this is a research which has been done by me and my cellmates on controlling dc motor using pid controller:- a complete review

1. Speed control of Dc motor Using
PID controller: - A review
Submitted by :
Shriya Jain Chirag Jain
Abhishek Jangid

2. Contents
• Introduction
• Solution approaches used by researchers
• Common findings
• Strengths
• Weakness
• Motivation
• References

3. Introduction
 Proportional integral controllers (PID controller) are widely used industrial
controller due to its control loop feedback mechanism.
 It has simple structure and stable characteristics.
 It is also termed as three term controller as it controls all the three
controllers i.e. proportional, integral, and derivative.
 An electric motor is a device which uses electrical energy to produce
mechanical energy nearly always by the interaction of magnetic fields and
current carrying conductors.
 A DC machine is a device that can convert electrical energy into
mechanical energy.

4.  The principle of DC motor is based on the law of electromagnetic interaction.
According to this law whenever a current carrying conductor is placed in a
magnetic field, the conductor experiences an electromagnetic force.
 There are mainly two types of dc motors used in industry.
1. The first one is the conventional dc motor where the flux is produced by the current
through the field coil of the stationary pole structure.
2. The second type is the brushless dc motor (BLDC motor) where the permanent
magnet provides the necessary air gap flux instead of the wire-wound field poles.
 For tuning PID controller various optimization techniques are used like fuzzy ,
PSO,GA, Neuro fuzzy etc

5. Proportional Controller:-(Gain)
 The proportional part examines the magnitude of the error and it reacts
proportionally. A large error receives a large response.
 All three components of the PID algorithm are driven by the difference between
the process value (i.e. the current speed) and the reference point (i.e. the target
speed.) We will call this difference (or error) for one particular time step
and for that same time, we call the process value
and the reference point as
 The output value (i.e acceleration position) is called
 The proportional component simply calculates based on the size of the error
term by simply multiplying it by a constant
.
.
.

6. Integral Controller:-
 Enter the Integral component of the PID algorithm. Remember
back to your calculus days, integral refers to the area under a
curve. If you have a function, the integral of that function
produces a second function which tells you the area under curve
of the first function.
 Over time, the integral component compensates for the error in
the proportional component and the system stabilizes out at the
desired speed.
 I represents the steady state error of the system and will remove
set point / measured value errors.
(Reset)

7. Derivative Controller:- (Rate)
 The derivative of a function implies the rate of change of the
function output. If you know the function, you can take the
derivative of that function to produce a second function. For
any point in time, the derivative function will tell you the rate
of change (or slope) of the first function.
 Quickly we are closing on our target value (i.e. the rate of
change from each time step to the next) is an important piece
of information that can help us build a more stable system that
more quickly achieves the target value.

8. PID-Controller:-

9. Solution Approaches used by
researchers
Fuzzy PID controller
Genetic algorithm
Basic PID controller using Atmaga 16
A.N.F.C

10. Common findings
 GA based fuzzy controller controls the speed of BLDC motor with two closed loop. The inner loop is
current feedback loop which adjust the torque of the motor and outer loop is the fuzzy logic controller
whose control rules are optimized and parameters are adjusted.
 ANFS system based GA designed PID controller for shunt wound DC motor is much better in terms of rise
time and settling time than the conventional method.
 The speed of response was faster with fuzzy PID controller PID-FUZZY controller based on 16-bit
microcontroller has the sampling time of 10ms for ramp and random function for speed control of dc motor.
 60% of the value of current peak of PID controller was obtained by the Neuro-fuzzy controller.
 Steady state error obtained ±6 rpm in fuzzy self tuning methodology.
 PID-OPAmp technique was allowed the motor to be driven at the maximum current load of 50a for 5sec
before shutting down the motor.
 Fuzzy PID controller using FPGA gives dynamic response on BLDC motor to control the speed of motor
when the load varies.

11.  Dual line PID controller based on PSO gives the best performance for wide operating
conditions where as fixed gain feedback controllers fail to provide best control performance
over a wide range of off-nominal operating conditions .
 Fuzzy PID controller improves the static and dynamic performances of the system.
 Self tuning PID controller has a better dynamic response curve, short response time, and
small overshoot, high steady precision, good static and dynamic performance
 Relay feedback experiment is used to identify the approximate parameters of PID controller
and then PSO algorithm is used for refining the parameters of PID controller
 Immunity particle swarm optimization algorithm enhances the total searching ability of PSO
algorithm, accelerate the evolving accuracy and improve the convergent accuracy.
 For fuzzy logic reliable expert knowledge was required else developing of decision rules
become too time consuming and tedious.

12. Basic Block Diagram of
• Genetics Algo:- • Neural Fuzzy:-

13.  Fuzzy based PID controller is effective method as it use the information from
data and expert knowledge.
 Immunity particle swarm optimization algorithm performs better then PSO
algorithm as it works on tracking effect, anti jamming and response time.
 Fuzzy PID controller using FPGA gives dynamic response on BLDC motor to
control the speed of motor when the load varies
 GA based fuzzy controller used in BLDC motor controls fuzzy parameters by
using two control closed loops.
 Fuzzy PID controller improves the dynamic and static quality of the controller
by reducing the rise time when applied to level control system.
Strengths

14. • Fuzzy logic provides a certain level of artificial intelligence to the
conventional controllers, leading to the effective fuzzy controllers.
• When ACO applied to the second order transfer function giving the
higher accuracy of peak overshot as compared to Ziegler Nichol
overshoots.
• ACO method is capable of generating the optimum or quasi-
optimum parameters to the control system in a high dimensional
space.
• Neuro-FUZZY controller is free from overshoot and the settling
time is observed 10%shorter than PID controller.

15.  Tuning of fuzzy PID controller is much more difficult than conventional PID controller.
 The disadvantage of the neuro-fuzzy controller is that the controller is only about 60% of
the value of current peak of PID controller, less efficient for speed control of motor.
 PID based on GSA required root locus analysis for the position control of the dc motor.
 PID controller design based on gain and phase margin specifications cannot be solved
analytically as it contains arctan functions
 GA based PID controller has number of generation to be performed to find out the best
fitness value in the process control system so the process was lengthy and complex
Weakness

16. Motivation
 Brushless DC (BLDC) motors are one of the electrical drives that are rapidly gaining
popularity, due to their high efficiency, good dynamic response and low maintenance.
 They are used in industries such as Appliances, HVAC industry, medical, electric traction,
road vehicles, aircrafts, military equipment, hard disk drive, etc.
 Comparing BLDC motors with DC motors, the DC motor have high starting torque capability,
smooth speed control and the ability to control their torque and flux easily and independently.
 But in the DC motor, the power losses occur mainly in the rotor which limits the heat transfer
and consequently the armature winding current density, while in BLDC motor the power
losses are practically all in the stator where heat can be easily transferred through the frame,
or cooling systems can be used specially in large machines.

17.  Brushless DC motor has the following advantages: smaller
volume, high force, and simple system structure.
 This kind of motor not only has the advantages of DC motor
such as better velocity capability and no mechanical
commutator, but also has the advantage of AC motor such as
simple structure, higher reliability and free maintenance.

18. References
• Changhua Lu; Jing Zhang, "Design and simulation of a fuzzy-PID composite parameters' controller with MATLAB," Computer Design
and Applications (ICCDA), 2010 International Conference on , vol.4, no., pp.V4-308,V4-311, 25-27 June 2010
• Changhua Lu; Jing Zhang, "Design and simulation of a fuzzy-PID composite parameters' controller with MATLAB," Computer Design
and Applications (ICCDA), 2010 International Conference on , vol.4, no., pp.V4-308,V4-311, 25-27 June 2010
• Rey, J.P.; Lamego, M.M.; Dalvi, C.; Vescovi, M.R.; Ferreira, E.P., "A numerical-based fuzzy PID controller applied to a DC
drive," Industrial Electronics, 1994. Symposium Proceedings, ISIE '94., 1994 IEEE International Symposium on , vol., no., pp.429,434,
25-27 May 1994
• Wang Xiao-kan; Sun Zhong-liang; Wanglei; Feng Dong-qing, "Design and Research Based on Fuzzy PID-Parameters Self-Tuning
Controller with MATLAB," Advanced Computer Theory and Engineering, 2008. ICACTE '08. International Conference on , vol., no.,
pp.996,999, 20-22 Dec. 2008
• Singh, S.; Mitra, R., "Comparative analysis of robustness of optimally offline tuned PID controller and Fuzzy supervised PID
controller," Engineering and Computational Sciences (RAECS), 2014 Recent Advances in , vol., no., pp.1,6, 6-8 March 2014
• Ananthababu, P.; Reddy, B.A.; Charan, K.R., "Design of Fuzzy PI+D and Fuzzy PID Controllers Using Gaussian Input Fuzzy
Sets," Emerging Trends in Engineering and Technology (ICETET), 2009 2nd International Conference on , vol., no., pp.957,961, 16-18
Dec. 2009
• Dev, D.V.; Kumari, S.U., "Modified method of tuning for fractional PID controllers," Power Signals Control and Computations
(EPSCICON), 2014 International Conference on , vol., no., pp.1,6, 6-11 Jan. 2014
• Boubertakh, H.; Tadjine, M.; Glorennec, P.-Y.; Labiod, S., "Tuning fuzzy PID controllers using ant colony optimization," Control and
Automation, 2009. MED '09. 17th Mediterranean Conference on , vol., no., pp.13,18, 24-26 June 2009
• Qingdong Zeng; Guanzheng Tan, "Optimal Design of PID Controller Using Modified Ant Colony System Algorithm," Natural
Computation, 2007. ICNC 2007. Third International Conference on , vol.5, no., pp.436,440, 24-27 Aug. 2007
• Xiao-yi Wang; Zai-wen Liu; Yang Jiang; Long-hou Sun, "A Fuzzy-PID Controller Based on Particle Swarm Algorithm," Fuzzy Systems
and Knowledge Discovery, 2008. FSKD '08. Fifth International Conference on , vol.1, no., pp.107,110, 18-20 Oct. 2008
• Shih-Feng Chen, "Particle Swarm Optimization for PID Controllers with Robust Testing," Machine Learning and Cybernetics, 2007
International Conference on , vol.2, no., pp.956,961, 19-22 Aug. 2007