International Journal of Electrical & Computer Sciences IJECS-IJENS Vol: 11 No: 02 12 Fuzzy Logic Controller for BLDC Permanent Magnet Motor Drives Tan Chee Siong, Baharuddin Ismail, Siti Fatimah Siraj, Mohd Fayzul Mohammed School of Electrical System Engineering, Universiti Malaysia Perlis, Malaysia terry_tcs_5510@yahoo.com, baha@unimap.edu.my Abstract- This paper presents a fuzzy logic controller for brushless direct current (BLDC) permanent magnet motor drives. Initially a fuzzy logic controller is developed using MATLAB Fuzzy-Logic Toolbox and then inserted into the Simulink model. The dynamic characteristics of the brushless DC motor such as speed, torque, current and voltage of the inverter components are observed and analyzed using the developed MATLAB model. In order to verify the effectiveness of the controller, the simulation results are compared with TMS320F2808 DSP experimental results. The simulation and experimental results show that the brushless direct current motor (BLDC) is successfully and efficiently controlled by the Fuzzy logic controller. Index Term Fuzzy logic controller, BLDC motor drives, Digital Signal Processing I. INTRODUCTION Modern intelligent motion applications demand accurate speed and position control. Many machine and control schemes have been developed to improve the performance of BLDC motor drives. Some simulation models based on state-space equations, Fouries-transforms, d-q axis model and variable sampling have been proposed for the analysis of BLDC motor drives. Limitations of brushed DC motors overcome by BLDC motors include lower efficiency and susceptibility of the commutator assembly to mechanical wear and consequent need for servicing, at the cost of potentially less rugged and more complex and expensive control electronics. BLDC motors offer better speed versus torque characteristics, high dynamic response, high efficiency, long operating life, noiseless operation and higher speed ranges [1]. Due to their favorable electrical and mechanical properties, BLDC motors are widely used in servo applications such as automotive, aerospace, medical, instrumentation, actuation, robotics, machine tools and industrial automation equipment. Many machine design and control schemes have been developed to improve the performance of BLDC motor drives. The model of motor drive has to be known in order to implement an effective control in simulation. Furthermore, fuzzy logic controllers (FLCs) have been used to analyze BLDC motor drives [2]. In this paper, a comprehensive simulation model with a fuzzy logic controller is presented. MATLAB/fuzzy logic toolbox is used to design the FLC, which is integrated into simulations with Simulink [3]. Besides, considering that the computational time without affecting the accuracy of the results obtained is very low, it can be said that the proposed method is promising [4]. Previous studies and development of control schemes have made a very good contribution to BLDC motor drives, but the comprehensive approach has not been available for modeling and analysis of fuzzy logic controlled BLDC motor drives using TMS320F2808 hardware experimental set. Several simulation and experimental results are shown to ensure the validity and performance of the fuzzy logic BLDC motor drive... II. SYSTEMS STRUCTURE A. Permanent-Magnet BLDC Motor Structure Fig. 1 illustrates the transverse section structure of a brushless DC motor. The stator windings of BLDC are similar to those in a polyphase AC motor, and the rotor is composed of one or more permanent magnets. Brushless DC motors (BLDC) contain a powerful permanent magnet rotor and fixed stator windings. The stationary stator windings are usually three phases, which means that three separate voltages are supplied to the three different sets of windings [5]. Brushless DC motors are different from AC synchronous motors in that the former incorporates some means to detect the rotor position (or magnetic poles) to produce signals to control the electronic switches as shown in fig. 2. Fig. 1. Transverse section structure of a brushless dc motor
International Journal of Electrical & Computer Sciences IJECS-IJENS Vol: 11 No: 02 13 Fig. 2. Diagram for BLDC motor systems Fig. 3 shows the electrical diagram of BLDC motor. It consists of a phase resistance (R) and an inductance (L) respectively. Fig. 5. Structure of fuzzy logic controller Fig. 6. (a) triangle, (b) trapezoid, and (c) bell membership function Fig. 3. Electrical diagram of BLDC motor B. Structure of fuzzy logic controller Fig. 4 shows the Fuzzy logic for a BLDC motor drive system. The system contains two loops, the first loop is the current control loop that accomplishes torque control of BLDC motor and the second loop is the speed control loop that adjusts the speed of t h e BLDC motor. Fig. 7 illustrates the membership function of fuzzy logic controller that used the fuzzification of two input values and defuzzification output of the controller. There are seven clusters in the membership functions, w i t h seven linguistic variables defined as: Negative Big (NB), Negative Medium (NM), Negative Small (NS), Zero (Z), Positive Small (PS), Positive Medium (PM), and Positive Big (PB). Fig. 4. Fuzzy logic for BLDC motor drive system Fig. 5 shows the basic structure of a fuzzy logic controller. Fuzzy logic linguistic terms are most often expressed in the form of logical implications, such as If- Then rules. These rules define a range of values known as fuzzy membership functions [4]. Fuzzy membership functions may be in the form of a triangle, a trapezoid, a bell as shows in fig. 6, or of another appropriate form [6]. Fig. 7. Membership functions of fuzzy logic controller A sliding mode rule-base, used in the fuzzy logic controller is given in Table 1. The fuzzy inference operation is implemented by using the 49 rules. The min-max compositional rule of inference and the center of gravity method have been used in the defuzzification process [5]. If p 1 is NB and p 2 is NB Then out is PB, If p 1 is NB and p 2 is NM Then out is PB, If p 1 is NB and p 2 is NS Then out is PM, If p 1 is NB and p 2 is Z Then out is PM,
International Journal of Electrical & Computer Sciences IJECS-IJENS Vol: 11 No: 02 14 Where; v a, v b, and v c are phase voltages, R is resistance, L is inductance, M is mutual inductance, e a, Fig. 8. Matlab simulation diagram of fuzzy logic control TABLE I. RULE BASE OF FUZZY LOGIC CONTROLLER e b, and e c are trapezoidal back EMFs. The motion equation is: (3) The trapezoidal shape functions with limit values between +1 and -1: C. Simulation structure of fuzzy in Matlab Fig. 8 shows the Matlab simulation diagram of the Fuzzy logic controller. The developed Matlab model is use to observe the phase current waveforms, speed, torque and maximun current. III. MATHEMATICAL EQUATIONS The trapezoidal back-emf wave forms are modeled as a function of rotor position so that rotor position can be actively calculated according to the operation speed. The back EMFs are expressed as a function of rotor position (θ). The expression of electromagnetic torque: (4) T e = k e ( f a (θ) i a + f a (θ) i b + f c (θ) i c ) (5) Speed and torque characteristics of BLDC motor: (6) The error and the change in error: (1) Where k e is back-emf constant, f a (Θ ), f b(θ ), and f c (Θ ) are functions of rotor position. e 1[n] = w ref [n] w r [n] (7) e 2[n] = e 1 [n] e 1 [n-1] (8) A. MATLAB Simulations IV. RESULTS (2) In order to validate the control strategies as described, digital simulations were carried out on a
International Journal of Electrical & Computer Sciences IJECS-IJENS Vol: 11 No: 02 15 converter f o r a DC motor drive system using MATLAB/SIMULINK, w h e r e the parameters used for the DC motor drive system is given in table II. TABLE II. THE PARAMETER OF DC MOTOR DRIVE SYSTEM Armature resistance (Ra) 0.5 Ώ Armature inductance (La) 8 mh Back e.m.f constant (K) 0.55 V/rad/s Mechanical inertia (J) 0.0465 kg.m2 Friction coefficient (B) 0.004 N.m/rad/s Rated armature current (Ia) 10 A Fig. 9 shows the phase current waveforms based on the rotor position at 4000 rpm. The phase difference between Ia, Ib and Ic is approximately 120 0. The peak current value is approximately 9 A for all Ia, Ib and Ic. Fig. 10. Speed of BLDC motor, Electromagnetic torque and maximum current (Imax) Fig. 11 shows the speed response for the FLC model developed in Matlab. The speeds reach the desired value of 4000 rpm in 5ms. Fig. 9. Phase current waveforms based on the rotor position at 4000 rpm Fig. 10 shows the dynamic responses of the speed, torque and Imax, respectively. The reference value of maximum current (Imax) is computed from the generated constant torque reference. Fig. 11. Speeds responce for FLC Fig. 12 demonstrates the speed response for FLC on load change that BLDC motor manage to set back to 4000 rpm successfully when the load torque changes occurs either load increased or decrease.
International Journal of Electrical & Computer Sciences IJECS-IJENS Vol: 11 No: 02 16 Fig. 12. Speeds responce for FLC on load change B. Experimental Results A TMS320F2808 DSP and BLDC motor was used to observe the speed response and the phase current waveforms. Fig. 13 shows the experimental results for phase current waveforms of the BLDC motor via an oscilocope when the rotor speed is 4000 rpm. The phase difference between Ia, Ib and Ic is approximately 120 0 and the value of the current magnitude for Ia, Ia and Ic is about 9A. Fig. 14. Speed of BLDC motor, Electromagnetic torque and maximum current (Imax) Fig. 15 shows the BLDC rotor position as indicated by the Hall effect sensor outputs of the BLDC motor as observed on an oscilloscope when the rotor speed is 4000 rpm. The phase difference between the 3 waveforms is about 120 0. Fig. 15. Hall effects of BLDC motor Fig. 13. Phase current waveforms based on the rotor position at 4000 rpm Fig. 14 shows the experimental results of speed of BLDC motor, Electromagnetic torque and maximum current (Imax) by CCStudio when the rotor speed is 4000 rpm. Fig. 16 shows the speed response of the BLDC motor for the experimental set observed by Code Composer Studio (CCStudio). The speed of the motor reached the desired value or steady state at approximately 5ms. A very small value of CCStudio overshoot and the experimental result, is about 50 rpm from the desired value.
International Journal of Electrical & Computer Sciences IJECS-IJENS Vol: 11 No: 02 17 Rapid Prototyping of Fuzzy PID Controls for High Performance Brushless Servo Drives, IEEE Industry Applications Conference, 41st IAS Annual Meeting, page(s):1360 1364, 2006 [6] B. Sing, A.H.N. Reddy, and S.S. Murthy, Gain Scheduling Control of Permanent Magnet Brushless dc Motor, IE(I) Journal-EL 84, 52-62, 2003 [7] Tan Chee Siong, Baharuddin Ismail, Siti Fatimah Siraj, M.Fayzul, Analysis of Fuzzy logic controller for permanent magnet BLDC Motor Drives, 2010 IEEE Student Conference on Research and Development, ScoRED 2010. Fig. 16. Speed responce of BLDC motor V. CONCLUSION As a conclusion, the increasing demand for using fuzzy logic as a controller for BLDC permanent magnet motor in modern intelligent motion control of BLDC motors, both simulation and experimental set-up have provided a good dynamic performance of the fuzzy logic controller system. The speed of the BLDC motor is detected by Hall-sensor IC s accurately instead of the usual, expensive and complicated encoder system. Besides, fuzzy reasoning algorithm designed to control BLDC to get the optimum control under the unstable rotor turning situation or sudden load change, the proposed fuzzy logic controller system has a good adaptability and strong robustness whenever the system is disturbed. The simulation model which is implemented in a modular manner under MATLAB environment allows dynamic characteristics such as phase currents, rotor speed, and mechanical torque to be effectively considered. The result paired with Matlab/simulink is a good simulation tool for modeling and analyzing fuzzy logic controlled brushless DC motor drives. Besides, both simulated results and experimental results shows very good agreement. Some of other adaptive enhancements technique such as artificial neural networks or neuro-fuzzy implementations could be use for future work. REFFRENCES [1] P. Yedamale, Brushless DC (BLDC) Motor Fundamentals. Chandler, AZ: Microchip Technology, Inc., last access; March 15, 2009.[Online].Available:http://ww1.microchip.com/downloads/e n/market_communication/feb%202009%20microsolutions. pdf [2] R. Akkaya, A.A. Kulaksız, and O Aydogdu, DSP implementation of a PV system with GA-MLP-NN based MPPT controller supplying BLDC motor drive, Energy Conv. and Management 48, 210-218, 2007. [3] Tan Chee Siong, Baharuddin; M.Fayzul; M.Faridun N.T, Study of Fuzzy and PI Controller for Permanent-Magnet Brushless DC Motor Drive, IEEE International Power Engineering and Optimization Conference.PEOCO 2010. [4] C.W. Hung; C.T. Lin, and C.W. Liu, An Efficient Simulation Technique for the Variable Sampling Effect of BLDC Motor Applications, IECON 2007, pp. 1175 1179, 2007 [5] A. Rubai, A. Ofoli, and M. Castro, dspace DSP-Based