SPEED CONTROL OF BRUSHLESS DC MOTOR USING PID AND FUZZY PID CONTROLLER IN MATLAB AND ITS COMPARISION 1

We all are known about the increasing popularity of use of BLDC motor in various applications due to its advantageous features like electronic commutation, longer life, higher speed range, low maintenance, low cost, etc. Hence in order to obtain the desired characteristics according to the application, it is necessary to have the control over its speed. However, there have been many control strategies implemented till date. To control the BLDCM drive which exhibit nonlinear characteristics, a nonlinear controller like Fuzzy Logic controller can be used. This paper represents the speed control strategy using conventional PID controller combined with intelligent fuzzy controller. The shortcomings of PID controller are minimized using Fuzzy PID controller. The simulation for speed control using only PID and using both Fuzzy and PID is done in MATLAB and results are compared.  Direct current motor(BLDCM),Fuzzy Logic Controller(FLC) ,Proportional Integral Derivative (PID) controller. I.INTRODUCTION Now a days road vehicles are more equipped with permanent magnet synchronous motors, particularly the BLDCM due to its sensorless operation. They are used on a large scale in, aerospace, automotive industries and in robotic applications. BLDCM exhibit nonlinear characteristics, PID control are the conventional and convenient methodology for its speed control[1] Although PID control has merits like robustness and simple algorithm  ,it is used where only stability is expected. But BLDCM drive involve a complex process like modeling, control scheme selection and parameters tuning also expert knowledge of system is required for PID tuning in order to get an optimal performance. Also, using PID controller the error has to occur and after that action is taken to minimize it instead of rectifying it instantly. Though sensorless control offer advantage of reduced cost, it gives a poor performance at transients or low speed range and offer complexity of control. Hence, hall effect sensors are used to sense the rotor position which inturn enable the commutation logic for 3 phase inverter. Now, we can use only FLC for speed control it is simple and efficient compared to PID[3][4] but lack in robustness and has a dimensionality curse. Hence ,if we combine both fuzzy and PID then will get combined merits like robustness, speed in error rectification, omit manual interference in control, etc[5].The control parameters Kp,Ki,Kd are kept fixed in case of PID controller for control of speed within a specific range. But, these control parameters cannot be ideal for a vast range of speed. Hence, to obtain best performance in speed control these parameters have to be adjusted along with the change in speed of motor. Now, use of Fuzzy PID controller will update the control parameters Kp, Ki, Kd automatically even in a small occurrence of error without any manual interference in order to obtain optimized performance of BLDCM. II.BLOCK DIAGRAM FOR SPEED CONTROL OF BLDCM BY FUZZY LOGIC+PID CONTROLLER Fig1.Block Diagram For Speed Control Of BLDCM By Fuzzy Logic+PID Controller The above block diagram represent the control strategy for speed control of BLDCM using Fuzzy PID controller. The block diagram consists of an inverter for which will be input for BLDCM to run, DC supply which operates inverter, the rotor position is estimated by hall sensors whose output is given to pulse generator. The actual speed of BLDC motor act as feedback signal and is compared with reference speed and the error is fed as an input to the FuzzyPID controller block further output of this Fuzzy PID is given to pulse generator.  Using PID and Fuzzy PID Controller in MATLAB and its Comparision 130 TABLE II. MOTOR SPECIFICATION BLDC MOTOR MODEL 23F 2 III.TRANSFER FUNCTION EQUATIONS FOR BLDCM The modeling of BLDCM can be done by 2 methods i.e. state space modeling and transfer function modeling. The simplified equivalent circuit of BLDCM is given by Fig.2 Simplified Equivalent circuit of BLDCM Fig.3 Per phase Equivalent Electromechanical system of BLDCM From the above Fig.2 and Fig.3 the transfer function of BLDCM can be derived [5],which is written as, Electrical Torque, Now putting the BLDCM parameters we obtain the transfer function for 23F 2 model given by, Now, by Zigler Nichols method we find the values of Kp,Ki,Kd given by, TABLE II. PID PARAMETERS IV.SIMULATION DIAGRAM OF CONTROLLERS Fig.4 Simulation diagram of discrete PID controller Fig shows the simulation model of discrete PID controller. The control parameters Kp,Ki,Kd gain values are calculated by zigler nichols 2 method of PID designing. The simplest form of transfer function equation of PID controller is given by Where, Kp,Ki,Kd are the proportional, integral and derivative gains.The input to the controller is the difference between the actual speed and the reference speed i.e.error. The output of the PID controller is the summation of Kp, times the magnitude of error, Ki times the integral of error and, Kd times the time derivative of error, which can be written in the equation form as International Journal of Industrial Electronics and Electrical Engineering,  2016 Speed Control of Brushless DC Motor Using PID and Fuzzy PID Controller in MATLAB and its Comparision 131 Fig.5 Simulation diagram of fuzzy PID controller FLC logic work as the human brain and then it effectively implement the modes of reasoning that are not exact but are approximate. The mapping rules in FLC use words instead of numerical values.Seven membership functions are used in order to create 49 rules using if then logic. NB(NegetiveBig),NM(NegetiveMedium),NS(Negetiv e Small) ZE(Zero),PS(PositiveSmall),PM(Positive Medium),PB(Positive Big) and alpha having values between [1 6] are the membership functions. Now here, the rules for alpha are only named by numbers but they are not the actual numerical values, they also act as words as for Kp and Kd As shown in the fig FLC has 2 inputs error and change in error created by a delay and the output of this FLC is further given as input to PID controller i.e. Kp, Kd and alpha which is time constant, further Ki is calculated from these two parameters. All inputs are normalized within the interval[ 1 1] and outputs Kp,Kd within[0 1] interval and for alpha it is [16].Following fig shows 2 inputs and 3 outputs .Combined rule table is prepared consisting of 49 rules. Fig.6 Fuzzy PID controller Fig.7.Input Membership functions for error Fig.8.Input Membership functions for change in error Fig.9 Rules for Kp and Kd Fig.10 Rules for alpha V. SIMULATION OF FUZZY PID CONTROLLER APPLIED TO BLDCM Fig 11.Simulation diagram for speed control of BLDCM