Design of Fuzzy PID controller for
BRUSHLESS D.C MOTORS
Working of BLDC -MOTOR
As there is no commutator ,the
current direction of the conductor
on the stator controlled
Rotor consists the permanent
magnet where as stator consist a
no. of windings. Current through
these winding produces magnetic
field and force.
Hall sensor used to determine
the position during commutation.
• Each sequence has
•one winding energized
positive (current into the
•one winding energized
negative (current out of the
•one winding non-energized
•When a rotor pole passes a
Hall-Effect sensor, get a high
or low signal, indicating that
a North or South pole
Why we need controller?
• In close loop response four characteristics are important
• 1)Rise time: the time it takes for the plant output to rise beyond
90% of the desired level for the first time.
• 2)Overshoot time : how much the peak level is higher
than the steady state, normalized against the steady state.
• 3)Settling time: the time it takes for the system to
converge to its steady state.
• 4) Steady state error: The difference between the
steady-state output and the desired output.
• We wanted the PID controller to satisfy the following criteria:
▫ Settling time --less
▫ Overshoot and rise time --less
▫ Steady-state error less than 1%
Conventional PID controller:
• Proportional-integral-derivative (PID) control provides a
generic and efficient solution to real world control problems
It is used to eliminate error:
• Error is defined as the difference between set-point and
measurement. (error) = (set-point) - (measurement)
• The output of PID controller will change in response to the
• A PID controller is simple three-term controller
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 KD to reduce the overshoot and settling time.
• iv) Use KI to eliminate the steady-state error.
• The design of the BLDCM drive involves a
complex process such as modeling, control scheme
selection, simulation and parameters tuning etc
PID controller working is not good for non-linear and
• Fuzzy PID control method is a better
method of controlling, to the complex and unclear model
systems, it can give simple and effective control
• Fuzzy logic is a form of many-valued logic or
probabilistic logic; it deals with reasoning that is
approximate rather than fixed and exact.
• Fuzzy logic variables may have a truth value that ranges
in degree between 0 and 1.
• Fuzzy logic can be described simply as "computing with
words rather than numbers"
Fuzzy logic includes:
• 1)Fuzzy Set: A fuzzy set is collection of related items which
belong to that set to different degrees in interval [0,1].
• 2)Fuzzy rules:
• In fuzzy logic these rules are used to make decisions.
• IF-THEN rules :
―IF temperature very cold THEN stop fan‖
• AND, OR, and NOT operators :
• ―IF temperature IS hot AND pressure IS low, THEN fan ON‖ .
NOT x = (1 - truth(x))
x AND y = minimum(truth(x), truth(y))
x OR y = maximum(truth(x), truth(y))
Variables whose values are words or sentences in human
language are called linguistic variables
For the case of motor, speed can be taken as linguistic variable
A set is characterized by a membership (characteristic)
function which assigns to each object a grade of
membership ranging between zero and one.
Diagrammatic representation of motor speed in terms
of linguistic expression
Fuzzy logic controller (FLC):
• A fuzzy control system is a control system based
on fuzzy logic—a mathematical system that
analyzes analog input values in terms
of logical variables that take on continuous values
between 0 and 1.
• Fuzzy control is based on fuzzy logic, 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"
Fuzzy logic controller:
• A fuzzy controller can include empirical rules,
and that is especially useful in operator controlled plants.
It follows following processes
• Every crisp value of input we attribute a set of degrees of
membership to fuzzy sets defined in the universe of
discourse for that input.
It measure the values of input variable.
It performs scale mapping that transfers the range of
values of input variables into their corresponding
universe of discourse(fuzzy set) or into degree of
• The fuzzy IF-THEN rule expresses a fuzzy implication
relation between the fuzzy sets of the premise and the
fuzzy sets of the conclusion. It includes decision logic
operators such as OR, AND ALSO etc
• IF LOAD INCREASES THEN SPEED REMAIN
• It includes:
Matching of the facts with the rule premises.
Implication The next step is the determination of
the individual rule output.
Aggregation: The collective sum of each rule is
obtained in this step.
This process to obtain crisp output from fuzzy sets is
called defuzzification. It is the reverse process of
In fuzzy controller don’t require equations , its algorithm is
rules that is made by human. Fuzzy controller make
decisions automatically according to these rules.
Fuzzy logic in MATLAB:
Fuzzy logic is a problem-solving control system methodology that lends
itself to implementation in systems ranging from simple, small,
embedded micro-controllers to large, networked, multi-channel PC or
workstation-based data acquisition and control systems. It can be
implemented in hardware, software, or a combination of both.
Characteristics of motor, 1500 rpm
with no load
Characteristics of motor, 1500 rpm
Step up Characteristics of motor,10001500 rpm with no load
Step down Characteristics of motor,15001000 rpm with no load
Step down Characteristics of motor,15001000 rpm with load
• It can work with less precise
• It does not need fast
• Tuning of fuzzy PID controller
is easy ,more robust than
other non-linear controllers.
• Fuzzy controllers have better
stability, small overshoot, and
Fuzzy PID CONTROLLER
• Conventional PID controller
algorithm is simple, stable,
easy adjustment and high
• It does not require processor
• Tuning PID control parameters
is 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.
Limitations of fuzzy PID controller:
• · Require more fine tuning and simulation before
• 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
• An fuzzy logic controller cannot be achieved by
trial and- error
Fuzzy PID controller reduces overshoot very significantly as compare
to conventional PID controller . Similarly fuzzy PID controller gives
more robust with change of load and speed but PID controller
produces do not robust sudden change in speed and load.
So, Fuzzy PID controller is better than simple PID.
•  Q.D.Guo,X.MZhao. BLDC motor principle and
technology application [M]. Beijing: China electricity
•  Chuen Chien Lee, ―Fuzzy Logic in Control
Systems:Fuzzy Logic controller–Part 1‖ 1990 IEEE
•  Chuen Chien Lee, ―Fuzzy Logic in Control Systems :
Fuzzy Logic controller Part 2‖ 1990 IEEE .
• Comparison between Conventional and Fuzzy Logic
PID Controllers for Controlling DC Motors by IJCSI
International Journal of Computer Science Issues, Vol. 7,
Issue 5, September 2010
• Design of Fuzzy PID Controller for Brushless DC
Motor 2012 International Conference on Computer
Communication and Informatics (ICCCI -2012), Jan. 10
– 12, 2012, Coimbatore, INDIA