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Intelligent Speed Controller for
BRUSHLESS D.C MOTORS
SEMINAR PRESENTATION
GuideMs. Rinu Alice Koshy
Assistant Professor
Presented by,M. Sankar
Reg No:11012477
S7 EEE
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The seminar focuses on the design and implementation of fuzzy
based PID controller for BLDC Motors in order to keep the speed
of the motor to be constant when the load varies. The
effectiveness of the same in speed control of BLDC on load
variation is also proven by simulation
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Rotor: Permanent magnet
Stator consist of a number of windings. Current through
these winding produces magnetic field and force
No commutator ,the current direction of the stator conductoris controlled electronically
Stator current is commutated through electronic switches to
appropriate phases
Hall sensor used to determine the rotor position duringcommutation
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Halls Sensors sense the position of the rotor and feeds to
decoder circuit
The Decoder Circuit turns appropriate switches on and off
The voltage through the specific coils turns the motor
Table 1: CLOCKWISE ROTATION SEQUENCE Fig 1: BLDC Motor Working Animation 4
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Fig 2: Voltage Source Inverter 5
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Fig 3: Block Diagram of speed control of BLDC Motor
Two control loops:
The inner loop synchronizes the inverter gates signals with theelectromotive forces.
The outer loop controls the motor's speed by varying the DC bus
voltage.
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A PID controller is simple three-term controller {P- Proportional,
I- Integral, D-derivative }. The transfer function of the most basic
form of PID controller ,is
Where KP= Proportional gain, KI= Integral gain and
KD= Derivative gain.
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Error Signal: e
The Control signal ufrom the controller to the plant is equal to
the Proportional gain (KP) times the magnitude of the error plus theIntegral gain (KI) times the integral of the error plus the Derivative
gain (KD) times the derivative of the error.
The output of PID controller will change in response to the error
Fig 4: Simulation model of PID Controller
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In close loop response the four important characteristics are:1)Rise time: Time taken by output to cross 90% of desired level for the
first time.
2)Peak Overshoot : How much the peak level is higher than the steady
state level
3)Settling time: time taken for the system to converge to its steady state
4)Steady state error: The difference between the steady-state output
and the desired output
For optimal performance the PID controller must satisfy the following
criteria:
Less Settling time less
Low Overshoot and rise
Steady-state error less than 1%
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Typical steps for designing a PID controller are
i) Determine what characteristics of the system needs to be
improved.
ii) Use KP
to decrease the rise time.
iii) Use KDto reduce the overshoot and settling time.
iv) Use KI to eliminate the steady-state error.
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The design of the BLDCM drive involves a complex processsuch as modeling, control scheme selection, simulation and
parameters tuning etc.
PID controller working is not good for non-linear andcomplex systems
Conclusion: Fuzzy PID control method is opted as it is a better
method of controlling complex and unclear systems.
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Fuzzy logic is a form of many-valued logic or probabilistic
logic; it deals with reasoning that is approximate rather than fixed
and exact.
The fuzzy logic, unlike conventional logic system, is able to
model inaccurate or imprecise models.
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In fuzzy logic we define human readable rules to form
the target system. For instance assume we want to
control the room temperature, first of all we define
simple rules:
If the room is hot then cool it down
If the room is normal then don't changetemperature
If the room is cold then heat it up
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Fig 5: Fuzzy Logic Working 14
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Linguistic variables are the input or output variables of the
system whose values are in natural language.
Example:
The room is hotlinguistic value
How much it is hotlinguistic variable
Fuzzy logic variables may have a truth value that ranges in
between 0 and 1.17
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A fuzzy control system is a control system based on fuzzy
logica system that analyzes input values in terms
of logical variables that take on continuous values between 0
and 1.
Fuzzy logic is a logical system which is much closer to human
thinking and natural language than traditional logical systems
Fuzzy control can be described simply as "control with
sentences rather than equations"
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The Values of Kp, Ki and Kd values of PID Controller is shown
in below Table 2 are obtained by using the ZN method.
Speed controlled indirectly by controlling the Voltage Source
inverter.
Fuzzy logic controller output is the inner dc Voltage controller.
The Voltage is controlled by varying the dc voltage.
Table 2: PID VALUES
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The fuzzy control rule is in the form of:
IF e=E and de=CE Then UPD = O/P.
These rules are written on rule base look-up Table 3. This
rule base structure is Mamdani type
Table 3: FUZZY RULES 20
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Linguistic variables which implies inputs and output have
been classified as: NB, NM, NS, Z, PS, PM, PB.
Negative Big (NB), Negative Medium (NM), Negative
Small (NS), Zero (Z), Positive Small (PS), Positive
Medium (PM), Positive Big (PB).
Inputs and output are all normalized in the interval of [-10,10] as shown
Fig 6: Triangular Membership Function 21
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The output equation obtained by Defuzzification by Centre of
Gravity Method is:
This is the fuzzy PID controller signal given to voltage source
inverter.
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The fuzzy rules are extracted from knowledge of PID controller
and human experience about the process. These rules contain the
input/the output relationships that define the control strategy. Each
control input has seven fuzzy sets so that there are at most 49 fuzzy
rule.
Table 3: FUZZY RULES23
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Fig 7: Fuzzy PID Controller
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Characteristics of motor, 1500 rpm with no load
tr %Mp ts tr %Mp ts
1500
no-
load
0.0202 16.53 0.35 0.0061 13.13 0.10
Speed PID Controller Fuzzy PID Controller
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Characteristics of motor, 1500 rpm with load of 5N
tr %Mp ts tr %Mp ts
1500
load
0.0209 15.53 0.40 0.0077 3.6 0.15
Speed PID Controller Fuzzy PID Controller
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Step down Characteristics of motor,1500-1000 rpm with no load
tr %Mp ts tr %Mp ts
1500
no-load
0.0202 16.53 0.35 0.0061 13.13 0.15
Speed PID Controller Fuzzy PID Controller
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Step down Characteristics of motor,1500-1000 rpm with load
tr %Mp ts tr %Mp ts
1500
load
0.0209 15.53 0.35 0.0077 3.6 0.15
Speed PID Controller Fuzzy PID Controller
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Fuzzy PID Controller PID Controller
Conventional PID controlleralgorithm is simple, stable,
easy adjustment and high
reliability.
Tuning PID control parametersis very difficult, poor
robustness, therefore, it's
difficult to achieve the optimal
state under field conditions in
the actual production
When load varies it becomes
unstable, give more overshoot.
It can work with less preciseinputs.
Tuning of fuzzy PID controller
is easy ,more robust than other
non-linear controllers.Fuzzy controllers have better
stability, small overshoot, and
fast response.
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Require more fine tuning and simulation before operational.
If the a reliable expert knowledge is not Available , or If the
controlled system is too complex to derive the required
decision rules, development of a fuzzy logic controller become
time consuming and tedious or sometimes impossible.
A fuzzy logic controller cannot be achieved by trial and- error
method. Only ZN method is used.
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With results obtained from simulation, it is clear that for the
same operation condition, the BLDC speed control using
Fuzzy PID controller technique had better performance than
the conventional PID controller.
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REFERENCES
[1] Dr. R Arulmozhiyal An Intelligent Speed Controller for Brushless DC Motor
2012 7th IEEE Conference on Industrial Electronics and Applications (ICIEA)
[2]Atef Saleh Othman Al-Mashakbeh, Proportional Integral and Derivative Control
of Brushless DC Motor, European Journal of Scientific Research 26-28 July 2009,
vol. 35, pg 198-203.
[3]Q.D.Guo, X.MZhao BLDC motor principle and technology application[M].
Beijing: China electricity press, 2008.
[4]K. Ang, G. Chong, and Y. Li, PID control system analysis, design and
technology, IEEE Trans.Control System Technology, vol. 13, pp. 559-576, July
2005.
[5] C Zhang. BLDC motor principle and application [M].Beijing: Machinery Industry
Press, 1996.
[6] J.E Miller, "Brushless permanent-magnet motor drives," Power Engineering
Joumal, voI.2, no. 1 , Jan. 1988. 33
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