Implementation of Sensor less Vector control of PMSM

THE DISSERTATION ENTITLED

Implementation of Sensor less Vector control of

PMSM

SUBMITTED IN

PARTIAL FULFILMENT OF THE REQUIREMENTS

FOR THE AWARD OF THE DEGREE OF

BACHELOR OF TECHNOLOGY

IN

ELECTRICAL ENGINEERING

SUBMITTED BY

JAY M. PATEL (U09EE507)

ABHISHEK D. GOLWALA (U09EE514)

UDAY M. PANWALA (U09EE515)

ROHIT KUMAR (U09EE520)

NEELESH KUMAR GUPTA (U09EE541)

SUPERVISOR:

PROF. JANAK J. PATEL

Department of Electrical Engineering

SARDAR VALLABHBHAI NATIONAL INSTITUTE OF

TECHNOLOGY, SURAT-395007, GUJARAT, INDIA

2012-2013

Department of Electrical Engineering,

Sardar Vallabhbhai National Institute of

Technology,

Surat-395007, INDIA.

CERTIFICATE

This is to certify that the dissertation report entitled IMPLEMTATION

OF SENSORLESS VECTOR CONTROL OF PMSM has been carried out by

Mr. Jay M. Patel (U09EE507), Mr. Abhishek D. Golwala (U09EE514), Mr.

Uday M. Panwala (U09EE515), Mr. Rohit Kumar (U09EE520) and Mr.

Neelesh Kumar Gupta (U09EE541), students of B.Tech Electrical Engineering

under our supervision. They completed this work within a period prescribed

under the ordinances governing the leading to Bachelor Degree in Electrical

Engineering in Sardar Vallabhbhai National Institute of Technology, Surat.

DATE:

PLACE: Surat

(PROF. JANAK J. PATEL) (PROF. M.N.BHUSAVALWALA)

GUIDE Head of Department

DECLARATION

We hereby declare that the dissertation report entitled IMPLEMENTATION

OF SENSORLESS VECTOR CONTROL OF PMSM is being submitted in

partial fulfillment for the award of the degree in BACHELOR OF

TECHNOLOGY IN ELECTRICAL ENGINEERING to Sardar Vallabhbhai

National Institute of Technology, Surat is an authentic record of our own work

done under the guidance of PROF. JANAK J. PATEL in Electrical Engineering

Department. The matter reported in this dissertation has not been submitted at

any other place for award of any degree or diploma.

DATE:

PLACE: Surat

Approval Sheet

Project entitled IMPLEMENTATION OF SENSORLESS VECTOR

CONTROL OF PMSM by Mr. Jay M. Patel (U09EE507), Mr. Abhishek D.

Golwala (U09EE514), Mr. Uday M. Panwala (U09EE515), Mr. Rohit Kumar

(U09EE520) and Mr. Neelesh Kumar Gupta (U09EE541) is approved.

Examiners

Supervisor

Examiner : 1

Examiner : 2

Examiner : 3

Date :

Place : Surat

ACKNOWLEDGMENT

We must acknowledge the strength, energy and patience that almighty GOD bestowed upon

us to start & accomplish this work with the support of all concerned, a few of them we are

trying to name hereunder.

It has been great privilege for us to work under esteemed personality respected Janak J.

Patel, experienced and qualified professor in Electrical Engg. Department, SVNIT, Surat. It

is our achievement to be guided under him. He is a constant source of encouragement and

momentum that any intricacy becomes simple. We gained lot of invaluable guidance and

prompt suggestions from him during dissertation preliminary work. We will be indebted of

him forever and we take pride to work under him.

I express deep sense of regards to all those who directly or indirectly helped me during this

dissertation preliminary work.

hpHighlight

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i

ABSTRACT

Compared to other motors, Permanent Magnet Synchronous Motor (PMSM) has higher

efficiency. To maintain that efficiency, the control has to be efficient. In this work, study is

carried out on scalar and vector control of PMSM. Further, Sensor less vector control theory

using Extended Kalman Filter (EKF) is discussed.

An attempt is made to run the PMSM in open loop scalar control and in vector control by

generating the sinusoidal supply from the inverter which can be generated by giving Space

Vector Pulse Width Modulation (SVPWM) pulses. These SVPWM pulses are generated with

the help of STM32F4 which has Cortex-M4 core.

The speed and torque of the motor can be varied by varying frequency and amplitude of the

sinusoidal supply using SVPWM. The performance of scalar and vector control is compared

in the study.

ii

TABLE OF CONTENT

ABSTRACT ............................................................................................................................................. i

TABLE OF CONTENT .......................................................................................................................... ii

LIST OF SYMBOLS ............................................................................................................................. iii

LIST OF FIGURES ................................................................................................................................ ii

LIST OF TABLES .................................................................................................................................. ii

1. INTRODUCTION .............................................................................................................................. 1

2. SCALAR CONTROL OF PMSM ...................................................................................................... 3

2.1 SCALAR CONTROL OF PMSM ................................................................................................ 3

3. SPACE VECTOR MODULATION TECHNIQUE .......................................................................... 5

3.1 TIME CALCULATION TO GENERATE VS ......................................................................... 6

4. VECTOR CONTROL OF PMSM ...................................................................................................... 9

4.1 DYNAMIC MODEL OF PMSM ............................................................................................... 10

4.2 EXTENDED KALMAN FILTER .............................................................................................. 11

4.2.1 DISCRETE-TIME PREDICT AND UPDATE EQUATIONS ............................................ 11

4.3 SENSOR LESS DRIVE- BLOCK DIAGRAM AND ALGORITHM ....................................... 13

5. INTRODUCTION TO STM32F4 ..................................................................................................... 15

6. IMPLEMENTATION STEPS AND DIAGNOSTICS ..................................................................... 16

6.1 SVM CODE TO RUN PMSM ................................................................................................... 16

6.2 IMPLEMENTING OPEN LOOP SCALAR CONTROL .......................................................... 17

6.3 IMPLEMENTING SENSORLESS VECTOR CONTROL ....................................................... 17

6.4 APPLYING BAND PASS FILTER ........................................................................................... 20

7. CONCLUSION ................................................................................................................................. 22

8. REFERENCES ................................................................................................................................. 23

iii

LIST OF SYMBOLS

Mechanical synchronous angular

speed

Frequency of the currents

Number of poles

Emf induced in stator winding

Stator turns

Winding factor

Flux in motor

Applied voltage per phase

Generated torque

Flux linkage

Resultant space vector for SVM

DC link voltage

SVM Pulses duration

Time for state V1

Time for state V2

Time for state V0/7

Modulation index

Stator per-phase resistance

Stator per-phase inductance

Permanent magnetic flux linkage

Rotor position angle

Rotor speed

Current in phase a

Current in phase b

Current in phase c

, Rotating two phase current

, Stationary two phase currents

Voltage matrix

State variable matrix

Covariance matrix

Control matrix

System noise covariance matrix

Kalman gain

Measurement noise covariance

matrix

Output matrix

Systems state matrix

Partial derivative system matrix

Sampling time

ii

LIST OF FIGURES

Figure 3.1: Phase voltages and resultant vector ( ) -----------------------------------------------------------5

Figure 3.2: Space Vector Hexagon -------------------------------------------------------------------------------6

Figure 3.3: Vector in sector 1 ----------------------------------------------------------------------------------6

Figure 3.4: Switching waveforms for sector 1 ------------------------------------------------------------------8

Figure 4.1: Vector representation of coordinate ----------------------------------------------------------------9

Figure 4.2: Block diagram of Sensor less Vector control of PMSM ---------------------------------------13

Figure 4.3: Flowchart of the control algorithm to be implemented on STM32F4 ------------------------14

Figure 6.1: Currents and (for 25Hz) ---------------------------------------------------------------------16

Figure 6.2: Current and (for 50Hz) -----------------------------------------------------------------------17

Figure 6.3: Estimated rotor position by EKF ------------------------------------------------------------------18

Figure 6.4: ADC currents and ----------------------------------------------------------------------------18

Figure 6.5: Currents and ----------------------------------------------------------------------------------19



Figure 6.8: Tracking profiles of and --------------------------------------------------------------------20

Figure 6.9: Input and output currents for band pass filter ---------------------------------------------------21

Figure 6.10: Currents and using band pass filter -------------------------------------------------------21

Figure 6.11: Currents and using band pass filter ----------------------------------------------------21

LIST OF TABLES

Table 3.1: SVM switching states ---------------------------------------------------------------------------------5

Table 5.1: Reading for open loop scalar control -------------------------------------------------------------17

Table 6.2: Reading for vector controlled drive----------------------------------------------------------------20

Table 6.3: Reading for vector controlled drive with band pass filter---------------------------------------20

1

1. INTRODUCTION

A Permanent Magnet Synchronous Motor (PMSM) consists of a magnetic rotor and wound

stator construction. Its wound stators can rapidly dissipate heat to the motor housing and

environment. In contrast, a brush motor traps the heat under a non-conductive air gap,

resulting in greater efficiency and power density for the PMSM design providing high torque-

to-inertia ratios.

A PMSM generates magnetic flux using permanent magnets in the rotors, which are driven

by the stators synchronous rotational field. On the other hand, the flux that is applied by the

stator (the armature-reaction flux) generates torque most effectively when it is perpendicular

to the flux generated by the rotors.

A PMSM abandons the excitation winding and the rotor turns at the same speed as the stator

field. The PMSMs design eliminates the rotor copper losses, giving very high peak

efficiency compared with a traditional induction motor. The power-to-weight ratio of a

PMSM is also higher than induction machines. Advancements in power electronics and

microelectronics have enabled the application of PMSMs for high-performance drives,

where, traditionally, only DC motors were applied. Thanks to sophisticated control methods,

a PMSM offers same control capabilities as high performance four quadrant DC drives. A

PMSM is an excellent alternative in an appliance application like refrigerators, air

conditioners etc.

The scarcity of primary energy resources and the ecological pollution crisis have made

energy saving practices unavoidable. Since most generated electricity is consumed by electric

motors, their loss minimization has attracted much attention recently. Although interior

permanent magnet (IPM) motors are inherently efficient, their optimum efficiency is highly

reliant on their control strategy.

Historically, several general controllers have been developed:

Scalar controllers: Despite the fact that Voltage-Frequency (V/f) is simplest

controller, it is the most widespread, and is utilized in majority of the industrial

applications. It is known as a scalar control and acts by imposing a constant relation

between voltage and frequency. The structure is simple and it is normally used

without speed feedback. However, this controller does not achieve a good accuracy

in both speed and torque responses, mainly due to the fact that the stator flux and

torque are not directly controlled.

2

Vector Controllers: In these types of controller, there are control loops for

controlling both the torque and the flux. The most widespread controllers of this type

are the ones that use vector transform such as either Park or Ku. The main

disadvantages are the huge computational capability required and the compulsorily

good identification of the motor parameters.

In chapter 2, we discuss scalar control theory. Chapter 3 discusses the theory and

implementation of SVPWM. We discuss sensor less vector control technique and extended

Kalman filter, a state variable estimator in chapter 4. Chapter 5 gives an introduction to

STM32F4; a Cortex-M4 architecture controller used to implement the project. Chapter 6

discusses the steps undertaken to implement the project and records the data taken during the

experiments. In chapter 7 we derive a conclusion from observations taken in these

experiments.

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3

2. SCALAR CONTROL OF PMSM

With scalar control, V/f tuning adjustments are used to provide a family of torque vs. speed

curves that are equivalent to the utility power torque vs. speed curve over as wide a speed

range as possible. The drives operating point is at the intersection of the selected drive torque

vs. speed curve and the characteristic torque vs. speed curve of the driven equipment.

Acceleration and deceleration, ramp time adjustments are used to prevent acceleration and

deceleration currents from exceeding safe limits. Current limit adjustments are used to reduce

the speed of the motor rather than shut down in the event that the load torque exceeds the safe

limit of the drive.

Current measurement can also be used to automatically trim various tuning adjustments to

provide enhanced performance. Properly tuned scalar drives with the best control

enhancements can provide 150% of rated torque to overcome static friction at zero speed and

to accelerate the load. They can also provide relatively smooth full torque operation at any set

speed down to about 10% of base speed.

In this dissertation, open loop scalar control of PMSM is carried out and is used as basis to

compare the performance of the vector control drive. In order to achieve open loop scalar

control of PMSM, we need a technique through which we can control the terminal voltage

and frequency of the voltage applied to the motor. The Space vector technique used in this

project is discussed in the next chapter.

2.1 SCALAR CONTROL OF PMSM

The mechanical synchronous angular speed is proportional to the frequency of the

supply voltage

1

The RMS value of the induced voltage of AC motors is given as

2

By neglecting the stator resistive voltage drop and assuming steady state conditions, the stator

voltage is identical to the induced one and the expression of magnetic ux can be written as

3

4

To maintain the stator ux constant at its nominal value in the base speed range, the voltage-

to-frequency ratio is kept constant, hence the name V /f control. If the ratio is dierent from

the nominal one, the motor will become over-excited or under-excited. The rst case happens

when the frequency value is lower than the nominal one and the voltage is kept constant or if

the voltage is higher than that of the constant ratio V/f. This condition is called over-

excitation, which means that the magnetizing ux is higher than its nominal value. An

increase of the magnetizing ux leads to a rise of the magnetizing current. In this case the

hysteresis and eddy current losses are not negligible. The second case represents under-

excitation. The motor becomes under-excited because the voltage is kept constant and the

value of stator frequency is higher than the nominal one. Scalar control of the synchronous

motor can also be demonstrated via the torque equation of SM, similar to that of an induction

motor.

After neglecting the stator resistance and rewriting the reactance and angular speed as a

function of frequency, it is possible to rewrite the maximal torque as

4

All constant values in 4 can be replaced with constant C. Taking into account equation 4, the

torque will be constant in a wide speed range up to the nominal speed if the ratio of stator

voltage and frequency is kept constant. Expression 4 is valid only if can be neglected in

comparison with the synchronous reactance . This is valid for big machines around the

rated frequency. Since is proportional to the stator frequency, resistance cannot be

neglected in the range of low frequencies (less than 10Hz). Therefore, keeping the constant

ratio / is not enough during the full speed range. In the range of low frequencies, the

decreasing of the voltage must be slower. This can be achieved by keeping the voltage at a

constant value in the region of the low frequencies.

5

3. SPACE VECTOR MODULATION TECHNIQUE

V/F control using the Sine PWM algorithm is popular. However, this algorithm has certain

drawbacks like inability to fully utilize the DC link Voltage and more harmonic content

which affect the overall system efficiency. The Space Vector Modulation (SVM) technique

helps in reducing the drawback of SPWM and thereby increases the overall system

efficiency.

The SVM is a sophisticated, averaging algorithm which gives 15% more voltage output

compared to the SPWM, thereby increasing the DC link utilization. It also minimizes the

harmonic content as well as switching losses.

120

120

120

Vs

van

Vbn

Vcn

Figure 3.1: Phase voltages and resultant vector ( )

The required balanced 3 phase voltages can be represented as a single space reference voltage

( ) (Figure 3.1). By controlling the amplitude and the frequency of , the motor voltage and

motor frequency can be controlled. The technique gives rise to eight distinct switching states

of the Voltage Switched Inverter (VSI). Table 3.1 lists all the possible VSI switching states

and respective line to neutral voltages. States 1 through 6 are called the active states, as the

energy is supplied from the supply to the motor. States 0 and 7 are called inactive states, as

no energy is supplied from the supply to the motor. Each state can be represented as the

voltage vector in space. Figure 3.2 shows the space vector representation of all the possible

switching states.

Table 3.1: SVM switching states

Switching state On switches Van V bn V cn Space voltage vector

0 S2,S4,S6 0 0 0

1 S1,S4,S6 2/3 VDC -1/3 VDC -1/3 VDC

2 S1,S3,S6 1/3 VDC 1/3 VDC -2/3 VDC

6

3 S2,S3,S6 -1/3 VDC 2/3 VDC -1/3 VDC

4 S2,S3,S5 -2/3 VDC 1/3 VDC 1/3 VDC

5 S2,S4,S5 -1/3 VDC -1/3 VDC 2/3 VDC

6 S1,S4,S5 1/3 VDC -2/3 VDC 1/3 VDC

7 S1,S3,S5 0 0 0

Sector 2

sector3

Sector 4

Sector 5

Sector 6

Sector 1

V1(S1,S4,S6)

V2(S1,S3,S6)(S2,S3,S6)V3

(S2,S3,S5)V4

(S2,S4,S5)V5

Vs

Figure 3.2: Space Vector Hexagon

3.1 TIME CALCULATION TO GENERATE VS

Let us take an example where is in sector 1 at a vector angle (), as shown in figure 3.3. It

is assumed that during time , remains steady. For implementing the conventional SVM

using SVM switching rules, is split as shown in Equation 5.

Y Axis

X Axis

V0/7

V2

Vs

V1

/3

Ta

Vdc

Figure 3.3: Vector in sector 1

7

(

) (

) (

) 5

Equation 6 means that the VSI is in active state 1 for TA time and it is in active state 2 for TB

time. For the remaining time of TS, no voltage is applied. This can be achieved by applying

inactive state 0 (or 7) for the remaining time (or ).

6

The time intervals, , and , have to be calculated such that the average volt seconds

produced by the vectors, , and along the X and Y axes, are the same as those

produced by the desired reference space vector .

The modulation index or amplitude ratio is defined as:

| |

7

Resolving along the X and Y axes, we get:

( ) ( (

) | |

8 (

) | |

Solving for and , we get:

(

)

9

( )

can be found from Equation 6. For better THD, is split into two and then applied at

the beginning and at the end of . The typical VSI switching waveforms in Sector 1, as

defined by Equation 5-9 and the switching rules for the conventional SVM using centre

aligned PWM, are as given in Figure 3.4.

8

Figure 3.4: Switching waveforms for sector 1

We can observe the same axes of symmetry in all the waveforms as shown in Figure 3.4.

These symmetries are mainly responsible for having lower THD in SVM compared to Sine

PWM in the linear operating region.

From Figure 3.4, it is clear that in the linear operating region, the maximum line-to-line

voltage amplitude can be achieved when is rotated along the largest inscribed circle in the

space vector hexagon. In mathematical terms, this is equivalent to:

10

From figure 3.3 and above equation can be

(

)

(

)

11

By solving equation 5, 8 & 11 we get:

12

Equation 8 shows that it is possible to get line-to-line voltage amplitude as high as VDC

using the SVM algorithm in the linear operating range. This is the main advantage of the

SVM algorithm when compared to the Sine PWM algorithm. Due to higher line-to-line

voltage amplitude, the torque generated by the motor is higher. This results in better dynamic

response of the motor.

9

4. VECTOR CONTROL OF PMSM

Vector control of a motor is a technique in which a three phase motor is mathematically

expressed as a DC motor to facilitate its control. By mathematically transforming the three

phase currents into dc quantities helps to control flux and torque independently just like a DC

motor. It has been proven that vector control gives better dynamic response than a scalar

control.

The 3 phase currents can be transformed into dc quantities by using Clark and Park

Transformations [4].

Clark Forward Transform:

[

]

[

] [

] 13

Park Forward Transform:

[

] *

+ [

] 14

The above transformations are performed

on three phase currents to obtain the

following vector representation (Figure

4.1). As shown in figure, component

of the current is responsible for flux

generation, whereas, component is

responsible for torque generation. Thus,

we have mathematically expressed three

phase motor as a DC motor. The motor

can then be controlled by individually

controlling the flux and torque generating

components of the currents.

In case of PMSM, the air gap flux is generated by the permanent magnet. Thus, the flux

generating component of the three phase stator winding should be zero.

A sensor less vector control of a motor is a technique where a vector control is achieved

without employing a rotor position sensor. Instead the rotor position is estimated by using a

state variable estimator. Various techniques like Extended Kalman Filter and Sliding Mode

Figure 4.1: Vector representation of coordinate

10

Observer are employed to estimate the rotor position and speed with the help of measureable

state variables like currents and voltages.

A sensor less drive has the following advantages:

It is cost effective as the cost of sensor is eliminated.

It gives high speed stability with load change

It gives high torque even at low speeds.

It has better dynamic response to load change.

4.1 DYNAMIC MODEL OF PMSM

The system considered is a permanent magnet synchronous motor with permanent magnets

mounted on the rotor, and a sinusoidal flux distribution. A dynamic model for this motor in a

stator fixed reference frame (, ), by choosing the current components , , the rotor speed

, and the rotor position as state variables is as follows:

( )

15

( )

16

17

18

The voltage components and are the deterministic control inputs of the system. Both

the voltage and current components are measurable quantities. They are obtained from the

three phase stator components by a linear (Clarke) transformation:

(

)

19

( )

20

Similar equations are obtained for voltages.

11

4.2 EXTENDED KALMAN FILTER

The Kalman filter dynamics results from the consecutive cycles of prediction and filtering.

The dynamics of these cycles is derived and interpreted in the framework of Gaussian

probability density functions. Under additional conditions on the system dynamics, the

Kalman filter dynamics converges to a steady-state filter and the steady-state gain is derived.

The innovation process associated with the filter, that represents the novel information

conveyed to the state estimate by the last system measurement, is introduced. The filter

dynamics is interpreted in terms of the error ellipsoids associated with the Gaussian

Probability Distribution Function (PDF) involved in the filter dynamics.

When either the system state dynamics or the observation dynamics is nonlinear, the

conditional probability density functions that provide the minimum mean-square estimate are

no longer Gaussian. The optimal non-linear filter propagates these non-Gaussian functions

and evaluates their mean, which represents a high computational burden. A non-optimal

approach to solve the problem, in the frame of linear filters, is the Extended Kalman filter

(EKF). The EKF implements a Kalman filter for a system dynamics that result from the

linearization of the original non-linear filter dynamics around the previous state estimates.

4.2.1 DISCRETE-TIME PREDICT AND UPDATE EQUATIONS

The EKF predict update equations are shown below with the above discussed model

implemented on it [2].

PREDICT

Predicted state estimate:

[ ( ) ] 21

Predicted covariance estimate:

[ ] 22

UPDATE/INNOVATION

Innovation step:

( ) 23

Innovation covariance:

24

12

Near-optimal Kalman gain:

(

)

25

Where means previous estimate and means estimated result,

[ ]

[ ]

[ ]

( )

[

]

[

]

*

+

[

]

[

]

[

]

*

+

,

and

Where, is the starting values given as

input to EKF and updates in iteration.

13

4.3 SENSOR LESS DRIVE- BLOCK DIAGRAM AND ALGORITHM

A schematic of the drive taken into consideration is shown in Figure 4.2. The power stage of

the drive consists of a sinusoidal, isotropic, PM synchronous motor fed by a voltage source

SV (State Vector) PWM inverter. The digital control and estimation system of the drive is

also shown in Fig. For the sake of simplicity, unity gains for all the feedbacks and for the

power converter have been assumed.

The main control tasks are the speed control, the predictive current control and the generation

of the PWM commands for the inverter switches. The speed controller delivers the q-axis

current reference (torque reference). The d-axis current is kept equal to zero to maximize the

torque/current ratio. The current controller evaluates the references of the stator voltages by a

predictive algorithm. The estimation task is to derive the speed and position of the motor by

means of an extended Kalman filter. Therefore the controller uses the predicted current

estimates rather than the actual current feedbacks.

In order to apply the Extended Kalman filter algorithm as described in the previous Section,

the motor state equations and the output equations must be derived. To this purpose the motor

is described in a stationary two-axis reference frame. The model voltages and currents are

therefore related to the actual physical quantities by simple linear combinations with constant

coefficients. In addition the motor equations are derived supposing rotor inertia of infinite

value. As a consequence the torque equation reduces to a speed derivative equated to zero

and any mechanical load parameter as well as the load torque disappears in the motor

equations. This means, to assume a constant speed in the prediction step and to produce the

entire speed dynamics by the innovation step.

Figure 4.2: Block diagram of Sensor less Vector control of PMSM

14

Permanent Magnet Synchronous Motor is a fourth order nonlinear system. However it is

linear with respect to the input and output. Such a linearity remains also with respect to the

physical three-phase voltages and currents because of the linear transformations which relate

physical quantities to stationary two-axis quantities. The description of the motor dynamics in

a stationary reference frame is thus to be preferred to the description in a synchronous

rotating reference frame, as the former does not introduce further nonlinearities besides those

inherent to the motor.

To implement the control algorithm using a microcontroller, the following flow chart (Figure

4.3) needs to be implemented. According to the flowchart, the control scheme will use

Extended Kalman filter to predict the state variable for the step k by using the data of step k-

1. The predicted values will then be applied to the PI controller to calculate errors and do

corrective action. Based on this, new voltage values will be generated which will be used to

update the duty cycle of SVM. Now the three phase currents will be read next and is used by

the innovation step. It is to be noted that the microcontroller should be powerful enough to

process the loop as fast as possible in order to get required dynamic characteristics.

Figure 4.3: Flowchart of the control algorithm to be implemented on

STM32F4

15

5. INTRODUCTION TO STM32F4

The STM32F405xx and STM32F407xx family is based on the high-performance ARM

Cortex-M4 32-bit RISC core operating at a frequency of up to 168 MHz The Cortex-M4

core features a Floating point unit (FPU) single precision which supports all ARM single

precision data-processing instructions and data types. It also implements a full set of DSP

instructions and a memory protection unit (MPU) which enhances application security. The

STM32F405xx and STM32F407xx family incorporates high-speed embedded memories

(Flash memory up to 1 MByte, up to 192 Kbytes of SRAM), up to 4 Kbytes of backup

SRAM, and an extensive range of enhanced I/Os and peripherals connected to two APB

buses, three AHB buses and a 32-bit multi-AHB bus matrix.

Key features of the microcontroller kit:

STM32F407VGT6 microcontroller featuring 1MB of Flash memory, 192 KB of

RAM in an LQFP100 package running at 168 MHz (max) providing peak throughput

of 210 MIPs

On-board ST-LINK/V2 debugger for hardware level debugging (SWD connector for

programming and debugging).

312-bit, 2.4MSPS A/D converters: up to 24 channels (simultaneous sampling of all

three ADCs is possible).

212-bit D/A converters.

General-purpose DMA: 16-stream DMA controller with FIFOs and burst support.

Up to 17 timers: up to twelve 16-bit and two 32-bit timers up to 168MHz, each with

up to 4 IC/OC/PWM or pulse counter and quadrature (incremental) encoder input.

6 complimentary PWM channels with programmable dead time insertion.

Up to 15 communication interfaces like UART, I2C, and SPI etc.

5V tolerant GPIO pins

Floating point unit

Incremental Encoder interface

Motor drive and application control

Medical equipment

Industrial applications: PLC, inverters, circuit breakers

Printers and scanners

Alarm systems, video intercom, and HVAC

Home audio appliances

16

6. IMPLEMENTATION STEPS AND DIAGNOSTICS

The implementation of the project is as follows. Firstly, the SVM code is written to generate

the required magnitude and frequency of the voltage to be applied to the motor. The open

loop scalar control is implemented next and is used to compare the performance of vector

control. The proposed sensor less vector control technique is implemented and the results are

compared.

6.1 SVM CODE TO RUN PMSM

As explained in article 3.1, the SVM code is written to obtain the required magnitude and

frequency of the voltage at the output of the inverter. Figures 6.1 and 6.2 shows currents

and for motor running at 25 Hz and 50Hz resp. It is to be noted that the currents are

sinusoidal in nature. Thus, generalizing the SVM algorithm required magnitude and

frequency of 3 phase balanced voltages were obtained.

Figure 6.1: Currents and (for 25Hz)

17

Figure 6.2: Current and (for 50Hz)

6.2 IMPLEMENTING OPEN LOOP SCALAR CONTROL

The next step is to write an algorithm which allows the user to change the speed of motor via

key press. While doing this, V/f ratio is maintained constant according to the scalar control

theory. Once the algorithm is implemented the motor is run at 25 Hz and pull-out torque of

the motor along with the corresponding current is measured and tabulated in table 6.1.

Table 5.1: Reading for open loop scalar control

DC link Voltage Load Phase Current

16 V 1.9 Kg 2.2 Amp.

6.3 IMPLEMENTING SENSORLESS VECTOR CONTROL

In this process, the Extended Kalman Filter (EKF) is implemented to estimate the rotor

position. The estimated rotor position is then used to convert the three phase currents into dc

quantities which are required for vector control. The obtained dc currents are then fed into a

pi controller which takes corrective action on the motor.

Figure 6.3 shows the rotor position estimated by the EKF in synchronism with a phase

current. Note that the current is not pure sinusoidal as the algorithm is designed to take

corrective action every PWM cycle. As a result, the modulation index changes every PWM

cycle. A significant change in modulation after every PWM cycle results in the current shown

in Figure 6.3.

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Figure 6.3: Estimated rotor position by EKF

Figure 6.4 shows the two out of the three phase currents measured by the ADC. Figure 6.5

shows the two phase currents ( and ) obtained by Clarke Transform. It can be noted that

the two currents are 90 degree phase displaced with each other. Figure 6.6 shows and in

phase, which corroborates with theory. Figure 6.7 shows and dc currents obtained using

EKF and Park Transform.

Figure 6.4: ADC currents and

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Figure 6.5: Currents and



20

Table 6.2 contains the parameters and measurements for the vector controlled drive. Figure

6.8 gives the tracking profile for currents and .

Table 6.2: Reading for vector controlled drive

DC Link Voltage Load Phase Current

16 V 1.9Kg 1.7 Amp.

Figure 6.8: Tracking profiles of and

6.4 APPLYING BAND PASS FILTER

As noted in 6.3 the currents in the motor is not pure sinusoidal. This can affect the

performance of EKF. Thus, in order to study the effects of sinusoidal and non-sinusoidal

currents on the drive we implemented a digital band pass filter on the currents to extract 25Hz

component and studied its performance.

Figure 6.9 shows the effect of band pass filter on current. The filter output has reduced noise

which theoretically should improve the performance of EKF. Figure 6.10 shows and

currents obtained after band pass filter is applied to the ADC currents. Figure 6.11 shows

and currents with reduced noise and ripple as compared to figure 6.7. The table 6.3

gives data acquired from the vector control drive for similar running conditions for the motor

after introducing the band pass filter.

Table 6.3: Reading for vector controlled drive with band pass filter

DC Link Voltage Load Phase Current

16 V 1.9Kg 1.7 Amp.

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Figure 6.9: Input and output currents for band pass filter

Figure 6.10: Currents and using band pass filter

Figure 6.11: Currents and using band pass filter

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7. CONCLUSION

It can be inferred from tables 6.1, 6.2 and 6.3 that vector control techniques is more effective

in efficiently running the motor than scalar control. The current drawn from source is less in

vector control drive than in scalar control drive for a given DC link voltage, frequency and

load. There is 22.7% reduction in the current drawn by the motor to drive 1.9Kg load at 16 V

DC Link.

Further, it is noted that the currents drawn by the drive with and without band pass filter is

same (1.7 amp.). However, the drive using band pass filter has less current ripple compared

to drive without band pass filter (Observed using the deflection in ammeter needle).

For the experimental study presented above, the system performance was observed to be

quiet good for a vector control drive with and without band pass filter. The experiment

demonstrates that a sensor less vector control drive helps in increasing the efficiency of the

motor while saving the cost of encoder and thus, reducing the overall cost of the drive.

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8. REFERENCES

[1]: Sensor less Full-digital PMSM Drive with EKF estimation of speed and rotor position-

SilverioBolognani, Roberto Oboe, and Mauro Zigliotto

[2]: Effective Estimation of speed and rotor position of a PM Synchronous Motor Drive by a

Kalman Filtering Technique- A. Bado, S. Bolognani, M. Zigliotto

[3]: An introduction of Kalman Filter Greg Welch and Gary Bishop

[4]: VF Control of 3-Phase Induction Motor Using Space Vector Modulation- Rakesh Parekh,

Microchip Technology Inc.

[4]: Coordinate transform- www.fujitsu.com

[5]: Sensor less PMSM Vector Control with a Sliding Mode Observer for Compressors

Using MC56F8013- www.freescale.com

[6]: Documents and reference manual of STM32f4-www.st.com