(1/Lm)(ϕsa⃑-Lsisa⃑), then substitute it into equations (1) and (2); then convert the formula into the synchronous coordinate system (ω=ωe), and thence get
(5)RsIise⃑+(pI+ωeJ)ϕse⃑=vse⃑,(6)-LsLm[(Rr+σLrp)I+σLrωslJ]ise⃑+LrLm[(1τr+p)I+ωslJ]ϕse⃑=0.Finally, formulas (5) and (6) are arranged into the dynamic as follows:
(7)pX=AX+Bvse⃑,(8)ise⃑=CX,
wherein
(9)X=[ise⃑ϕse⃑]T,A=[A11A12A21A22],B=[1Lσ1]T,C=[10],A11=-(RsLs+1τrσ)I-(ωe-ωr)J,A12=1Lσ(1τrI-ωrJ),A21=-RsI,A22=-ωeJ,τr=LrRr,Lσ=Ls-Lm2Lr,
and ωe is the speed of the synchronous angle.


### 2.2. Full-Order Adaptive Speed Estimator

Formulas (7) and (8) are added into the full-order flux observer and arranged as
(10)pX^=A^X^+Bvse⃑+H(ise⃑-ise⃑^)(11)ise⃑^=CX^,
wherein, the symbol “A^” represents an estimated value, A^=[A^11A^12A21A22], A^11=-(Rs/Ls+1/τrσ)I-(ωe-ω^r)J, A^12=(1/Lσ)((1/τr)I-ω^rJ), H1=h1I+h2J, H2=h3I+h4J, and H=[H1H2] represents the observer feedback gain matrix.According to the model reference adaptive system, formulas (7) and (8) are the model reference and formulas (10) and (11) are the adjustable model. To make a subtraction between formulas (7) and (10) as well as a subtraction between formulas (8) and (11), respectively, get
(12)pe=Ae+ΔAX^-H(ise⃑-ise⃑^)(13)ei=Ce,
wherein, in formulas (12) and (13), e=[eieϕ]T, ei=ise⃑-ise⃑^, eϕ=ϕse⃑-ϕse⃑^, ΔA=[ΔA11ΔA1200], ΔA11=JΔωr, ΔA12=-J(1/Lσ)Δωr, and Δωr=ωr-ω^r.Incorporate the current difference calculated by formula (13) into formula (12) and get the following formula after the process of linearization:
(14)pe=(A-HC)e+W,
wherein W is denoted by
(15)W=[J(ise⃑^-1Lσϕse⃑^)Δωr0].The structure of the rotor speedω^r estimator as shown in Figure 1 includes a linear feedforward block and a nonlinear feedback block, wherein the nonlinear function F, set as the subject speed estimator, is defined by
(16)ω^r=(Kpω+Kiω∫dt)eiTJ(ise⃑^-1Lσϕse⃑^).Figure 1
Model reference adaptive system block.In formula (16), Kpω is the proportional gain and Kiω is the integration gain.With respect to the selection of the feedback gain of the observer, formulas (7) and (10) are converted into the synchronous coordinate system (ω=ωe) while deducing and rewriting them as
(17)pise⃑=A11ise⃑+A12ϕse⃑+vse⃑pϕse⃑=A21ise⃑+A22ϕse⃑(18)pise⃑^=A11ise⃑^+A12ϕse⃑^+vse⃑+H1(ise⃑-ise⃑^)pϕse⃑^=A21ise⃑^+A22ϕse⃑^+H2(ise⃑-ise⃑^).
Subtract formula (17) from formula (18) and then get an error equation as follows:
(19)[sI-(A11-H1)]ei=A12eϕ+ΔA11ise⃑^+ΔA12ϕse⃑^[sI-A22]eϕ=(A21-H2)ei.
The following formula is obtained from formula (19):
(20)ei=G(s)J(ise⃑^-1Lσϕse⃑^),
wherein
(21)G(s)=[G11G12G21G22],ei=[edeq]T.
Under a steady state,
(22)eq=G22(i^dse-1Lσϕ^dse)Δωr.The arranged close-loop system of the full-order adaptive speed estimator is shown in Figure2.Figure 2
Torque-speed plane.


## 2.1. Dynamic of the Induction Motor

Under the randomd-q coordinate system, the electrical equations for the stator side and the rotor side of the induction motor can be, respectively, expressed by
(1)[(Rs+Lsp)I+ωLsJ]isa⃑+[LmpI+ωLmJ]ira⃑=vsa⃑(2)[LmpI+Lm(ω-ωr)J]isa⃑+[(Rr+Lrp)I+Lr(ω-ωr)J]ira⃑=0.Under the random coordinate, the stator fluxϕsa⃑ and the rotor flux ϕra⃑ can be, respectively, expressed by
(3)ϕsa⃑=Lsisa⃑+Lmira⃑,(4)ϕra⃑=Lmisa⃑+Lrira⃑,
wherein the stator voltage is vsa⃑=[vdsavqsa]T, the stator current is isa⃑=[idsaiqsa]T, the rotor current is ira⃑=[idraiqra]T, the stator flux is ϕsa⃑=[ϕdsaϕqsa]T, the rotor flux is ϕra⃑=[ϕdraϕqra]T, the motor parameters, such as RS, Rr, LS, Lr, and Lm are, respectively, are denoted by the stator resistance, the rotor resistance, the stator inductance, the rotor inductance, and the mutual inductance, ω and ωr are denoted by the speed of the random angle and the speed of the rotor angle, p=d/dt, I=[1001], and J=[0-110].Formula (3) can be rewritten by ira⃑=(1/Lm)(ϕsa⃑-Lsisa⃑), then substitute it into equations (1) and (2); then convert the formula into the synchronous coordinate system (ω=ωe), and thence get
(5)RsIise⃑+(pI+ωeJ)ϕse⃑=vse⃑,(6)-LsLm[(Rr+σLrp)I+σLrωslJ]ise⃑+LrLm[(1τr+p)I+ωslJ]ϕse⃑=0.Finally, formulas (5) and (6) are arranged into the dynamic as follows:
(7)pX=AX+Bvse⃑,(8)ise⃑=CX,
wherein
(9)X=[ise⃑ϕse⃑]T,A=[A11A12A21A22],B=[1Lσ1]T,C=[10],A11=-(RsLs+1τrσ)I-(ωe-ωr)J,A12=1Lσ(1τrI-ωrJ),A21=-RsI,A22=-ωeJ,τr=LrRr,Lσ=Ls-Lm2Lr,
and ωe is the speed of the synchronous angle.


## 2.2. Full-Order Adaptive Speed Estimator

Formulas (7) and (8) are added into the full-order flux observer and arranged as
(10)pX^=A^X^+Bvse⃑+H(ise⃑-ise⃑^)(11)ise⃑^=CX^,
wherein, the symbol “A^” represents an estimated value, A^=[A^11A^12A21A22], A^11=-(Rs/Ls+1/τrσ)I-(ωe-ω^r)J, A^12=(1/Lσ)((1/τr)I-ω^rJ), H1=h1I+h2J, H2=h3I+h4J, and H=[H1H2] represents the observer feedback gain matrix.According to the model reference adaptive system, formulas (7) and (8) are the model reference and formulas (10) and (11) are the adjustable model. To make a subtraction between formulas (7) and (10) as well as a subtraction between formulas (8) and (11), respectively, get
(12)pe=Ae+ΔAX^-H(ise⃑-ise⃑^)(13)ei=Ce,
wherein, in formulas (12) and (13), e=[eieϕ]T, ei=ise⃑-ise⃑^, eϕ=ϕse⃑-ϕse⃑^, ΔA=[ΔA11ΔA1200], ΔA11=JΔωr, ΔA12=-J(1/Lσ)Δωr, and Δωr=ωr-ω^r.Incorporate the current difference calculated by formula (13) into formula (12) and get the following formula after the process of linearization:
(14)pe=(A-HC)e+W,
wherein W is denoted by
(15)W=[J(ise⃑^-1Lσϕse⃑^)Δωr0].The structure of the rotor speedω^r estimator as shown in Figure 1 includes a linear feedforward block and a nonlinear feedback block, wherein the nonlinear function F, set as the subject speed estimator, is defined by
(16)ω^r=(Kpω+Kiω∫dt)eiTJ(ise⃑^-1Lσϕse⃑^).Figure 1
Model reference adaptive system block.In formula (16), Kpω is the proportional gain and Kiω is the integration gain.With respect to the selection of the feedback gain of the observer, formulas (7) and (10) are converted into the synchronous coordinate system (ω=ωe) while deducing and rewriting them as
(17)pise⃑=A11ise⃑+A12ϕse⃑+vse⃑pϕse⃑=A21ise⃑+A22ϕse⃑(18)pise⃑^=A11ise⃑^+A12ϕse⃑^+vse⃑+H1(ise⃑-ise⃑^)pϕse⃑^=A21ise⃑^+A22ϕse⃑^+H2(ise⃑-ise⃑^).
Subtract formula (17) from formula (18) and then get an error equation as follows:
(19)[sI-(A11-H1)]ei=A12eϕ+ΔA11ise⃑^+ΔA12ϕse⃑^[sI-A22]eϕ=(A21-H2)ei.
The following formula is obtained from formula (19):
(20)ei=G(s)J(ise⃑^-1Lσϕse⃑^),
wherein
(21)G(s)=[G11G12G21G22],ei=[edeq]T.
Under a steady state,
(22)eq=G22(i^dse-1Lσϕ^dse)Δωr.The arranged close-loop system of the full-order adaptive speed estimator is shown in Figure2.Figure 2
Torque-speed plane.


## 3. Indirect Stator Field-Oriented Control

For conducting an indirect stator field-oriented control, it needs to computeωsl during the control because the synchronous speed (ωe) needs to estimate rotor speed (ωr) and the slip (ωsl) together. In the synchronous d-q coordinate system, when the equation derived from the stator current and the stator flux which are set as the state variables makes the q-axis component of the stator flux reach zero to acquire the slip difference speed, and the stator flux is in accordance with d-axis of the synchronous reference coordinates frame. Hence, the slip difference speed and the d-axis stator flux can be expressed respectively, as follows:
(23)ωsl=LS(1+στrs)iqseτr(ϕdse-Lσidse),(24)1+τrs(1+στrs)ϕdse=idse-στrωsl1+στrsiqse.In the paper, use formula (23) to estimate ωsl. Furthermore, because formula (24) includes the q-axis component, the feedforward compensation quantity is added and defined by formula (25) to execute the decoupling control. Consider
(25)idqe=στrωsl1+στrsiqse.After adding the feedforward compensation quantity, formula (24) can be renewed as
(26)ϕdse=Ls(1+στrs)1+τrsiqse.Herein, the electromagnetic torqueTe under the stator field-oriented control can be expressed by
(27)Te=3P4iqseϕdse.And formula (5) is renewed into the following equations:
(28)vdse=Rsidse+pϕdse(29)vqse=Rsiqse+ωeϕdse.As shown in formula (29), it includes the d-axis component, so the feedforward compensation quantity ωeϕdse is added for subjecting the axis to the decoupling control.


## 4. Design of the Observer Feedback Gain

As shown in Figure2, the transfer function G22 is arranged by
(30)G22=s3+xs2+(m+ωe2)s+ωe(n+ωex)[s2+xs+(m-ωey-ωe2)]2+[(y+2ωe)s+(n+ωex)]2,
wherein
(31)x=h1+RsLσ+1στry=h2-ωrm=1Lσ[1τr(Rs+h3)+ωrh4]n=1Lσ[1τrh4-ωr(Rs+h3)].To design the observer feedback gain, the molecular equation ofG22 is made in the Routh table as shown in (32) according to the Routh Stability Criterion. Consider
(32)s31m+ωe2s2xnωe+xωe2s1mx-nωex0snωe+xωe20.
According to the Routh table, a steady-state condition can be made as follows:
(33)x>0mx>nωexωe(ωe-ωc)>0
wherein
(34)ωc=-nx.To analyse the unstable-speed area clearly, the torque-speed chart is applied to present the relationship between the two elements. As shown in formula (23), under the steady state, the slip ωsl can be arranged by
(35)ωsl=Lσiqseϕdse-Lσidse.
From formula (35),
(36)iqse=ωsl(ϕdseLσ-idse).
Incorporate formula (36) into formula (27) and get
(37)Te=3P4(ϕdse2Lσ-idseϕdse)ωsl=KTωsl.
In formula (37), the slip ωsl=ωe-ω^r and KT is a torque constant, which is presented by
(38)KT=3P4(ϕdse2Lσ-idseϕdse).
If the condition of ωc=ωe is satisfied, the incorporation of formula (33) can get
(39)ωe=Rs+h3Lσxω^r-h4τrLσx.Finally, incorporate formula (39) into formula (37) and arrange it; the equation is got from the torque and the rotation speed as follows:
(40)Te=aω^r+b.
In formula (40), a and b are shown as follows:
(41)a=KT(Rs+h3Lσx-1).b=-KTh4τrLσx.Figure2 shows the torque-speed chart, wherein the x-axis represents the rotor speed of the motor ωr and the y-axis represents the motor torque Te. In the figure, the gray part shows that, when the speed estimator is operated in the regenerating area, an unstable part will be generated during the speed estimation, and the steady-state condition of formula (33) is used to design the observer gain to reduce the unstable part.From the above analysis, whenωc=0, the observer gain is
(42)h1=-(RsLσ+1στr)+k,h2=kωr,h3=0,h4=τrRsωr,k>0,
wherein, to satisfy the steady-state condition, a positive integer k larger than zero is selected and the observer feedback gain h1~h4 incorporates with x, y, m, and n, respectively, to get
(43)x=k,y=(k-1)ωr,m=1Lσ[Rsτr+ωr2τrRs],n=0.


## 5. Design of PI Gain Parameters of the Speed Estimator

In the single-input single-output close-loop system of Figure3, η represents the noise which affects the speed estimation, and the paper inferred from the influence of the noise that
(44)ω^η=(i^dse-(1/Lσ)ϕ^dse)(Kpω+Kiω/s)1+G22(i^dse-(1/Lσ)ϕ^dse)2(Kpω+Kiω/s).
To consider the influence of the high frequency noise on the speed estimation, incorporate s=j∞ into formula (44) and infer that
(45)ω^η|s=j∞=(i^dse-1Lσϕ^dse)Kpω.
From formula (45), it can be found that the size of the proportional gain Kpω can restrict the high frequency noise, so the proportional gain Kpω selected smaller value to reduce the influence of the noise on the speed estimator. Considering the effect of the low frequency noise, it can incorporate s=j0 into formula (44) and arrange it to get
(46)ω^η|s=j0=1(i^dse-(1/Lσ)ϕ^dse)G22|s=j0.
From formula (46), the adjustment to the proportional gain Kpω or the integration gain KIω cannot improve the influence of the low frequency noise on the speed estimator. However, consider the influence of the proportional and integration gain of the motor acceleration/deceleration on the speed estimator (R can be set as an acceleration/deceleration velocity) and infer from formula (47) that a selection of the value of the integration gain KIω can affect the steady-state error of the speed estimation as the error es is set by acceleration/deceleration during a slope input speed. When the integration result is increased, the steady-state error of the speed is relatively decreased to increase the accuracy of the speed estimator. Consider
(47)es=lim⁡s→0s×Rs2×11+G22(i^dse-(1/Lσ)ϕ^dse)2(Kpω+Kiω/s)=RKiω(i^dse-(1/Lσ)ϕ^dse)2G22|s=0.Figure 3
Close-loop system of the full-order adaptive speed estimator.


## 6. Estimation of Rotor Resistance by FTA

Rewrite formula (3) as iss⃑=(1/Lm)(ϕrs⃑-Lrirs⃑) and incorporate formula (2) to get
(48)pϕdrs=-Rridrs-ωrϕqrs(49)pϕqrs=-Rriqrs-ωrϕdrs.
Then rewrite formula (49) as
(50)ωr=pϕqrs+Rriqrsϕdrs.
Incorporate formula (50) into formula (48) and arrange it to get the formula of the estimated rotor resistance Rr and thence rewrite it according to the fixed trace algorithm (FTA) as follows:
(51)Rr=-pϕdrsϕdrs+pϕqrsϕqrsidrsϕdrs+iqrsϕqrs
or
(52)R^r[n]=R^r[n-1]-ra[n]1+r(a[n])2(R^r[n-1]a[n]-b[n]),
wherein a[n]=-(idrsϕdrs+iqrsϕqrs), b[n]=pϕdrsϕdrs+pϕqrsϕqrs, and r represents the gain. This paper adopts the estimated rotor resistance Rr of this formula.


## 7. Results of Simulation and Experiment

To verify the feasibility and sturdiness of the subject system, an experimental platformas shown in Figure5 is constituted. According to the structure of the system shown in Figure 4, it executed simulations and experiments by controlling different situations. First, the personal computer of Simulink program containing MATLAB is the speed sensorless vector controller, which functions as the simulation process. The result of the simulation drives the induction motor via PC-based control card which controls the inverter practically. The specification of the induction motor is shown in Table 1. During the experiment, there are three different rotor speeds, namely, ±1800 rpm, ±900 rpm, ±600 rpm, and ±300 rpm, and conduct the simulation of controlling the acceleration and deceleration under the nonloading and loading modes. Results are shown in Figures 6, 7, 8, and 9. Figures 10, 11, 12, and 13, respectively, show the simulation results while loading 1 N-m under the rotor speed of the induction motor commanded at ±300 rpm, ±900 rpm, ±600 rpm, and ±1800 rpm. Figures 6–9, respectively, show the nonloading practical data when the induction motor is at ±300 rpm, ±900 rpm, and ±1800 rpm. Figures 10–13, respectively, show the practical data of loading 1 N-m under the rotor speed of the induction motor commanded at ±300 rpm, ±900 rpm, and ±1800 rpm.Table 1
Maximum output power
1 HP
Phase
3
Rated voltage
220 V
Rated current
3.1 A
Rated rotary speed
1680 rpm
P (pole)
4
R
s
2.85Ω
R
r
2.3433Ω
L
s
0.1967 H
L
r
0.1967 H
L
m
0.1886 H
J
m
0.009N·m·sec2/rad
B
m
0.00825N·m·sec2/radFigure 4
Integral block of speed sensorless controller.Figure 5
The structure of the experimental platform.Simulation and Practical data of the induction motor at 300 rpm. (a) Actual motor speed. (b) Estimated motor speed. (c) Stator flux. (d) Estimated torque.
(a)
(b)
(c)
(d)Simulation and Practical data of the induction motor at 600 rpm. (a) Actual motor speed. (b) Estimated motor speed. (c) Stator flux. (d) Estimated torque.
(a)
(b)
(c)
(d)Simulation and Practical data of the induction motor at 900 rpm. (a) Actual motor speed. (b) Estimated motor speed. (c) Stator flux. (d) Estimated torque.
(a)
(b)
(c)
(d)Simulation and Practical data of the induction motor at 1800 rpm. (a) Actual motor speed. (b) Estimated motor speed. (c) Stator flux. (d) Estimated torque.
(a)
(b)
(c)
(d)Loading simulation and Practical data of the induction motor at 300 rpm. (a) Actual motor speed. (b) Estimated motor speed. (c) Stator flux. (d) Estimated torque.
(a)
(b)
(c)
(d)Loading simulation and Practical data of the induction motor at 600 rpm. (a) Actual motor speed. (b) Estimated motor speed. (c) Stator flux. (d) Estimated torque.
(a)
(b)
(c)
(d)Loading simulation and Practical data of the induction motor at 900 rpm. (a) Actual motor speed. (b) Estimated motor speed. (c) Stator flux. (d) Estimated torque.
(a)
(b)
(c)
(d)Loading simulation and Practical data of the induction motor at 1800 rpm. (a) Actual motor speed. (b) Estimated motor speed. (c) Stator flux. (d) Estimated torque.
(a)
(b)
(c)
(d)As shown from Figures6–13, the upper part shows the simulation result and the lower part shows the experimental result, wherein (a) and (b) represent the respective comparisons between the simulations and experiments of the actual and estimated motor speed. The x-axis shows the time with the unit showing the seconds (sec) and the y-axis shows the rotor speed with the unit showing the speed per minute (rpm). The blue solid line represents the speed command, wherein 0-1 seconds can be the clockwise acceleration of the motor, 1-2 seconds can be the constant and clockwise rotation of the motor, 2-3 seconds can be the clockwise deceleration of the motor, 3-4 seconds can be the counterclockwise acceleration, 4-5 seconds can be the constant and counterclockwise rotation of the motor, and 5-6 seconds can be the counterclockwise deceleration of the motor. (c) is the motor estimated torque, wherein the x-axis shows the time with the unit showing the seconds (sec) and y-axis shows the motor torque with the unit showing the Newton meter (N-m). (d) is the stator flux circle, wherein the x-axis shows the stator flux of d-axis component and y-axis shows the stator flux of q-axis component.From Figures6–9 showing the results of simulations and experiments of the motor in the nonloading clockwise and counterclockwise rotations, Figure 17 can verify that, when the motor is operated at the acceleration/deceleration speed, the estimator can estimate the actual rotation speed of the motor accurately and catch the commanded speed of the motor rapidly. When the motor is operated at the constant speed, the estimator can estimate the actual rotation speed of the motor accurately and render the motor able to follow the commanded speed stably. In Figures 10–13, when the motor is loaded by 1 N-m, the estimator can also estimate the actual speed accurately and catch the commanded speed rapidly. The simulations and experiments shown in Figures 6–13 can verify that the speed estimator can possess a wide-range speed estimation regardless of the loading or nonloading condition.Furthermore, for the operation under the low rotary speed regenerating test, Figure14 and Figure 15 show respective data derived from simulating and practicing when the rotor speed to be set at 100 rpm and loading −1 N-m at the 3rd second, wherein Figure 14(a) is the simulation data without adding the observer feedback gain. Figure 14(b) is the simulation data with the addition of the observer feedback gain. Figure 15(a) is the practical data without adding the observer feedback gain. Figure 15(b) is the practical data with the addition of the observer feedback gain. From the data in Figures 14 and 15, when the verification controller is operated in the low rotary speed regenerating mode, it can select the adequate observer feedback gain and estimate the rotor speed correctly and steadily. As shown in Figure 16(a), if the rotor resistance is raised by 30% in 2-3 seconds due to the increased temperature, the estimation result of FTA to the rotor resistance is shown in the figure. The dotted line in the figure denotes the actual value of the rotor resistance of the motor, and the solid line in the figure denotes the estimated value of the rotor resistance giving the slip angle. When the rotor resistance estimator is not added, the generation of error on the synchronous angle can be read in Figure 16(b), wherein the dotted line denotes the reference value of the synchronous angle and the solid line denotes the estimated value of the synchronous angle. Figure 17 shows the data after adding the rotor resistance estimator, from which the synchronous angle can be accurately estimated. Figure 18 shows the estimation results of the simulation and experiment of the rotor resistance by the fixed trace algorithm (FTA), from which it can verify that FTA can estimate the rotor resistance accurately. Figure 19 shows that the result of simulation without FTA when actual rotor resistance is changed caused by temperature variations. It is unstable and it verifies the feasibility and sturdiness of the method set forth in the research.Simulation data of the speed estimation under the regenerating mode of the rotor speed at 100 rpm. (a) Without adding feedback gain. (b) Adding feedback gain.
(a)
(b)Practical data of the speed estimation under the regenerating mode of the rotor speed at 100 rpm. (a) Without adding feedback gain. (b) Adding feedback gain.
(a)
(b)Estimation result of synchronous angleθe without adding the rotor resistance estimator under variations of Rr. (a) Rotor resistance. (b) Synchronous angle.
(a)
(b)Estimation result of synchronous angleθe by adding the rotor resistance estimator under variations of Rr. (a) Rotor resistance. (b) Synchronous angle.
(a)
(b)Results of simulation and experimental of the rotor resistance estimator by FTA. (a) Simulation results, (b) Experimental results.
(a)
(b)Figure 19
Results of simulation without FTA when actual rotor resistance is changed disagree with controller parameter.


## 8. Conclusion

The subject sensorless indirect stator field-oriented control technique provides a set of standard designed steps to be operated under the low rotary speed at regenerating mode and the circumstance of the unstable system. This paper further considers that the property of changes of the rotor resistance caused by variations of temperature would affect the accuracy of estimating the synchronous angle, so this paper combines the FTA which conducts the online and instant estimation of the rotor resistance in order to solve the problem of incorrect estimation of the synchronous angle incurred by the changes of the rotor resistance. Finally, the simulation and practical results can verify the feasibility and sturdiness of the method set forth in the research.



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*Source: 676202-2014-06-16.xml*