1

CHAPTER 1

INTRODUCTION

  1. Background

Permanent magnet Brushless DC (BLDC) motors are becoming very popular rapidly in industries such as automotive, aerospace, consumer, medical, industrial automation equipment and instrumentation because of their high efficiency, high power factor, silent operation, compact form, reliability, and low maintenance.

The Permanent magnet brushless motors are categorized into two types based upon the back-EMF waveform, brushless AC (BLAC)and brushless DC(BLDC)motors [1]. BLDC motor has trapezoidal back EMF and quasi-rectangular current waveform. BLDC motors are rapidly becoming popular in industries such as Appliances, HVAC industry, medical, electric traction, automotive, aircrafts, military equipment, hard disk drive, industrial automation equipment and instrumentation because of their high efficiency, high power factor, silent operation, compact, reliability and low maintenance [2].

To replace the function of commutators and brushes, the BLDC motor requires an inverter and a position sensor that detects rotor position for proper commutation of current. The rotation of the BLDC motor is based on the feedback of rotor position which is obtained from the hall sensors. BLDC motor usually uses three hall sensors for determining the commutation sequence. In BLDC motor the power losses are in the stator where heat can be easily transferred through the frame or cooling systems are used in large machines. BLDC motors have many advantages over DC motors and induction motors. Some of the advantages are better speed versus torque characteristics, high dynamic response, high efficiency, long operating life, noiseless operation; higher speed ranges [3]. However, there are disadvantages for the BLDC because of variable speed, and therefore adjustable speed drives are used to overcome this.

1.2Problem Statement

Conventional DC motors are being replaced by BLDCs due to their high efficiency and low noise features which are more desirable for consumers. Due to a major disadvantage associated with BLDCs regarding the variable speed operation, various studies are being done in order to operate the BLDCs with constant speed. This allows BLDCs to be used in applications that require constant speed with varying load. Since BLDCs are more expensive than the conventional DC motors, various approaches have been taken to reduce the cost of the BLDCs. BLDCs with Hall Effect sensors are being replaced with sensor less control techniques.These sensors less techniques require a complex algorithm which are not always easy to implement.

To achieve desired level of performance, the motor requires suitable speed controllers. Usually speed control is achieved by using proportional integral and derivative (PID) controller. Although conventional PID controllers are widely used in the industry due to their simple control structure and ease of implementation, these controllers pose difficulties where there are some control complexity such as nonlinearity, load disturbances and parametric variations. PID controllers require precise linear mathematical models. As the BLDC machine has nonlinear model, the linear PID may no longer be suitable. Moreover, PIDcontrollercannotbeappliedwiththe systemwhich have a fastchange of parameters,becauseitwouldrequire thechangeof PIDconstantinthe time.

The new technique which uses fuzzy controller is considered as the extension of the conventional technique, because it preserves the linear structure of PID controller. These controllers are designed using the basic principle of fuzzy logic control to obtain a new controller that possesses analytical formulas similar to digital PID controllers. Fuzzy PID controllers have variable control gains in their linear structure. These variable gains are nonlinear function of the errors and changing rates of error signals. These variable gains help in improving the overall performance due to their characteristics features like self-tuned mechanism which can adapt to rapid changes of the errors and rate of change of error caused by time delay effects, nonlinearities and uncertainties of the process [3].

An often-remarked disadvantage of the methods based on the fuzzy logic is the lack of appropriate tools for analysing the controller’s performance, such as stability, optimality, robustness, etc. The most important is to make a good choice of rule based and parameters of membership functions because Fuzzy Logic control is a control algorithm based on a linguistic control strategy, which derived from expert knowledge into an automatic control strategy. The operation of a FLC is based on qualitative knowledge about the system being controlled. An adequate knowledge and experience must be applied to ensure the system can give a good response.

PID controller cannot be applied with the system which have a fast change of parameters, because it would require the change of PID constant in the time. It is necessary to further study the possible combinations of PID and FUZZY controller. It means that the system can be well controlled by PID which is supervised by a fuzzy system [4].

A number of different types of membership functions (MFs) have been proposed for fuzzy control system. There is also provision to custom-design MFs in some fuzzy control software tools. The literature on fuzzy control indicates application of different types of MFs. For example, in modern neuro-fuzzy control, particularly where neural network techniques are used to tune and implement a fuzzy controller, sigmoid type MFs have been found very useful. Sometimes, MF types are hybrid for the input and output fuzzy variables. Although trapezoidal type MF has often been used in fuzzy control literature, triangular MFs are most commonly used almost intuitively for all the variables. Is there any justification for using triangular type MF compared to other types of MFs? Unfortunately, so far in the literature, there has been no systematic analysis, evaluation and comparison of fuzzy control with different types of MFs in order to established the superiority of a particularly type MFs [5].

1.3Project Objective

The goals of this project are as follow:

  1. To develop a controller model of a BLDC motor with Fuzzy-PID controller.
  2. To analyze the speed of BLDC motor using different types of MFs in the Fuzzy logic controller
  3. To improve performance of the BLDC motor speed control using Fuzzy-PID controller

1.4Scope of Project

The scope of this project is :

  1. motor rooting for parament magnetic brushless direct current motor is used in this project.
  2. membership function uses in this project are tringle and trapezoidal model
  3. source of this model three phase (……).voltage
  4. inverter uses for this model consists of six mosft configuration