Cmjv04i02p0118

J. Comp. & Math. Sci. Vol.4 (2), 118-125 (2013)

Induction Motor Modelling Using Fuzzy Logic (Fl) Speed Controller RAFIYA BEGUM1 ZAKEER MOTIBHAI2, GIRIJA NIMBAL3 and S.V. HALSE3 1

Govt. Polytechnic Zalki, Karnataka, INDIA. Govt. Polytechnic Bijapur, Karnataka, INDIA. 3 Karnataka State Women’s University Bijapur, INDIA. 2

(Received on: April 3, 2013) ABSTRACT This paper presents a hybrid system controller, incorporating fuzzy controller with vector-control method for induction motors.

The vector-control method has been optimized by using fuzzy controller instead of a simple P-I controller. The presented hybrid controller combines the benefits of fuzzy logic controller and vector-control in a single system controller. High quality of the regulation process is achieved through utilization of the fuzzy logic controller, while stability of the system during transient processes and a wide range of operation are assured through application of the vector-control. The hybrid controller has been validated by applying it to a simulation model. The proposed fuzzy controller is compared with PI controller with no load and various load condition. The result demonstrates the robustness and effectiveness of the proposed fuzzy controller for high performance of induction motor drive system.

Keywords: Indirect vector control, Fuzzy logic control, PI controller, Hybrid Speed Controller.

INTRODUCTION Recent years have witnessed rapidly growing popularity of fuzzy control systems in engineering applications. The numerous successful applications of fuzzy control have

sparked a flurry of activities in the analysis and design of fuzzy control systems. Fuzzy logic based flexible multi-bus voltage Control of power systems was developed by Ashok et.al. In the last few years, fuzzy logic has met a growing interest in many

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Rafiya Begum, et al., J. Comp. & Math. Sci. Vol.4 (2), 118-125 (2013)

motor control applications due to its nonlinearity’s handling features and independence of the plant modelling. The fuzzy controller (FLC) operates in a knowledge-based way, and its knowledge relies on a set of linguistic if-then rules, like a human operator. Ramon et.al. Presented a rule-based fuzzy logic controller applied to a scalar closed loop induction motor control with slip regulation & they also compared their results with those of a PI controller1.

They used a new linguistic rule table in FLC to adjust the motor control speed.

The design and implementation of industrial control systems often relies on quantitative mathematical models of the plants the controllers, etc. At times, however, we encounter problems for which controller design becomes very difficult and expensive to obtain. In such cases, it is often necessary to observe human experts or experienced operators of the plants or processes and discover rules governing their actions for automatic control. In this context, the fuzzy logic concepts play a very important role in developing the controllers for the plant as this controller does not require that much complicated hardware & uses only some set of rules. Induction motor can be controlled like a separately excited dc motor, brought a great improvement in the high-performance control of ac drives especially with the invention of vector control in the beginning of 1970s. Because of dc machine-like performance, vector control is also known as decoupling, orthogonal, or transvector control. The vector control and the corresponding feedback signal processing, particularly for modern sensorless vector control, are complex and the use of powerful microcomputer or DSP is necessary.

Because of major advantages of vector control, this method of control will oust scalar control, and will be accepted as the industry-standard control for ac drives. PI controllers are widely used in different industries for control of different plants and have a reasonable performance. This performance, however, may not be desirable for some applications such as ac drive control. Therefore it is essential to use a more advance controller in these cases2. PI controller can never achieve perfect control, that is, keep the speed of induction motor continuously at the desired set point value in the presence of disturbance or set point changes. Therefore, we need an advance control technique such as fuzzy logic controller for this goal. Nowadays, fuzzy systems are applied in wide range of academic and industrial fields such as modelling and control, signal possessing, medicine, and etc. An important Fuzzy Logic application is finding a new solution for control problems that will be discuses later. The present paper discusses a Fuzzy Logic Based Intelligent controller. A Fuzzy Logic Controller (FLC) does not need complex mathematical algorithms and is based on the IF_THEN linguistic rules3. In this article we first introduce electrical and mechanical modelling of an induction motor.

Then we will explain the block diagram of the indirect vector control. In the we will discuss the PI, fuzzy logic and hybrid controller, respectively4. Finally we will present the simulation results and a brief discussion.

INDUCTION MOTOR MODELING The electrical part of an induction motor is represented with a fourth-order

Journal of Computer and Mathematical Sciences Vol. 4, Issue 2, 30 April, 2013 Pages (80-134)

Rafiya Begum, et al., J. Comp. & Math. Sci. Vol.4 (2), 118-125 (2013)

state-space model and the mechanical part with a second-order system. All electrical parameters and variables are referred to the

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stator5,6. All rotor and stator quantities are in the arbitrary two axis reference frame is shown in Fig 1.

Fig 1: Stator and rotor in two-axis reference frame (a) q-axis, and (b) d-axis

CONTROL PRINCIPLE

Hybrid Speed Controller

The fuzzy controller in a vectorcontrolled drive system is used as presented in Fig. 1. The controller observes the pattern of the speed loop error signal and correspondingly updates the output DU so that the actual speed wr matches the command speed w *. There are two input

To take over the advantages present in both FL and PI controllers, a hybridization of FL and PI controllers, called fuzzy pre-compensated PI (FPPI) controller, is done and is used as a single controller. In this controller, FL is used for precompensation of reference speed, which means that the reference speed signal (ω*) is changed in advance in accordance with the rotor speed (ω), so that a new reference speed signal (ω1*) is obtained and the main control action is performed by PI controller.

Some particular features such as overshoot and undershoot happening in the speed response, which are obtained with PI controller can be removed and this controller is much useful to loads where the torque/speed of the motor varies every moment. As is customary, the speed error ( e (n) * ) and the change in speed error ( ∆ e

r

signals to fuzzy controller, the error E = wr* - wr and the change in error, CE, which is related to the derivative dE/dt of error. In a discrete system, dE/dt = ∆E/∆r = CE/Ts, where CE = ∆E in the sampling time Ts. with constant Ts, CE is proportional to dE/dt.

The controller output DU in a vector controlled drive is ∆iqs* current. This signal is summed or integrated to generate the actual control signal U or current iqs*7-9.

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Rafiya Begum, et al., J. Comp. & Math. Sci. Vol.4 (2), 118-125 (2013)

(n) ) are the inputs to the FL, the output of the FL controller is added to the reference speed to generate a precompensated

reference speed ( δ ), which is to be used as a reference speed signal by the PI controller shown in Fig 2.

Fig.2 Hybrid Speed Controller

Fuzzy logic (FL) Speed Controller The PI speed controller, which has been discussed in the previous section, is simple in operation and has zero steady-state error when operating on load. But the drawbacks of this PI controller are the occurrence of overshoot while starting, undershoot while load application and overshoot again while load removal.

Furthermore, it requires motor model to

determine its gains and is more sensitive to parameter variations, load disturbances and suffer from poor performance when applied directly to systems with significant nonlinearity. These drawbacks of PI controller can be removed with the help of a FL controller, which need not require model of the drive and can handle non-linearity of arbitrary complexity. The fuzzy logic controller (FLC) has three functional blocks as shown in Fig 3.

Fig.3 Fuzzy Logic Controller (FLC) Functional Block

INDIRECT VECTOR CONTROL The squirrel cage IM using direct and quadrature axes (d-q) theory in the stationary reference frame, which needs less Variables and thus analysis becomes easy.

Fig 4 shows the block diagram of the indirect vector control technique10-15. The drive is controlled with two control loops, i.e. internal pulse width modulation (PWM) current control loop and external speed control loop16,17. The induction motor is fed

Journal of Computer and Mathematical Sciences Vol. 4, Issue 2, 30 April, 2013 Pages (80-134)

Rafiya Begum, et al., J. Comp. & Math. Sci. Vol.4 (2), 118-125 (2013)

by a current-controlled PWM inverter18.

This inverter operates as a three-phase sinusoidal current source19. The error

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between speed ω and the reference speed ω* (ω - ω*) is processed by the speed controller to produce a command torque Te*20,21.

Fig 4: Block diagram of the indirect vector control technique

Fig 5: (a) Input membership functions, (b) Output membership function Journal of Computer and Mathematical Sciences Vol. 4, Issue 2, 30 April, 2013 Pages (80-134)

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Rafiya Begum, et al., J. Comp. & Math. Sci. Vol.4 (2), 118-125 (2013)

Fuzzy membership function In the fuzzification block, the inputs and outputs crisp variables are converted into fuzzy variables ‘e’, ‘de’ and ‘du’ using the triangular membership function shown in Fig 5.

Linguistic variables The fuzzification block produces the

fuzzy variables ‘e’ and ‘de’ using their crisp counterpart. These fuzzy variables are then processed by an inference mechanism based on a set of control rules contained in (7*7) table as shown in Table 3. {NVB (negative very big), NB (negative big), NM (negative medium), NS (negative small), Z (zero), PS (positive small), PM (positive medium), PB (positive big), PVB (positive very big)} (Y.Miloud et al., 2000). Shown in table.

De/e

NB

NM

NS

ZE

PS

PM

PB

NB

NVB

NVB

NVB

NB

NM

NS

ZE

NM

NVB

NVB

NB

NM

NS

ZE

PS

NS

NVB

NB

NM

NS

ZE

PS

PM

ZE

NB

NM

NS

ZE

PS

PM

PB

PS

NM

NS

ZE

PS

PM

PB

PVB

PM

NS

ZE

PS

PM

PB

PVB

PVB

PB

ZE

PS

PM

PB

PVB

PVB

PVB

CONCLUSION In this paper fuzzy logic controller for the control of an indirect vectorcontrolled induction motor was described.

The drive system was simulated with fuzzy logic controller and PI controller and their performance was compared. Here simulation results shows that the designed fuzzy logic controller realises a good dynamic behaviour of the motor with a rapid settling time, no overshoot and has better performance than PI controller. Fuzzy logic control has more robust during change in load condition. The performance of PI and fuzzy controllers in speed control of IM drive are simulated.

Hybridization of FL and PI controllers is done and used as a single controller by

extracting the advantages present in FL and PI controllers.

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Journal of Computer and Mathematical Sciences Vol. 4, Issue 2, 30 April, 2013 Pages (80-134)