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Abstract This paper is about speed control ofinduction motor combining composite control mode in series

with one of the soft computing techniques, fuzzy logic. Thecomposite control mode PID controller is designed based on

Ziegler-Nichols (Z-N) tuning technique. Fuzzy logic controller(FLC) is connected in series with the PID controller for the

effective speed control of widely used induction motors,especially direct field-oriented induction motor (DFOIM). The Z-N PID is adopted because its parameter values can be chosen

using a simple and useful rule of thumb. The FLC is connected tothe PID controller for enhancing robust performance in bothdynamic transient and steady-state periods. The FLC is developed

based on the output of the PID controller, and the output of the

FLC is the torque command of the DFCIM. Simulation resultsdemonstrate that the proposed Z-N PID+FLC scheme can lead todesirable robust speed tracking performance under load torque

disturbances.

Keywords Composite control mode, Fuzzy controller, PIDcontroller, Speed tracking performance.

I. INTRODUCTION

In recent years, field-oriented induction machine

(FOIM) drives have been increasingly utilized in motioncontrol applications due to easy implementation and low

cost. Usage of induction motors reminds us to develop a

better control over it. These induction motors have theadvantage of decoupling (separation) of the torque and

flux control, which makes high servo quality achievable.

Torque and flux parameters are responsible for generatingrotating motion of rotor. These parameters are effected

depending on the load disturbances. The decouplingcontrol feature can be adversely affected by load torque

disturbances and parameter variations in the motor. Thisinstinctly lowers the speed down compared to the desired

speed, so that the variable-speed tracking performance ofan Induction motor is degraded. In order to attain the

rated speed there are many controllers like conventionalPI and PID controllers. But, these have the difficulty in

making the motor closely follow a reference speedtrajectory under torque disturbances. In this regard, an

effective and robust speed controller design is needed.The emerging of artificial intelligence soft computing

D. Praveen Kumar. Author is with Sree Vidyanikethan Engineering

College, Tirupathi, 517102, India. He is Assistant Professor, Department

of Electronics and Control Engineering; (Corresponding author phone:+91-9908753983; e-mail: [email protected]).

S. HemaChandra, is with Sree Vidyanikethan Engineering College,

Tirupathi, 517102, India. He is Associate Professor and Head,

Department of Electronics and Control Engineering (e-mail:[email protected]).

D. Sunitha, student of Bachelor of Technology in Sree Vidyanikethan

Engineering College, Department of Electronics and Control Engineering(e-mail: [email protected]).

techniques for finding any difficult solution became asource for developing new technologies in severalapplications. One of those computing techniques is fuzzy-

logic. These are referred as intelligent controllers whichwe have been proposed for speed control of FOIM drives.

Thos controllers are associated with adaptive gains due to

fuzzy inference and knowledge base. As a result, they can

improve torque disturbance rejections in comparison withbest trial-and-error PI or PID controllers. Nonetheless, no

performance advantages of intelligent controllers incombination with a PI or PID controller are investigated.

Motivated by the successful development and applicationwe propose a hybrid PID+fuzzy controller consisting of a

PID controller and a fuzzy logic controller (FLC) in aserial arrangement for speed control of FOIM drives,

more specifically, direct field-oriented IM (DFOIM)drives. The Ziegler-Nichols (Z-N) method is adopted for

designing a PID controller (denoted as the Z-N PID)because its design rule is simple and systematic. We next

design a FLC carrying out fuzzy tuning of the output ofthe Z-N PID controller to issue adequate torque

commands. Based on a simulation model of the DFOIMdrives incorporating the proposed controller, experiments

are set up in a Matlab/Simulink environment andimplemented in real time using the MRC-6810 analog-to-

digital (AD)/ digital-to-analog (DA) servo control card

together with a DSP electronic controller. The resultsshow that the incorporation of the proposed controller in

to the DFOIM drives can yield superior and robust

variable-speed tracking performance.

II. INDUCTION MOTOR

The principle of vector control of electricaldrives is based on the control of both the magnitude and

the phase of each phase current and voltage. For as longas this type of control considers the three phase system as

three independent systems the control will remain analogand thus present several drawbacks. The most common

accurate vector control is Field Orientated Control, adigital implementation which demonstrates the capability

of performing direct torque control, of handling systemlimitations and of achieving higher power conversion

efficiency. The electrical drive controls become moreaccurate in the sense that not only are the DC current and

voltage controlled but also the three phase currents andvoltages are managed by so-called vector controls. This

vector control scheme Field Oriented Control is discussed

here. It is based on three major points: the machinecurrent and voltage space vectors, the transformation of a

three phase speed and time dependent system into a two

co-ordinate time invariant system and effective Pulse

Width Modulation pattern generation. This controlstructure, by achieving a very accurate steady state andtransient control, leads to high dynamic performance in

terms of response times. The Field Orientated Control

Speed Control of Induction Motor using PID and

Fuzzy ControllerD. Praveen Kumar, S. HemaChandra, D. Sunitha


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(FOC) consists of controlling the stator currentsrepresented by a vector. This control is based on

projections which transform a three phase time and speeddependent system into a two co-ordinate (d and q co-

ordinates) time invariant system. These projections leadto a structure similar to that of a DC machine control.

Field orientated controlled machines need two constants

as input references: the torque component (aligned with

the q co-ordinate) and the flux component (aligned with dco-ordinate). As Field Orientated Control is simply based

on projections the control structure handles instantaneouselectrical quantities. This makes the control accurate in

every working operation (steady state and transient) andindependent of the limited bandwidth mathematical

model. We introduce the DFOIM drive shown in Figure1. The dynamics of an induction motor can be described

by synchronously rotating reference frame direct-quadrature (d-q) equations as

where the notational superscript e stands for the

synchronous reference frame;-

stand for the d-axis and the q-axis stator voltages, stator currents and

rotor currents; Rs , Rr, Ls and Lrdenote the resistancesand self-inductances of the stator and the rotor; Lmdenotes the mutual inductance; Te and TL represent the

electromagnetic and external force load torques,respectively; J m and Bm are the rotor inertia and the

coefficient of viscous damping, respectively; rand rmdenote the rotor and motor mechanical speeds; e standsfor electrical angular velocity;Nis the number of poles of

the motor mechanical speed; p stands for the differentialoperator (d /dt) . The notational superscript s in Figure

1 stands for stationary reference frame. For a DFOIMdrive, the flux has to fall entirely on d-axis.

Therefore, the q- axis rotor flux is set to zero. Theroot-locus method is utilized for the design of PI

controllers. The controllers PI-1, PI-2, and PI-3 are

chosen to ensure that and the

flux command r and the estimated d-axis rotor flux

satisfies , respectively. The parameters

and are given by . To

control the speed of the IM, the speed controller of theDFOIM drive transforms the speed error signal e into an

appropriate electromagnetic torque command Te*.

Figure 1: Block diagram of induction motor


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III.PROPOSED CONTROLLER

The structure of the proposed controller is shown

in Figure 2. This hybrid controller comprises of PIDcontroller in series with the intelligent fuzzy logic

controller. Fuzzy logic is developed based on the outputof the PID controller.

IV.ZIEGLER NICHOLS PID

The steps to acquire the Z-N PID controller for

speed tracking of the DFOIM in Figure 1 are given asfollows. First, we use a fixed step input rm and a linear

proportional speed controller. The proportional gain ofthe speed controller is increased until the DFOIM reaches

its stability limit. As a result, we obtain the period Tu ofthe critical oscillation at the stability limit of the DFOIM

with the critical proportional gain Ku. Next, the values ofthe parametersKp, TI, TD are given byKP=Ku /1.7; TI= Tu

/ 2;TD= TI/4, where KP is the proportional gain; TI is the

integral time and TD is the derivative time.

V. FUZZY LOGIC CONTROLLER

The output of the PID controller is given as theinput to the fuzzy controller. Fuzzy means uncertainty,

fuzzy computes uncertainty by assigning values between

0 and 1 compared to conventional computation 0 or 1.This fuzzy logic involves computing using knowledge

base and rule base. In fuzzy systems, input variables are

assigned with a membership function. Each membershipfunction is assigned with specified values. Fuzzy logic

comprises of three stages.

A. Fuzzification: In the fuzzification process, we

only employ three input membership functions N(x) , Z(x) and P (x) shown in Figure 3 to map a crisp input to a

fuzzy set with a degree of certainty where x = g(t) org(t) withg(t) =K1f(t) and g(t) =K2f(t) . Those three

membership functions are chosen because of theirsimplicity for computation since a large number of

membership functions and rules can cause highcomputational burden for a fuzzy controller. For any

xN where N denotes the interval (, 0), its

corresponding linguistic value is N. Moreover, for anyx P where P denotes the interval (0, ), its

corresponding linguistic value is P. For any xZwhere

Zdenotes the interval [b, b], its corresponding linguistic

value is Z. The membership functions N (x) , Z (x)and

B. Fuzzy inference: The fuzzy inference engine,

based on the input fuzzy sets in combination with the

experts experience, uses adequate IF-THEN rules in theknowledge base to make decisions and produces an

implied output fuzzy set u . For this particular application,the proposed IF-THEN fuzzy rule base is shown in Table

1 and is described as follows:

i. If g(t) N, then u(g(t), g(t)) = b .ii. If g(t) P, then u(g(t), g(t)) = b .

iii. If g(t) Z and g(t) N, then

u (g(t), g(t)) = b ..iv. If g(t) Z and g(t) P, then

u (g(t), g(t)) = b .

v. If g(t) Z and g(t) Z, thenu (g(t), g(t)) = 0 .

Moreover, the Mamdani-type min operation for fuzzy

inference is employed in this study. In this mamdani typefuzzy inference, membership functions like trapezoidal,

triangular, are applied to the input variables.

Figure 2: Block diagram of proposed controller


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Figure 3: Membership functions

C. Defuzzification: In the defuzzification process,

we employ the centre of mass defuzzification method

for transforming the implied output fuzzy set into a crispoutput, and obtain

VI.SIMULATION RESULTS

A computer simulation model of Figure 1 isdeveloped using the Matlab/Simulink software. The

parameter values of the0.14-hp squirrel-cage inductionmotor are given as follows:

Rs () =17,Rr() = 11,Ls (H) = 0.196,Lr(H) = 0.196,

(H) =1.88.103Lm,N= 4,J(Kg cms 2) = 2.4.104 m,B (kg cm) = 9.2.103 m Based on the root-locus method

and the control objectives of the PI controllers in Figure1, we obtain PI-1 as

and PI-3 as . Given a fixed stepinput rm rpm, we obtain the critical gainKu = 2.2 and the

critical oscillation period Tu = 0.049 of the DFOIM. From

equations, we get the Z-N PID as. To design the fuzzy

control part of the proposed controller in Figure 2, wefirst set b = 9 and K2 =1 . Then gains K1 and K3 are

varied until the desired system response under no torquedisturbance is achieved. In this regard, we get K1 = 2 and

K3 = 3 . The Simulink Fuzzy Logic Toolbox [13] is

employed for fuzzy control simulations. Figure 4 showsthat the proposed controller performs better than the Z-N

PID under the condition that the command speed is

increased from 0 to 900 rpm and

Figure 4: Simulation results of the DFOIM using the proposed controller and the Z-N PID under a load disturbance of 1.1 N-m occurring at

the 4.2 second

a load disturbance 1.1 N-m is suddenly applied to theshaft at 4.2 sec.Due to the variations in supply voltage,frequent changes in load, the currents produced and flux

generated are effected such that the torque developed torotate the motor is also varied. When there is severaltorque disturbances occurred in running induction motor

inindustrial sector, it causes huge damage and loss. Thesetorque disturbances shows an impact on speed control of

motor. The simulation results of conventional PIDcontroller are shown in Figure 6 and proposed PID and

fuzzy controllers helps to reduce the torque disturbance

rejections which are shown in Figure 7, to have smoothspeed control.


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Figure 5: The effect of single torque disturbance on speed controlof induction motor using PID controller only.

Figure 6: Simulation result showing effect of several torquedisturbances on speed control of induction motor using only PID

controller.


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Figure 7: The speed control is smooth for several torquedisturbances occurring at different time intervals using proposed

hybrid controller.

VII.CONCLUSION

In this paper, a novel hybrid modified Z-N

PID+FLC-based speed control of a DFOIM has beenpresented. The proposed controller has exhibited the

combined advantages of a PID controller and a FLC.Specifically, it can improve the stability, the transient

response and load disturbance rejection of speed controlof a DFOIM. The complete DFOIM drive incorporating

the proposed controller could be implemented in real timeusing a MRC-6810 AD/DA servo control card for the

Nikki DensoNA21-3F 0.14Hp Induction motor. Thefuzzy logic and only with three membership functions are

used for each input and output for low computationalburden, which can achieve satisfactory results. Simulation

results have illustrated that the proposed controller

scheme has a good and robust tracking performance. Amodified Z-N PID can perform better than a Z-N PID.

Our future effort will focus on how to further improve the

performance of the proposed controller herein byincorporating a modified Z-N PID.

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[8] M. N. Uddin, et al., Performances of fuzzy-logic-basedindirect vector control for induction motor drive,IEEE Trans

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1982.

D. Praveen Kumar, born on 13th July

1986 at Tirupathi, India. CompletedBTech in 2007 & MTech in 2011. One year project work in Research

Laboratory, Department of Space, Govt of India. Total teaching

experience is 3 years.

S. HemaChandra, pursuing his PhD in SV University, Tirupathi.Presently first author working as Assistant Professor and second author

Associate Professor & Head, Department of EConE, Sree Vidyanikethan

Engineering College. Total teaching/industry experience is 10 years.D. Sunitha, completed BTech in 2012 & working in Tata Consultancy

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