Comparative Study Between PID and Fuzzy PID Controller for Speed Control of BLDC Motor

Because of advantages of BLDC motor, including high accuracy, low volume, and clear control characteristic is still using in a wide range of industrial application. This paper presents comparative study of the Classical Proportion Integral Derivative Controller (PID) and Fuzzy self-tuning PID for speed control of BLDC. With rising complexity and accuracy of control systems, only using FLC or PID cannot satisfy the required operating conditions for such speed control of such type of machines. Fuzzy self-tuning PID controller is presented as a solution of this problem. Fuzzy self tuning PID improves the overall system performance including time response. The modeling, control and simulation of BLDC motor have been done using MATLAB/SIMULINK package. Keywords— Brushless DC Motor (BLDC); Proportional Integration Derivative (PID Controller); Fuzzy Logic Controller (FLC); Fuzzy Self-Tuning PID;


INTRODUCTION
Now a day, BLDC motor becomes very important in markets and widely used in automotive, computer, industrial, aerospace etc. DC motor need brushes for commutation, but The BLDC motor does not needs any brushes for commutation, because the BLDC motors are electronically commutated. BLDC motors have several advantages over brushed dc motor (High reliability and efficiency, lighter and smaller, lower acoustic noise, greater dynamic response, better speed vs. torque characteristics, longer life and higher speed range).
Permanent magnet brushless DC motor speed control is a nonlinear, multivariable, time-varying system and the parameter tuning is too difficult. When we use the conventional controller, it is difficult to meet the requirements. Due to these results, we use the intelligent controllers. Since 1965, the Uuniversity of California cybernetics expert Zadeh proposed fuzzy since its theories and methods of improving, just a few decades, and the fuzzy control is widely used in the natural, social sciences and engineering control field. Fuzzy logic has a natural language skills similar to the human brain, is very suitable for nonlinear systems [1].
Fuzzy logic control does not depend on the accurate mathematical model of the controlled object, it is possible to overcome the influences of nonlinear factors on the adjustment object parameter changed have robust, but the summary of fuzzy rules and fuzzy membership tuning function rely mainly on experience, greater subjectivity.
This paper presents a model that is based on fuzzy self-tuning PID control of brushless dc motor speed control system. The error and deviation of error act as the input parameters of the fuzzy control inference engine, with the fuzzy theory PID parameters online is adaptively adjusted and control system made simulation experiment in MATLAB/SIMULINK. The simulation results show that fuzzy self-tuning PID controller givesa fast response, a small overshoot, a small steady state error, and the dynamic characteristics are better than PID control. The motor drive consists of speed controller, current controller and position sensors as show in  Through electronically in BLDC motor, which results in maintenance free, there are two main types of BLDCmotors: trapezoidal type and sinusoidal type. In the trapezoidal motor, the back-emf induced in the stator windings has a trapezoidal shape and its phases must be supplied with quasi-square currents for ripple free operation. The sinusoidal motor on the other hand has a sinusoidally shaped backemf and requires sinusoidal phase currents for ripple free torque operation. The shape of the backemf is determined by the shape of rotor magnets and the stator winding distribution [2].
Resolution of the position sensors should be high in case ofsinusoidal motors because the rotor position must be knownat every instant of time for the optimal operation. It also requires more complex software and hardware. The trapezoidal motor is a more attractive alternative for most applications due to simplicity, lower price and higher efficiency. BLDC motors exist in many different configurations but the three-phase motor is most common type due to efficiency and low torque ripple. This type of motor also offers a good compromise between precise control and number of power electronic devices needed to control stator currents. Position detection is usually implemented using three Hall -effect sensors which detect the presence of small magnets that are attached to the motor shaft. This paper presents BLDC motor trapezoidal type [3]. BLDC motor is driven by a three-phase inverter, with this six-step commutation is possible. The conducting interval for each phase is 0 120 by electrical angle. Maximum torque is reachedwhen the field lines are perpendicular. The commutation phase sequence is like AB-AC-BC-BA-CA-CB. Each conducting stage is called one step. Therefore, only two phases conduct current at any time, leaving the third phase floating. In order to produce maximum torque, the inverter should be commutated every 0 60 so that current is in phase with the back EMF. The commutation timing is determined by the rotor position, which can be detected by Hall sensors as shown in the Fig.2 (H1, H2, and H3). Current commutation is done by inverter as shown in a simplified form in Fig.3. The switches are shown as bipolar junction transistors but MOSFET switches are more common. Table I shows the switching sequence, the current direction, and the position sensor signals [4].
III. BLDC MOTOR SPEED CONTROLLERS There are several controllers available nowadays like proportional integral derivative (PID), the fuzzy logic controller (FLC), neural networks or/and a combination of conventional and intelligent controllers. In this paper, we discuss PID controller and combination between fuzzy logic and PID controller.

A. PID Controller
Classic PID control has been widely used because of its simple construction and good robustness. In project practice, PID control still takes the dominated place. PID control consists of proportional, integral and derivative as shown in Fig.4.
One of design PID controller challengesis how to choose the controller parameters, can obtain satisfactory results. PID controller is a linear controller. For the control equation:

517
The control law for PID controller is: We are most interested in four major characteristics of the closedloop response. They are: • Rise Time: the time it takes for the plant output Y to Rise from 10% to 90% of the steady state response. • Steady state error: the difference between the steady state output and desired output.    Table II, III and IV. The proposal Fuzzy PID controller is shown in fig.13 [7].

III. SIMULATION AND RESULTS
In this paper, the system design in MATLAB /SIMULINK environment is used to investigate the speed control of BLDC motor using Conventional PID controller and Fuzzy PID controller, the parameters of BLDC motor as shown in table V.To assay the performance of the system, a sequence of measurements has been accomplished. Comparison between PID controller and Fuzzy PID controller of three phases BLDC Motor is shown in table VI [8-9-10]. The performance of the PID controller and Fuzzy PID controller of BLDC Motor on the reference speed of 4000rpm with no load condition is shown in fig. 9-10  From the simulation result the PID controller reach settling time is 0.0454 sec but in fuzzy PID controller reach settling time is 0.006 sec.
The performance of the PID controller and Fuzzy PID controller of BLDC Motor on the reference speed of 4000 rpm with load condition is shown in fig. 11-12 (a) speed and (b) torque. The load is applied at t=1.2s, P T =0.4 N.m. From the simulation result the PID controller reach settling time is 0.0453 sec but in fuzzy PID controller reach settling time is 0.006 sec.
The performance of the PID controller and Fuzzy PID controller of BLDC Motor on a step down speed of 4000 -3500 rpm with load variation at t=1.2s is shown in fig. 13-14  From the simulation result the PID controller reach settling time is 0.6041 sec but in fuzzy PID controller reach settling time is 0.6034 sec V. CONCLUTIONS AND FUTURE WORK In this paper, the design of speed controller system of BLDC motor using the PID and Fuzzy PID controller has been presented. The results of the simulation shown that the Fuzzy PID controller has a better performance, including a fast response speed, a small amount of overshoot, improve control accuracy and enhanced system robustness.