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Words 1757

Pages 8

Introduction

1.1 Background

AC induction motors are being applied today to a wider range of applications requiring variable speed as they are the leading elements to convert electrical energy into mechanical energy. They are being used as actuators in various industrial processes, robotics, house hold appliances (generally single phase induction motor) and other similar applications. The reason for its day by day increasing popularity can be primarily attributed to its robust construction, simplicity in design and cost effectiveness. Also, induction motors are proved to be more reliable than DC motor.

Generally, variable speed drives for induction motor requires wide operating range of speed and fast torque response, regardless of the load variations. Also, the conventional controllers have to linearize the non-linear system of induction motor in order to calculate the parameters, which is almost impossible to obtain a perfect non-linear model. Hence the values of the parameters that are obtained from it are thereby approximate. This leads us to more advanced control methods to meet the real demand.

To overcome the complexities of conventional controllers, fuzzy logic controller have been implemented in many motor applications. A Fuzzy Logic Controller (FLC) is incorporated for combination with Phase Locked Loop (PLL) for precise and robust speed of induction motor. The fuzzy logic controller is used to pull the motor speed into the locking range of PLL. When the speed error is between the set point speed and the measured speed is larger than the preset value, the motor speed is incremented or decremented by the fuzzy logic controller towards the PLL locking range. In order to achieve excellent speed regulation, PLL control replaces the FLC when speed error is within the locking range of PLL. When the system operates in the phase locked loop, the speed of motor is locked by a reference frequency. Synchronization of motor speed to a very accurate reference frequency warrants that the motor speed will not drift due to temperature or component wear. Thus, a precise speed control of induction motor operation is achieved.

1.2 Objective

The main objective of this project is design a speed control system of the induction motor based on fuzzy logic controller implemented with phase locked loop controller, employing the scalar control model. The voltage and frequency input to the induction motor are to be controlled in order to obtain the desired speed response. This system will be able to control the induction motor speed at desired speed regardless the changes in load.

1.3 Scope

The scope of the project is: a. To learn how to design a control system for a non-linear system. b. To develop an artificially intelligent speed control system for induction motor using phase locked loop and the fuzzy logic approach. c. To produce a learning package of fuzzy logic controller, for scalar speed control of induction motor, for future reference purpose.

Chapter 2

System Overview

2.1 Block Diagram

Fig. 1: Block diagram of proposed induction motor speed control system.

The block diagram of phase locked loop controlled induction motor speed drive system incorporating fuzzy logic controller is illustrated in fig. 1. An ac source is used to provide an ac voltage with adequate power to drive the induction motor at the desired speed. An encoder will be mounted in the motor shaft which serves as the speed feedback unit. The phase locked loop or fuzzy logic controller is employed to control the induction motor speed. When the speed error is larger than the preset value, the fuzzy logic controller is active and when the speed error is within the preset value, the phase locked loop is active. Thus motor speed is accelerated or decelerated towards the set point through fuzzy control where PLL synchronizes with the desired speed.

2.2 Flowchart Fig 2: Flow chart of the proposed induction motor speed control system.

The flowchart of the proposed induction motor speed control system using phase locked loop and fuzzy logic control is illustrated in fig.2. When the motor speed error reaches or within the phase locked loop locking range the system enters to the PLL operation mode. Once the speed error is beyond the preset range, the system will go to the fuzzy logic control mode. Thus a precise speed of induction motor can be achieved. The control mode is selected by the switching logic as shown in fig.1.

Chapter 3

Literature Survey

3.1 Induction Motor

The induction motor finds its place amongst more than 85% of industrial motors as well as in its single phase form in various domestic usages. Also a constant speed motor with shunt characteristics, speed drops only by a few percent from no-load to full load. Hence in past, induction motors have been used primarily in constant speed applications. The speed control of induction motors involves more complicacy than the control of dc motor, especially if comparable accuracy is desired. The main reason for the same can be attributed to the complexity of the mathematical model of induction machine.

Mathematically, the relation between the speed of induction motor and the synchronous speed can be stated as: n = (1 – s) ns (3.1) Also ns = 120 fP (3.2) which implies that there are two basic ways of speed control, namely a. Slip-control for fixed synchronous speed b. Control of synchronous speed

3.2 Phase Locked Loop (PLL)

Phase locked loop is a feedback loop which have been used intensively in systems where accurate frequency is required. The basic PLL has three components connecte.d in a feedback loop, a voltage controlled oscillator (VCO), a phase detector (PD) and a low pass filter (LPF). The VCO is an oscillator whose frequency is proportional to input voltage. The voltage at the input of the VCO determines the frequency of the periodic signal at its output. The VCO output and a periodic input signal (the reference) are inputs to the phase detector. When the loop is locked on the input signal, the frequency of the VCO output is exactly equal to that of a reference. Phase detector produces a signal proportional to the phase difference between the reference signal and the VCO output signal. The phase error signal is filtered by the low pass filter to provide a voltage proportional to the phase difference between the two signals. The loop is closed by connecting the filter output to the input of the VCO. The voltage output of the filter is used to vary the VCO frequency in such a direction that reduces the phase difference. An equilibrium state is reaches when the VCO frequency is exactly equal to the frequency of the reference input signal.

3.3 Fuzzy Logic (FL)

The fuzzy logic is a form of many valued logic or probabilistic logic. It deals with reasoning that is approximate rather than fixed and exact. In contrast with traditional logic they can have varying values, where binary sets have two logic, true or false, fuzzy logic variables may have truth value that ranges in degree between 0 and 1. Fuzzy logic has been extended to handle the concept of partial truth, where the truth value may range completely true and completely false.

Fuzzy logic controller is a technique to embody human like thinking into the control system. Fuzzy logic control can be designed to emulate human deductive thinking i.e. the process people use to infer conclusions from what they know. The fuzzy logic controls have four functions, fuzzification, interface mechanism, knowledge base and defuzzification. a. A fuzzification interface, the fuzzy control initially converts the crisp error and its rate of change in displacement into fuzzy variables, and then they are mapped into linguistic labels of fuzzy sets. Membership functions are defined within the normalized range and associated with each label. All the member functions are symmetrical for positive and negative values of the variables. The proposed controller uses following linguistic labels NB (Negative Big), NM (Negative Medium), NS (Negative Small), ZE (Zero), PS(Positive Small), PM (Positive Medium), PB (Positive Big).

b. A knowledge base involves defining the rules represented as If-Then rules statements governing the relationship between input and output variables in terms of membership function. It contains the definition of fuzzy subsets, their membership functions, their universe discourse and the whole rule of interface to achieve good control.

c. An interface mechanism, also known as interface engine or fuzzy interface module , is a heart of the fuzzy control which posses the capacity of feign the human decisions and emulates the expert’s decision making in interpreting and applying knowledge about how best to control the plant.

d. Defuzzification is a stage which introduces different methods that can be used to produce fuzzy set value for the output fuzzy variables. It is the interface which converts the conclusions of the interface mechanism into actual inputs for the process.

Chapter 4

Discussion & Conclusion

4.1 Discussion

Conventional speed controller for induction motor system requires a complete mathematical model of the motor. Fuzzy logic design uses linguistic description to replace the mathematical model. This can reduce design complexity and expedite the development cycle. However, fuzzy logic alone cannot provide best performance of induction motor speed control both in transient and steady state. Thus the PLL control is used to replace the FLC once the speed error is within a preset limit. When the speed error is larger, the FLC becomes active and the system responds quickly by drawing the motor speed into the preset speed error range.

4.2 Conclusion

The feasibility of using two different controllers i.e. Fuzzy Logic Controller (FLC) in combination with Phase Locked Loop (PLL), for the speed control of induction motor is presented here to obtain a robust and precise speed regulation is presented here. Further study and simulation is needed for the prediction of system behavior. The proposed system offers many advantages such as accurate speed control, synchronization and digital control possibility which can be economically implemented.

Gantt-Chart

Work Accomplished:

Work Remaining:

References 1. V. Chitra, R. S. Prabhakar “Induction Motor Speed Control using Fuzzy Logic Controller” World Academy of Science, Engineering and Technology 23 2006. 2. B. Zaineb, B. H. Mauna, L. Sbita, “Software Digital Phase Locked Loop for Induction Motor Speed Control” International Journal of Electrical And Computer Engineering 5:2 2010. 3. M. Lai, C. Chang, “Fuzzy Logic in the Phase Locked Loop DC Motor Speed Control System”. 4. W. Djatmiko, B. Sutopo, “Speed Control DC Motor under varying load using Phase Locked Loop System”.

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