A Review Study on Fuzzy PID Controller and its Various Applications

Call for Papers Engineering Journal, May 2019

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A Review Study on Fuzzy PID Controller and its Various Applications

Anshumala Tiwari

PG student Department of EIC Engineering

Azad institute of Engineering and Technology Lucknow, India

Mohd Asif Ali

Assistant Professor Department of EIC Engineering

Azad institute of Engineering and Technology Lucknow, India

Dr. Md. Sanawer Alam Professor and Principle Azad Lucknow Polytechnic Lucknow, India

Abstract The main objective of this paper is to describe fuzzy PID (FPID) controller, which is called the fuzzy proportional integral derivative controller or fuzzy logic proportional integral derivative controller. In this paper also briefly discus about the proposed application of fuzzy PID controller in various fields of control. Fuzzy PID controller has two section Fuzzy logic controller and PID controller. Fuzzy logic controller section tuned the parameters of Proportional Integral Derivative controller that gives much better performance in control system. Fuzzy PID controller gives minimum rise time, settling time, overshoot and steady state error in linear and nonlinear control system thats way they can be applied to more complicated problems. The main advantages of fuzzy PID controller are that linguistic variable are used in place of numerical ones, nonlinearity of the system can be handle easily and high degree of precision is accomplished. Fuzzy PID controller are used in aircraft pitch control ,real time speed control of D.C motor, CNC feed servo system, BLDC motor control system etc.

Keywords Fuzzy PID, fuzzy logic controller, PID controller, speed control of D.C motor, BLDC motor, aircraft pitch controller, nonlinear quarter car model.


    PID controllers have been used for several decades in industries for process control applications. The reason for their wide range of popularity is that simple to design and their efficiency for linear system but satisfactory control of performance may be difficult to achieve in higher nonlinear controlled system and some complex system. However expert can qualitative describe a controller, thats fuzzy logic controller which is provides a convenient method for constructing nonlinear controllers. Fuzzy logic controller makes the formulation of a tuning mechanism an extremely complex problem. Fuzzy logic control mechanism is used to reduce the complexity the linear combination of input variables with scaling of a PID controller. Therefore fuzzy logic controller blend with PID controller.[8] This intelligent controller has been an effective tool for control of many nonlinear and complex system. Fuzzy PID controller has the ability to produce better response performance against conventional controller.


      PID controller stands as Proportional integral derivative controller. As the name PID controller indicate three terms first is Proportional, second is integral and third is derivative. These three terms are combined together in such a way with feedback that is gives desired control output. Fig.1 shows block diagram of PID controller used with feedback system. [9]

      P KP*e(t)



      I KI

      I KI

      Set point

      D KD


      Fig-1 block diagram of PID controller

      The generalized equation of the transfer function of PID controller is given by [9]

      C(S) = G(S) / H(S) (1)

      C(S) = KP + KI/S + KD*S (2)

      C(S) = KP [1 + 1/TI*S + TD*S ] (3)


      C(S)=Controller signal G(S)= Control signal H(S)=Error signal KP=Proportional gain KI=Integral gain

      KD =Derivative gain

      TI= Integral time constant TD=Derivative time constant


      Fuzzy logic controller are more robust than Proportional Integral Derivative controller because they can cover a much wider range of operating condition compare than PID controller and can also operate with noise and disturbances of different nature. Fuzzy logic controller is easier to understand and modify their rules, which not only use a human operators strategy but also are expressed in natural linguistic terms. Fig.2 shows block diagram of fuzzy logic controller and their function.

      switching method gives smooth control during switching between Fuzzy logic controller and Proportional Integral Derivative controller. Fig.3 shows block diagram of fuzzy PID controller used with feedback system.

      Fuzzy logic controller

      Fuzzy logic controller








      D E F U Z Z I F I C A T I O N

      D E F U Z Z I F I C A T I O N

      F U Z Z I F I C A T I O N

      F U Z Z I F I C A T I O N

      Fig-3 block diagram of fuzzy PID controller


      Change in error






      The generalized equation of the transfer function of fuzzy PID controller is given by

      G(S) = Y(S)/X(S) (4)

      G(S) = KP(readjusted) + KI(readjusted)/S + KD(readjusted)*S



      Fig.2 block diagram of Fuzzy logic controller


      • Membership function of input fuzzy set.

      • Actual inputs are fuzzified and fuzzy inputs are produced. Because fuzzy logic controller work only on fuzzy input


      • Contain simple fuzzy Rules bases set. Rules bases set are simple if, than rules.

      • Processing fuzzy inputs according to the defined fuzzy rules bases set and fuzzy outputs are obtained.


      • Membership function of output fuzzy set.

      • Bring out a crisp or real value for a fuzzy output. This is easily understandable by human.


    As I discussed previously a Fuzzy PID controller is a combination of the PID controller and the Fuzzy logic controller in a healthy way and thus a new intelligent controller has been accomplished. Fuzzy logic controller section tune the parameter of PID controller i.e. Fuzzy logic controller has supervisory role to readjust the gain of the PID controller during the control operation and also a fuzzy PID

    G(S) = controller output Y(S) = control signal

    X(S) = Output signal of fuzzy logic controller KP(readjusted) =Readjusted proportional gain by fuzzy logic controller

    KI(readjusted) = Readjusted integral gain by fuzzy logic controller

    KD(readjusted) = Readjusted derivative gain by fuzzy logic controller


Many of the field like in aircraft pitch control, speed control of D.C motor, nonlinear quarter car model, BLDC motor etc are using fuzzy logic controller.


    In this paper self tuning controller introduced to control the pitch or longitudinal dynamic of aircraft for improved the fight stability [4]

  2. Real time Speed control of D.C motor:

    Fuzzy PID controller has been used to control the speed of DC motor with FPGA (Field Programmable Gate Array) to reduce the variation in speed when load varies. Fuzzy PID controller gives a constant speed when the load varies and better dynamic response when compared with PID controller and fuzzy controller. Fuzzy PID controller was implemented to run the motor as real time application under speed and load variation condition. [5]

  3. CNC feed servo system:

    Fuzzy PID controller with genetic algorithm has been used to control the feed servo system. The Feed servo system of CNC machine is the comlex electromechanical coupling control system because feed servo system is the main link between the CNC devices and driving part. The control system performance achieved accurately Its difficult due to the characteristics of time varying parameters, load disturbances and motor nonlinear. Fuzzy PID controller with genetic algorithm, optimization method has been used to remove the complex electromechanical coupling effect and it can also minimize the settling time and decrease overshoot.[6]

  4. BLDC motor control system

This paper proposed fuzzy PID controller to control the speed of BLDC motor. Fuzzy PID controller has higher stability, control precision and faster dynamic response speed with PSO control strategy. Its give better dynamic, static performance and robustness of the BLDC motor control system and achieves a acceptable control result.[7]


This paper gives a brief description of fuzzy PID controller and its different application in various field of engineering .fuzzy logic controller and PID controller together gives the much better performance as compare to other controller. Fuzzy PID controller is used in both linear and nonlinear complex system with different tuning techniques like Genetic, PSO, etc


I would like to express my gratitude to my supervisor Professor MOHD ASIF Ali for his valuable support, encouragement and valuable advice. Special thanks to all faculty member of Electronics Instrumentation Control Engineering Department for their support.


  1. ]Prof .H.KAZEMIAN, Intelligent fuzzy PID controller , Research Gate, 4 august 2014

  2. AHMAD MUHYIDDIN BIN YUSOF, A comparative study of conventional PID and Fuzzy PID for D.C Motor speed control, 2013

  3. FRANK DERNONCOURT, Introduction to Fuzzy logic, Research Gate, January 2013

  4. NURBAITI WAHID, NURHAFFIZAH HASSAN, Self tuning fuzzy PID controller design for aircraft pitch control., third international conference on intelligent system modelling and simulation, 2012

  5. BASIL HAMED, MOAYED ALMOBAIED, Fuzzy PID controller using FPGA technique for real time DC Motor speed control, intelligent control & automation, 2011,2,233-2440, august 2011

  6. XIE DONGA, ZHU JIAN-QUB, WANG FENGC, Fuzzy PID controller to feed servo system of CNC machine tool, Procedia engineering 29 (2012) 2853-2858, ELSEVIER, 2012

  7. SEN LIN, GUANGLONG WANG, Fuzzy PID controller algorithm based on PSO & application BLDC motor , earth and environment science 69 (2017)012186, 2017


    ,Richard zobel,ECMS,2006

  9. SALIM, JYOTI OHRI, Fuzzy based PID controller for speed control of D.C motor using lab view, E-ISSN 2224-2856, Volume 10, 2015

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