ISSN 2277-2685 IJESR/Oct. 2015/ Vol-5/Issue-10/1332-1337 Shivhar S. Chawale et. al.,/ International Journal of Engineering & Science Research DESIGN AND IMPLEMENTATION OF BRUSHLESS DC MOTOR BY USING FUZZY LOGIC PI CONTROLLER Shivhar S. Chawale* 1, Sankeswari S.S 1 1 P.G Department, M.B.E.S s College of Engineering Ambajogai, Dr. Babasaheb Ambedkar Marathwada ABSTRACT University, Aurangabad, India. This paper presents the design and implementation of an adaptive fuzzy logic controller for the speed control of brushless dc motors. The proposed system uses an adaptation of the slope of the membership functions of the variables used in the conventional fuzzy controller based on the magnitude of the error. A simulation analysis of the fuzzy controller and the adaptive fuzzy controller are done and their performances are compared. The controller uses three fuzzy logic controllers and three PI controllers. The output of the PI controllers is summed and is given as the input to the current controller. The current controller uses P controller. The mathematical modeling of BLDC motor is also presented. The BLDC motor is fed from the inverter where the rotor position and current controller is the input. The fuzzy logic control is learned continuously and gradually becomes the main effective control. The Simulink software was used to simulate the proposed scheme. The results are obtained for variable load torque. Keywords: brushless DC motors, speed control, PI controllers, P controllers, fuzzy logic controller. 1. INTRODUCTION Recently, permanent magnet brushless dc motor (PMBLDC) is very popular because of its attractive features such as high starting torque, high efficiency, low maintenance cost, absence of mechanical commutator, high speed operation, low volume to torque ratio, elimination of sparking and electromagnetic disturbances, noise. A PMBLDC motor is inside out construction of DC motor. The efficiency is likely to be higher than DC motor of equal size and the absence of commutator and brushes, reduces the motor length. Hence the lateral stiffness of the motor is increased, allowing for high speeds [1, 2]. The power electronic converters required in brushless dc motor are similar in topology to the PWM inverter used in induction motor drives. Nowadays brushless dc motors are used in various applications such as defense, industries, robotics, etc. In these applications, motor should be precisely controlled so as to give desired performance. The classical controller need accurate mathematical model of the system and can perform well only under linear condition. Since the PMBLDC motor is highly coupled non-linear multivariable system, it is difficult to obtain its accurate mathematical model. Hence there is a need for intelligent controller. So an attempt is made to develop fuzzy controller for PMBLDC motor. The fuzzy logic controller (FLC) is indeed capable of providing the high accuracy required by high performance drive system without the need of mathematical model [3, 4]. FLC accommodates non-linearity without utilization of mathematical model [5, 6]. The fuzzy logic controller uses fuzzy logic as a design methodology, which can be applied in developing nonlinear system for embedded control. Simplicity and less intensive mathematical design requirements are the most important features of the FLC. Fuzzy logic control is derived from fuzzy set theory introduced by Zadeh in 1965. In fuzzy set theory, the transition between membership and non-membership can be gradual. Therefore, boundaries of fuzzy sets can be vague and ambiguous, making it useful for approximate systems. Fuzzy Logic controller is an attractive choice when precise mathematical formulations are not possible [7, 8]. 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, *Corresponding Author www.ijesr.org 1332
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]. Up to now, over 80% of the controllers are PI (Proportional and integral) controllers because they are facile and easy to understand [4]. The speed controllers are the conventional PI controllers and current controllers are the P controllers to achieve high performance drive. Fuzzy logic can be considered as a mathematical theory combining multi-valued logic, probability theory, and artificial intelligence to simulate the human approach in the solution of various problems by using an approximate reasoning to relate different data sets and to make decisions. It has been reported that fuzzy controllers are more robust to plant parameter changes than classical PI or controllers and have better noise rejection capabilities. In this paper, fuzzy logic controller (FLC) is used for the control of the speed of the BLDC motor. We propose the fuzzy logic PI controller based BLDC motor drive. It is not only easy to understand but also more robust. We use three fuzzy logic PI controllers at the same time. The speed of the BLDC motor is given as the input to the fuzzy logic PI controller. The paper is organized as follows: Section 2 explains about construction and operating principle of BLDC motor, Section 3 elaborates the modeling of BLDC motor, Section 4 presents the speed and current controller, Section 5 introduces the fuzzy logic PI controller. The simulation results are presented in detail in Section 6 and Section 7 concludes the paper. 2. PROPOSED SYSTEM Fig. 1: Proposed System Figure 1 shows the block diagram of proposed system. The system above is composed of brushless dc motor, six step inverter, gate drive for inverter, fuzzy controller and switching logic. Due to the presence of parameter variation and load disturbance in a BLDC motor, closed loop control is necessary, to obtain a desirable behavior. BLDC motor has three phase windings on stator and Permanent Magnet on rotor. In order to define the shaft position, rotor position sensor is necessary. The sensor senses the rotor shaft position and signals. The processed signals are given to the fuzzy controller. The output of the controller is used to provide switching signals for the inverter from which the speed of the motor can be controlled. The structure of FLC consists of the following 3 major components which the first one is fuzzifier that used for measurement of the input or definition of the fuzzy sets that will applied. The second one is fuzzy control or rule base which provides the system with the necessary decision making logic based on the rule base that determine the control policy. The third method is defuzzifier which combines the actions that have been decided and produce single non-fuzzy output that is the control signal of the Systems. 2.1 Fuzzy As A Control Tool Generally PI controller is widely used in BLDC motor control; however it does not give satisfactory results when control parameters and loading condition changes rapidly [3]. The fuzzy logic controller (FLC) will guarantee a stable operation, even if there is a change in motor parameters and load disturbances. The reason is Copyright 2015 Published by IJESR. All rights reserved 1333
obvious; any control system maps the input space to the output space. Generally, a desired set of outputs are calculated for a given set of inputs. This mathematical calculation is represented with a formula, which demonstrates the system behavior. However, this mathematical formula may be too complex to use for the real world issues. In these cases, fuzzy logic provides a useful methodology to create a practical solution for controlling complex systems. It is not necessary to know the exact model of such complex systems in order to design a FLC. It is sufficient to understand the general behavior of the system. Fuzzy logic enables the designer to express the general behavior of the systems in an easier (linguistic) manner where it is allowed to use words and sentences instead of numbers and equations. This is accomplished by forming IF-THEN rules which describe the characteristics of the system. High degree of automation and robust nonlinear control is also possible by means of fuzzy controller. 2.2 Design Of Fuzzy Controller The purpose of the speed control of a brushless dc motor is to arrange the applied voltage in order to reach the reference speed. An error is determined by the difference between the actual speed and the reference speed. The applied voltage should be changed by increasing or decreasing the duty cycle of power transistors in order to minimize the error [4,5].In order to accomplish this task fuzzy controller is designed. Error and change in error are the inputs for the fuzzy controller whereas the output of the controller is change in duty cycle. Two input single output Mamdani type of fuzzy controller with 25 rules is designed for this work. Design of fuzzy controller involves three steps namely fuzzification, inference mechanism and defuzzification. 3. MODELLING OF BLDC MOTOR The flux distribution in BLDC motor is trapezoidal and therefore the d-q rotor reference frames model is not applicable. Given the non-sinusoidal flux distribution, it is prudent to derive a model of the PMBDCM in phase variables. The derivation of this model is based on the assumptions that the induced currents in the rotor due to stator harmonic fields are neglected and iron and stray losses are also neglected. The motor is considered to have three phases even though for any number of phases the derivation procedure is valid. Modeling of the BLDC motor is done using classical modeling equations and hence the motor model is highly flexible. These equations are described based on the dynamic equivalent circuit of BLDC motor. For modeling and simulation purpose assumptions made are the common star connection of stator windings, three phase balanced system and uniform air gap. The mutual inductance between the stator phase windings are negligible when compared to the self inductance and so neglected in designing the model [5]. 4. CONTROLLERS Fig. 2: BLDC motor Simulink model Speed controller The speed of the motor is taken and compared with the reference speed using summer. The resulting error is estimated as, we = wr wr * Copyright 2015 Published by IJESR. All rights reserved 1334
The resulting error is given to the PI controller. The transfer function of the PI controller has the following form [6]. Gs(s) = Kp (1+1/Tis) (10) where Ti = Kp/Ki known as the integral time constants. Kp and Ki are the proportional and integral gains, respectively. Fig. 3: Speed controller sub block 5. FUZZY LOGIC PI CONTROLLER FOR BLDC MOTOR In the past decade, fuzzy logic techniques have gained much interest in the application of control system. They have a real time basis as a human type operator, which makes decision on its own basis. We present the controller which includes three dual inputs but single rule for the fuzzy logic and three PI controllers in different sampling time, as shown below [4]. 6. SIMULATION RESULTS Fig. 4: Fuzzy logic PI controller The simulation results includes variation of different parameters of BLDC motor like total output electrical torque, rotor speed, rotor angle, three phase stator currents, three phase back EMF s with respect to time. The rotor position varies from 0 to 6.28 radians corresponding to 0 to 360. The rotor speed is kept constant with the variable load torque. The waveforms shown here is with respect to variable load torque. The rotor ratings are as below, Table 1: Motor ratings Copyright 2015 Published by IJESR. All rights reserved 1335
Fig. 5: A phase back EMF with variable load torque. Fig. 6: A phase stator current with variable load torque Fig. 7: Rotor speed with variable load torque Fig. 8: Electromagnetic Torque with variable load torque Copyright 2015 Published by IJESR. All rights reserved 1336
7. CONCLUSION Fig. 9: Rotor position In this paper, fuzzy logic PI controller for speed control of BLDC motor is proposed. In this paper, it uses three fuzzy logics to scale speed error for the three PI controllers. The simulation results demonstrate the fuzzy logic control at different load torque. As the load torque varies the speed of the BLDC motor remains constant. The mathematical modeling of BLDC motor is done and the speed control of the BLDC motor by using fuzzy logic speed controller and current controller is proposed. The results have been presented and analyzed for various load conditions. REFERENCES [1] Sakthival G, Anandhi TS, Natarjan SP. Real time implementation of DSP based Fuzzy logic controller for Speed control of BLDC motor. International Journal of Computer Applications 2010; 10(8). [2] Naga Sujatha K, Vaisakh K, Anand G. Artificial Intelligence based speed control of brushless DC motor, 2010. [3] AN885 - Brushless DC (BLDC) Motor Fundamentals. Microchip Technology Inc, 2003. [4] Cheng-Tsung L, Chung-Wen H, Chih-Wen L. Fuzzy PI controller for BLDC motors considering Variable Sampling Effect. IEEE Industrial Electronics Society (IECON) 2007; Nov. 5-8, Taipei, Taiwan. [5] Vandana Govindan TK, Gopinath A, Thomas S. George DSP based Speed control of Permanent Magnet Brushless DC motor. IJCA Special Issue on Computational Science - New Dimensions and Perspectives NCCSE, 2011. [6] Zhen-Yu Z. Masayoshi Tomizuka and Satoru Isaka Fuzzy Gain Scheduling of PID controllers. IEEE transactions on systems, man and cybernetics 1993; 23(5). [7] Pillay P, Krrishnan R. Modelling simulation and analysis of a Permanent magnet brushless Dc motor drive. IEEE trans. Ind Applicant 2002; 26: 124-129. [8] Chung-Wen H, Jen-Ta S, Chih-Wen L, Cheng-Tsung L, Jhih-Han C. Fuzzy Gain Scheduling PI controller for Sensorless four switches three phase BLDC motor, 2010. [9] Hun J, Zhiyong L. Simulation of Sensorless Permanent magnetic brushless DC motor control System. Proceedings of the IEEE International conference on automation and logistics. September, Quigdao, China, 2008. [10] Patil NJ, Chile RH, Waghmare LM. Fuzzy Adaptive Controllers for Speed Control of PMSM Drive. International Journal of Computer Applications, 2010; 1(11). [11] Mann GKI, Bao-Gang H, Gosine RG. Analysis of Direct Action Fuzzy PID Controller Structures. IEEE transactions on systems, man, and cybernetics-part b: cybernetics 1999; 29(3). Copyright 2015 Published by IJESR. All rights reserved 1337