ENHANCE SPEED OF BRUSHLESS SEDC MOTOR BY USING PID CONTROLLER, NEURO- FUZZY LOGIC CONTROLLER

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1 INTERNATIONAL International Journal of Electrical JOURNAL Engineering OF and ELECTRICAL Technology (IJEET), ENGINEERING ISSN (Print), & TECHNOLOGY (IJEET) ISSN (Print) ISSN (Online) Volume 5, Issue 12, December (2014), pp IAEME: Journal Impact Factor (2014): (Calculated by GISI) IJEET I A E M E ENHANCE SPEED OF BRUSHLESS SEDC MOTOR BY USING PID CONTROLLER, NEURO- FUZZY LOGIC CONTROLLER R.C.Chourasia 1, Dr. A.K. Bhardwaj 2 1 Ph.D. Scholar, Electrical Engineering Department from SHIATS Allahabad, India 2 Associate Professor and HOD in Electrical Engineering Department, SSET, SHIATS Allahabad, India ABSTRACT In modern era, the importance of DC motor is continuous growing for automotive industry; electric aircraft and electric appliances. The various researchers have proposed different control method for advancing DC motor. Using conventional methods we cannot derive or control multivariable and non-linear system. In this paper, we proposed BLSEDCM (Brushless Separately Excited DC Motor) using Neuro-fuzzy technique. The proposed methodology gave good robustness, excellent flexibility, adaptability and also gave high precision to the system. The speed estimator Neuro-fuzzy controller shows that the desired results over transient operating conditions and large operating range. Keywords: BLSEDCM (Brushless Separately Excited Direct Current Motor), PID (Proportional Integral and derivative), NFLC (Neuro-Fuzzy Logic Controller). 1. INTRODUCTION In modern and next generation more advanced aspects are required for Many Electric Appliances (MEA) [1-2]. The advanced technology gave more possibilities to manufacture MEA. The MEA has played very important role in electrical power in place of mechanical, pneumatic and hydraulic power to give best response and less cost of the electric motor [3-5]. Mostly MEA is using in electric actuator because the electric actuator is a key-board device, which is directly disturbed the device, such that the weight, speed, reliability and actuation power have need of high performance [6-8]. Nowadays there are three type of electric actuators required to manufacture for air vehicle flight control surfaces. These are integrated actuator packages (IAP), electromechanical actuators 357

2 (EMA), and Electro hydrostatic actuator (EHA). Mainly the electric actuators are used as a power source in rare-earth permanent magnet motor. The brushless separately excited direct current motor is highly non linear. It is very important to linear use fuzzy logic controller and neuro-fuzzy logic controller, it is able to improve the output response like as system stability, robustness and speed also [9-10]. Neuro-fuzzy controller is a combination of fuzzy logic controller and neural network controller. The neuro-fuzzy control are highly intelligent control theory and highly parallel processing. It has ability of self-organization, self-learning and distribute storage of the information [11]. The designing of neuro-fuzzy controller is the addition of memory capacity of fuzzy logic controller and learning ability of artificial neural network controller. In this paper, a neuro-fuzzy network is introduced in the speed control system of medical, road vehicles, aircrafts, military equipment, hard disk drive, electric traction etc. [12-13]. The speed response of the brushless SEDC motor has improved at high voltage. The proposed methodology which is neuro-fuzzy will obviously better than conventional method for motor speed control. In this paper, a neuro-fuzzy network is introduced in the speed control system of medical, road vehicles, aircrafts, military equipment, hard disk drive, electric traction etc.[12-13]; The speed response of the brushless SEDC motor has improved at high voltage. The proposed methodology which is neuro-fuzzy will obviously better than conventional method for motor speed control. II. MATHMATICAL MODEL OF BLSEDC MOTOR The model of BLSEDC Motor parameter can easily drive with mathmetical equation. It has consider the voltage equation across the motor for the balanced system and symmetrical winding are the equation number 1, 2 and 3. Applying Kirchhoff s voltage law for the three phase stator loop winding circuit s yield [2]: = (1) = (2) = (3) Where the back-emf waveforms e a, e b and e c are functions of angular velocity of the rotor shaft. The electromechanical torque is expressed as in equation no. 4. = + + (4) But in the three phases Brushless SEDC motor the electromagnetic torque is depends on the back EMF waveforms, current and speed, so that the equation number 5 is represented as the instantaneous torque of electromagnetic. = ( + + ) (5) The parameters are phase resistance of stator; = = =0.0013Ω respectively, phase reactance of stator; = = = H respectively, rotor moment of inertia; J=0.002kg.m 2, rated torque; T L =5N.m, emf; e a =e b =e c =0.198V/(rad/s) respectively, rated speed n=5000 R.P.M., DC voltage; V dc =250V it can be considered as any practical purposes [12-13]. 358

3 III. MODEL OF BRUSHLESS SEDC MOTOR Model of Brushless SEDC Motor Using Neuro-Fuzzy Controller Fig.1 Neuro-fuzzy speed control system of BLSEDCM The mathmatical model of BLSEDC motor with neuro fuzzy controller is drawn in figure 1. The original speed of BLSEDCM parameter has denoted as ω * and ω is the desired speed. In the neuro-fuzzy network using Gaussian function the two inputs and one output are a 1, a 2 and Y respectively.it is exist in the {0, 1} domain. It has assumed the error e and error chaange rate e r are connected to the input, a 1, a 2 of the nero-fuzzy network through X input to Y output is the role of designing the network output y 0 to the control current i(t) of the BLSEDCM. In the selflearning function, the learning algorithm has been operated online, so the output function J m is the error function i.e. ( ). Neural Network Structure using Fuzzy Gaussian Function The structure of neural network using fuzzy Gaussian function is shown in figure 2. The figure 2 shows a 1, a 2 are input in the networks on the first layer, a 1, a 2 are fuzzed at the second layer, in which membership function is used for the Gaussion function exp( ) (where a,b are the mean value of Gaussion function and standared deviation of the variable x), the thired layer related to fuzzy reasoning replacing fuzzy AND operation or product operation, the fourth layer relates to defuzzification operation. The input and output relationship of the network are shown as follows[13]: 1. The a r is the first layer input variable, it transmits same as output. 2. The equation number 6 is represents Gaussian membership functions; it is the input value of second layer (hidden layer). =exp ( ( ) )..(6) Where i th (i=1, 2 r) is the input variables and x j (j=1, 2 n) is the node of the j th term, a ij is the mean value and standard deviation of the Gaussian function. 3. The fuzzy inference mechanism implements rule layer it is third layer (hidden layer), in this layer each node multiplies the input signals and result of the product is output. The output of this layer is given as follows[12]: 359

4 =.(7) Where represents the i th output of the rule layer. 4. The computation of the outputs in the fourth layer, each node of output y 0 (y 0 =0, 1, 2.. N 0 ) represent output variables which are given in equation 8. =Σ. (8) Where represent the i th output weight of rule layer. Fig.2 Construction of neural network using fuzzy Gaussian function C. Learning Algorithm of Neuro-Fuzzy Network The need of neuro-fuzzy controller has arrangement of weight connection to all layers through calculation, so that the controlled output can reach the desired result. It should be adjusted according to the following equations [13]: ( +1)= ( )...(9) ( +1)= ( ) (10) ( +1)= ( )...(11) The main objective of the learning algorithm is to minimize the error between the desire output (reference model) and the motor output. The back propagation algorithm is used for the learning algorithm of neuro-fuzzy logic controller. 360

5 So, the function is defined as follows: = = ( ). (12) Where is the desired speed, is the user s signal which is the original speed and γ is the learning rate for each parameter in the system, i=1,2..r, j=1,2,3..n and (k=1,2,3,.7). So, the neuro-fuzzy controllers are implemented the arrangement of weight connections and error back propagation. The neuro-fuzzy controller has a high learning speed on the response of the modified back propagation learning algorithm. IV. SIMULATION OF PRAPOSED METHODOLOGY Figure 3 show that simulink model of proposed methodology (BLSEDC) motor using neurofuzzy controller through; By using various set of parameters, which is clearly shown in table no. 1 and 2. The figure number 3 represents the simulation model of the system. Fig.3: Using neuro-fuzzy logic controller simulation model of BLSEDC motor. 361

6 Table No.-1 The network parameter S.No. Parameter Types Values 1 Input Step input, Step input , Generator Transfer function Motor Transfer function 4 Gain 0,1,2,3,4,5,6, ,1,-0.01,4,-0.02,-0.03,- 0.02,1 5 Timer Time(s):[0,1,1.5,2,2.5] Amplitude[0,1,2,2.5,3.5] 6 Slider Gain Zero-Order Hold Random Number Mean 10 The following data are applied for training and tuning the ANN controller. Table No: 2 The input output data of ANN Controller Number of layer 3 Inputs 5 Number of neurons in the hidden layer 3 Output in the ANN controller 1 Table No: 3 In the ANN plant model Number of layer 3 Number of input 2 Neurons in the hidden layer 10 Output 1 The activation function Trainlm Function Training sample 100 No. of epochs 100 The proposed methodology has been trained by using neuro-fuzzy network. Learning algorithms causes the adjustment of the weights so that the controlled system gives the desired response. Fig 4. Speed response with using neuro-fuzzy controller 362

7 The simulation-verified neuro-fuzzy network based speed estimator was implemented. The simulation result shown in Figure 4, which shows that the graph started from t=0.5sec, the speed of motor is drastically increases as time increases and speed of motor has controlling started after 0.5 sec. speed is almost controlled at t=1.4sec, which may be call settling time of the motor. It can be observed that the control mechanism through neuro-fuzzy technique is better than all conventional controllers according to its control time. V. CONCLUSION This paper proposed neuro-fuzzy logic network controller using Gaussian functions, was implemented successfully and reaches the desire control of speed of the brushless SEDC motor. Through the neural network controller used fuzzy logic rule, the arrangement of weight connections and error back propagation to makes neuro-fuzzy logic control network. The of neuro- fuzzy logic controller have a high learning speed, which is optimized the control parameter. The proposed method neuro-fuzzy logic controller shows the simulation results, which has strong robustness, good adaptability and very reliable when the system is disturbed, it is better than PID and fuzzy logic control. The showing simulation response of the neuro-fuzzy logic controller is superior over the PID controller and fuzzy logic controller on the effect of control method. REFERENCE 1. R.C.Chourasia, Dr. A.K. Bhardwaj Brushless Separately Excited Direct Current Motor Electric Motors: A Survey IJIREEICE Vol. 1, Issue 3, June 2013 ISSN ISSN R.C.Chourasia, Dr. A.K. Bhardwaj Design Estimation Of Brushless SEDC Motor For Speed Control By Using Various Controllers IJEEER ISSN (P): X; ISSN (E): X Vol. 4, Issue 1, Feb 2014, Atef Saleh Othman Al-Mashakbeh Proportional Integral and Derivative Control of Brushless DC Motor European Journal of Scientific Research ISSN X Vol.35 No.2 (2009), pp J. R. Hendershort Jr and T. J. E. Miller, Design of Brushless Permanent-Magnet Motors, Oxford, U.K.: Magana Physics/Clarendon, Microchip Technology, Brushless DC (BLDC) motor fundamentals, Application note, AN885, S. Lin, C. Bi, Q. Jiang, and H. N. Phyu, '' Analysis of Three Synchronous Drive Modes for the Starting Performance of Spindle Motors'', IEEE Transactions on Magnetics, Vol. 43, No. 9, Abdullah Al Mamun, GuoXiao Guo, Chao Bi, Hard disk drive mechatronics and Control, CRC Press, Ben M. Chen, Tong H. Lee, Kemao Peng and Venkatakrishnan Venkataramanan, Hard Disk Drive Servo Systems, Springer-Verlag London Limited, Jacek F. Gieras, Rong-Jie Wang and Maarten J. Kamper, Axial Flux Permanent Magnet Brushless Machines, Springer Science and Business Media B.V, Tae-Sung Kim, Byoung-Gun Park, Dong-Myung Lee, Ji-Su Ryu, and Dong-Seok Hyun, '' A New Approach to Sensorless Control Method for Brushless DC Motors'', International Journal of Control, Automation, and Systems, August J. X. Shen, K. J. Tseng, '' Analyses and Compensation of Rotor Position Detection Error in Sensorless PM Brushless DC Motor Drives'', IEEE Tran. On energy conversion, Vol. 18, No. 1, March

8 12. Salam Abdul Hady Abdul Kareem, Fuzzy Neural and Fuzzy Neural Petri Nets Control for Robot Arm, MSc.Thesis, Computers Engineering, Basrah University, September, Khearia A. Mohamad, Fuzzy Neural Controller for Multi-Machine Induction Motor Drives, Ph.D.Thesis, Electrical Engineering, Basrah University, February, VenkataRamesh.Edara, B.Amarendra Reddy, Srikanth Monangi, M.Vimala, Analytical Structures for Fuzzy PID Controllers and Applications International Journal of Electrical Engineering & Technology (IJEET), Volume 1, Issue 1, 2010, pp. 1-17, ISSN Print: , ISSN Online: Manikandan P, Geetha M, Jubi K, Hariprasath P and Jovitha Jerome, Performance Analysis and Control Design of Two Dimension Fuzzy PID Controller International Journal of Electrical Engineering & Technology (IJEET), Volume 4, Issue 5, 2013, pp , ISSN Print : , ISSN Online: Ruba Al-Mulahumadi, Dr. Nizarhadi Abbas and Wameedhhammadi, PID Parameters Optimization Using Adaptive PSO Algorithm For A DCSM POSITION CONTROL International Journal of Electrical Engineering & Technology (IJEET), Volume 4, Issue 4, 2013, pp. 1-13, ISSN Print : , ISSN Online: AUTHORS DETAILS RAMESH CHANDRA CHOURASIA Allahabad received his B.Tech. Degree in Electrical Electronics Engineering from Birala Institute of Technology, Mesra, Ranchi. M.Tech in Electrical & Electronics Engineering (Power System) from SHAITS (formally Allahabad Agriculture, Institute, Allahabad India) in Presently He is pursuing Ph.D. in Electrical Engineering from SHIATS (formally Allahabad Agriculture, Institute, Allahabad -India). Dr. A.K. BHARDWAJ Allahabad, , Received his Bachelor of Engineering degree from JMI New Delhi in 1998; He obtained his M.Tech. degree in Energy and Env. Mgt. from IIT New Delhi in He completed his Ph.D in Electrical Engg. From SHIATS (Formerly Allahabad Agriculture Institute, Allahabad- India) in He has published several research paper in the field of Electrical Engineering. Presently he is working as Associate Professor and HOD in Electrical Engg. Department, SSET, SHIATS Allahabad- India. 364