Speed Control of Permanent Magnet Brushless DC Motor Using

Download PDF
0 downloads 0 Views 1MB Size Report
Comment
A Permanent Magnet Brushless DC motorworks like a shunt DC motor in which ... same time DC motor speed control also mandatory inorder to achieve the expected output. .... The most commonly used closed loop manual tuning method.
Available online at www.sciencedirect.com Available online at www.sciencedirect.com

ScienceDirect ScienceDirect

Energy Procedia 00 (2017) 000–000 Available online www.sciencedirect.com Available online atatwww.sciencedirect.com Energy Procedia 00 (2017) 000–000

ScienceDirect ScienceDirect

www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia

Energy Procedia 117 Energy Procedia 00(2017) (2017)1101–1108 000–000 www.elsevier.com/locate/procedia

1st International Conference on Power Engineering, Computing and CONtrol, PECCON-2017, 21st International Conference on Power Computing CONtrol, PECCON-2017, 24 March 2017, Engineering, VIT University, Chennai and Campus 4 March 2017, VIT University, Chennai Campus

Speed Control of Permanent Magnet Brushless DC Motor Using Speed Control of Permanent Magnet Brushless DCCooling Motor Using 15th International Symposium on District Heating and HybridThe Fuzzy Proportional plus Integral plus Derivative Controller a Proportional d e Hybrid Fuzzy Integral plus Derivative Controller E Gowthaman *, V Vinodhinib Mirplus Yasser Hussainc S K Dhinakaran T Sabarinathan

a d Assessing using the cheat demand-outdoor E Gowthamanthe *, Vfeasibility Vinodhinib MirofYasser Hussain S K Dhinakaran T Sabarinathane Department of Electronics and Instrumentation for Engineering, Hindusthan Collegedistrict of Engineering heat and Technology, Coimbatore-32, Tamilnadu. temperature function a long-term demand forecast UG Scholar,Department of EIE,Hindusthan of Engineering and Technology, Coimbatore-32, Tamilnadu. Tamilnadu. Department of Electronics and Instrumentation Engineering,College Hindusthan College of Engineering and Technology, Coimbatore-32,

a a

b,c,d,e

b,c,d,e

UG Scholar,Department of EIE,Hindusthan College of Engineering and Technology, Coimbatore-32, Tamilnadu.

I. Andrića,b,c*, A. Pinaa, P. Ferrãoa, J. Fournierb., B. Lacarrièrec, O. Le Correc

Abstract a IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal Abstract b Veolia Recherche & Innovation, Avenue digital Dreyfous Daniel, 78520 Limay, France Thec main core of this work is to investigate two291 different controllers (Conventional PID Controller and Hybrid Département Systèmes Énergétiques et Environnement IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France Theonmain coreloop of this work of is to investigate two different digital controllers (Conventional PID Controller and Hybrid Fuzzy PID) closed control Permanent Magnet Brush Less Direct Current (PMBLDC) Motor. The application of Fuzzy PID)control on closed loop control of Permanent Magnet Less Direct Current (PMBLDC) Thefor application of intelligent (fuzzy logic) technique is attempted in a Brush Proportional –IntegralDerivative (PID)Motor. controller tuning their intelligent (fuzzy logic) technique is attempted Proportional –IntegralDerivative (PID) controller for finest tuningset their parameterscontrol automatically in an online process. In-orderintoaachieve desired speed, Hybrid Fuzzy-PID provides the of parameters automatically in PID an online process. In-orderFuzzy-PID to achievecontroller desired speed, Hybrid Fuzzy-PID finest setand of solutions. The conventional controller and Hybrid performances are analyzedprovides both in the steady state Abstract solutions. The conventional andpoint Hybrid Fuzzy-PID controller performances aretime analyzed both instate steady state dynamic operating conditionPID withcontroller various set speeds. The rise time, dead time, settling and steady error areand the dynamic operating condition with variousThe set point deadFuzzy-PID time, settling time andoffers steadybetter state error are the parameters considered for comparison. resultsspeeds. proveThe that,rise thetime, Hybrid controller performance District heating networks are commonly addressed in the literature as one of the most effective solutions for decreasing the parameters considered for Reduction comparison. The results that,sate theerror) Hybrid controller betterThe performance (Improvement in rise time, in settling time, prove No steady overFuzzy-PID the conventional PIDoffers controller. simulation greenhouse gas emissions from the building sector. These systems require high investments which are returned through the heat (Improvement in rise time, Reductiontool in settling No steadyinsate the conventional PID controller. The simulation makes use of LabVIEW Fuzzy–PID and it istime, implemented realerror) time over through NI-DAQ 6009 with PMBLDC motor which sales. Due to the changed climate conditions and building renovation policies, heat demand in the future could decrease, makes of LabVIEW tool and it is implemented in real time through NI-DAQ 6009 with PMBLDC motor which is rateduse at 36V, 4A, 4000Fuzzy–PID RPM. prolonging the investment return period. is rated at 36V, 4A, 4000 RPM. The main scope of this paper is to assess the feasibility of using the heat demand – outdoor temperature function for heat demand © 2017 The Authors. Published by Elsevier Ltd. forecast. The districtPublished of Alvalade, locatedLtd. in Lisbon (Portugal), was used as a case study. The district is consisted of 665 © 2017 Elsevier © 2017 The The Authors. Authors. Published by by Elsevier Ltd. committee of the 1st International Conference on Power Engineering, Peer-review under responsibility ofthe scientific buildings that vary in both construction periodcommittee and typology. weather scenarios (low,onmedium, high) and three district Peer-review under responsibility of the scientific of theThree 1st International Conference Power Engineering, Peer-review under responsibility ofthe scientific committee of the 1st International Conference on Power Engineering, Computing and and CONtrol. Computing CONtrol. renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were Computing and CONtrol. compared with results from a dynamic heat demand model, previously developed and validated by the authors. Keywords:BLDC Drives, Hybrid Fuzzy-PID, Fuzzy Rule, Tuning of PID Controller, Data Acquisition. The results showed that when only weather change is considered, the margin of error could be acceptable for some applications Keywords:BLDC Drives, Hybrid Fuzzy-PID, Fuzzy Rule, Tuning of PID Controller, Data Acquisition. (the error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). 1.The Introduction value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the 1. Introduction decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and A Permanent Magnet Brushless motorworks like a intercept shunt DCincreased motor in permanent magnet is replacing renovation scenarios considered). On DC the other hand, function forwhich 7.8-12.7% per decade (depending on the A Permanent Magnet Brushless DC motorworks like a shunt DC motor in which permanent magnet is the stationary field winding.In research, laboratory experiments and industry electric traction, DC motor isreplacing playing coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and the stationary field winding.In research, laboratory experiments and industry electric traction, DC motor is playing improve the accuracy of heat demand estimations. © 2017 The Authors. Published by Elsevier Ltd. * Corresponding author. Tel.: +91-9003635645. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and E-mail address:[email protected]. * Corresponding author. Tel.: +91-9003635645. Cooling.

E-mail address:[email protected]. 1876-6102© 2017demand; The Authors. Published bychange Elsevier Ltd. Keywords: Heat Forecast; Climate Peer-review under responsibility ofthe scientific committee 1876-6102© 2017 The Authors. Published by Elsevier Ltd. of the 1st International Conference on Power Engineering, Computing and CONtrol. Peer-review under responsibility ofthe scientific committee of the 1st International Conference on Power Engineering, Computing and CONtrol.

1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the scientific committee of the 1st International Conference on Power Engineering, Computing and CONtrol. 10.1016/j.egypro.2017.05.234

1102 2

E Gowthaman et al. / Energy Procedia 117 (2017) 1101–1108 E. Gowthaman/ Energy Procedia 00 (2017) 000–000

important role nowadays. Industry also need of variable and fixed DC drives depends upon their applications. At the same time DC motor speed control also mandatory inorder to achieve the expected output. The speed of DC motor can be controlled by varying flux/pole, armature resistance and applied voltage. Here, DC motor speed is adjusted by varying the terminal voltage Real - time Speed control of the DC motor is realized by DAQ device in LabVIEW platform [1]. Conventional PI and PID controllers are also designed by modulus hugging (Hybrid) approach by analyzing controlled parameters in order to get the best process response [2]. Modified structure of PID controller (Internal Mode Control) is also developed for DC Drives. The results of the IMC tuning method provides a commendable improvement in the overshoot, rise time and settling time of the system when compared with the Ziegler Nichols closed loop tuning method [16]. Through the advanced technology, the PID Controller is implemented in FPGA with VHDL programming environment [15]. However, the tuning of PID Controller by conventional tuning methods is not reliable for real time process control. Instead, the soft computing based tuning methodology provides better reliability. Traditionally, soft computing techniques developed fuzzy neural-network PI, PD-like controller and Genetic algorithm based hybrid PID controller with online learning for a brushless drive system and industrial motor drives [2] - [13]. From the existing works on Fuzzy-PID controller for speed control of PM BLDC motor as reported in [2][7] are only based on simulation study. New proportional integral- derivative controller control scheme based on a fuzzy system for tuning PID gains has been implemented for robot manipulators [9].

Fig.1. Closed loop Control of Proposed System.

The above block diagram (Fig.1.) represents the proposed closed loop control scheme for PMBLDC motor.In this, the Hybrid Fuzzy-PID with self tuning technique (i.e. IF and THEN fuzzy rule base which provides optimal response through the auto-selection of PID controller parameters).Then, Data Acquisition (DAQ) which is used to acquire digital signal & generate analog output voltage to BLDC motor in order to meet setpoint speed. 2. Mathematical Modeling of BLDC Motor The only difference between the Brushless DC motor and the conventional DC motor is addition of phases involved which affects the overall results of the BLDC model [8]. The resistive and the inductive part of the BLDC arrangement gets affected when there’s phase change. The whole phase concept of BLDC motor with a symmetrical 3-phase and “wye” internal connection is shown in Fig.2. The mathematical modeling of BLDC motor can be derived fromthe mathematical model of typical DC motor. The schematic of DC motor is shown in Fig.3.

Fig.2. Equivalent circuit of Brushless DC motor.

1 Ke G(s) = τm .τe.s2 +τm .s+1

Fig.3. Schematic of Conventional DC motor.

(1) The above equation is the modified transfer function of conventional DC motor [8]. The BLDC motor produces a considerable effect on mechanical time constant when their phase effects are taken into consideration.



E GowthamanEnergy et al. Procedia / Energy Procedia (2017) 1101–1108 E Gowthaman/ 00 (2017)117 000–000

Rj = τ m = Ke Kt

j R Ke Kt

Since for three phase symmetrical arrangement, Mechanical constant becomes, j.3R τm = K e .K t By considering the phase effects, equation (3) becomes τ m = Where K e = K e(L- L)

1103 3

3.Rφ .j

(2) (3) (4)

K e .K t

3

Similarly, the electrical (time constant) can be calculated as, L L L (5) = τ e = = R  R 3.R The mathematical model of the BLDC motor is obtained according to the selected BLDC motor parameters from Table 1. Table 1.Parameters for modeling BLDC motor Parameters

Values

Motor power rating(H.P) Equivalent resistance( R) Equivalent inductance(L) Torque constant (Kt) Voltage constant (Ke) Moment of inertia (J)

1.5 HP 0.345Ω 0.314 mH 4.19 Ncm/A 0.0419 V/rad/s 0.0019 Ncm-S2

Electrical Constant value is calculated from Table 1 as = τe

Similarly, Mechanical Constant value is calculated as τm =

L 3.R

= 0.3019

3.Rφ .j K e .K t

(6)

(7)

= 0.01130

After substituting value of τ e and τ m in equation 1, the transfer function G(s) becomes, G(s) =

23.86 0.00341s 2 +0.3019s+1

(8)

Derived G(s) equation is the closed loop transfer function of the Brushless DC motor. 3. Design and Implementation of Hybrid Fuzzy- PID Controller PID controller finds its application in industrial control system due to feedback control loop mechanism. The integral action of PID controller helps in achieving the target value by providing equality between measured value and desired value with constant error which increases the controller output. The relationship between error input e(t) and the output y(t) of PID controller is given by the following equation,  1 ( de (t ))  y (t ) = K p  e ( t )+  e ( t ) dt +Td  Tí dt  

(9)

The PID controller can be tuned by various methods .The most commonly used closed loop manual tuning method is Ziegler Nichols tuning. Complete elimination of steady state error of any process is not possible by manual tuning. The drawback of manual tuning method was overcome by the development of soft computing tuning

1104 4

E Gowthaman et al. / Energy Procedia 117 (2017) 1101–1108 E. Gowthaman/ Energy Procedia 00 (2017) 000–000

methodology (Fuzzy Tuning) .According to the principle of fuzzy self tuning , error is changed in the entire process to adjust the three parameter (i.e) Kp=Propotional gain, ki=Integral gain and kd= derivative gain of the PID controller online so that good dynamic and static performance can be achieved[11]. When compared to conventional PID controller it helps to achieve the desired response with minimum settling time and rise time.

Fig.4. Basic structure of Hybrid Fuzzy-PID.

The relation between fuzzy input parameters (error and change in error) and fuzzy output parameters (Kp, Ki and Kd) are given by the Hybrid Fuzzy- PID controller which is shown in Fig.4. In Fuzzy tuning process five fuzzy labels (NL, NS, ZE, PS and PL) are framed for fuzzy input parameters such as error and rate of change in error values which are mentioned in Table 2 & Table 3 respectively. Similarly, six fuzzy labels (PVS, PS, PMS, PM, PL, PVL) are framed for fuzzy output parameters such as Kp, Ki&Kd which is represented in Table 4, Table 5 and Table 6 respectively. The triangular membership function (MF) is used for both input variables (“e”, “ec”) and output variables (Kp, Ki&Kd) as shown in the Fig.5, 6, 7, 8, 9 respectively. Table 2. Fuzzy Set Values for Inputs (Error) Fuzzy Label

Points

Negative Large (NL) Negative Small (NS) Zero (Z) Positive Small (PS) Positive Large (PL)

-1500 ; -1500 ; -800 -1500 ; -800 ; -50 -800 ; 0 ; 600 -50 ; 700 ; 1500 600 ; 1500 ; 1500

Fig.5. Fuzzy Control of MF’s for Error “e”. Table 3. Fuzzy Set Values for Inputs (Change in Error) Fuzzy Label Points Negative Large (NL) Negative Small (NS) Zero (Z) Positive Small (PS) Positive Large (PL)

-150 ; -150 ; -90 -150 ; -90 ; -30 -90 ; -30 ; 60 -30 ; 60 ; 150 60 ; 150 ; 150

Fig.6. Fuzzy Control of MF’s for Change in Error “ec”.

In the developed fuzzy system, the PID control parameters has been modified as follows, Kp=Kp’+ΔKp Ki=Ki’+ΔKi Kd=Kd’+ΔKd

(10) (11) (12)



E Gowthaman et al. / Energy Procedia 117 (2017) 1101–1108 E Gowthaman/ Energy Procedia 00 (2017) 000–000

1105 5

Here, Kp’,Ki’and Kd’are representing the previous gain values by Hybrid Fuzzy-PID controller actions Table 4. Fuzzy Set Values for Outputs (Kp) Fuzzy Label Positive Very Small (PVS) Positive Small (PS) Positive Medium Small (PMS) Positive Medium (PM) Positive Medium Large (PML) Positive Large (PL) Positive Very Large(PVL)

Points 0 ; 0 ; 15 0 ; 5 ; 10 5 ; 10 ; 15 5 ; 15 ; 25 10 ; 20 ; 25 15 ; 25 ; 30 25 ; 30 ; 30

Fig.7. Fuzzy Control of MF’s for Proportional gain “KP”. Table 5. Fuzzy Set Values for Outputs (Ki) Fuzzy Label Positive Very Small (PVS) Positive Small (PS) Positive Medium small (PMS) Positive Medium (PM) Positive Medium Large (PML) Positive Large (PL) Positive Very Large(PVL)

Points 0;0;5 0 ; 5 ; 10 5 ; 10 ; 15 10 ; 15 ; 20 15 ; 20 ; 25 20 ; 25 ; 30 25 ; 30 ; 30

Fig.8. Fuzzy Control of MF’s for Integral gain “Ki”. Table 6. Fuzzy Set Values for Outputs (Kd) Fuzzy Label Points Positive Very Small (PVS) Positive Small (PS) Positive Medium small (PMS) Positive Medium (PM) Positive Medium Large (PML) Positive Large (PL) Positive Very Large(PVL)

0;0;5 0 ; 5 ; 10 5 ; 10 ; 15 10 ; 15 ; 20 15 ; 20 ; 25 20 ; 25 ; 30 25 ; 30 ; 30

Fig.9. Fuzzy Control of MF’s for Derivative gain “Kd”.

The Twenty five rules are formulated for the developed fuzzy logic control system. Few of the rules are listed below. Rule1. IF Error “e” is Negative Large (NL) and Change in Error “ec” also Negative Large (NL) THEN Change in proportional gain Kp’ is Positive Very Large (PVL) and Change in Integral gain Ki’ is Positive Medium (PM) and Change in Derivative gain Kd’ is Positive Very Small (PVS).

E Gowthaman et al. / Energy Procedia 117 (2017) 1101–1108 E. Gowthaman/ Energy Procedia 00 (2017) 000–000

1106 6

This rule expresses that, when the output is R1, then the measured speed is more than the set point speed, then DAQ provides change in analog voltage to the driver circuit. R2. IF “e’ is NS and “ec” is NL THEN Kp’ is PVL and Ki’ is PM and Kd’ is PMS. R3. IF “e’ is ZE and “ec” is NL THEN Kp’ is PVL and Ki’ is PM and Kd’ is PM. Similarly, totally twenty five rules are framed. Finally, the fuzzy output variables (ΔKp, ΔKi, ΔKd) are converted into real value output (analog voltage) shown in the Fig. 7, Fig.8 and Fig.9. 4. Experimental Setup 4.1. LabVIEW based simulation of PMBLDC motor control This section describes LabVIEW based BLDC motor Control Simulation along with the implementation of conventional PID and Hybrid Fuzzy-PID controller. LabVIEW simulation block diagram of Conventional PID based BLDC drive is described in Fig.10. It consists of motor transfer function, Conventional PID controller along with their parameters proportional gain (Kp), integral time (Ti) and derivative time (Td).

Fig.10.Conventional PID simulation –Block diagram window.

Fig.11.Hybrid Fuzzy PID simulation –Block diagram window.

The above block diagram window (Fig.11) shows the simulation of Hybrid Fuzzy- PID Speed control of PMBLDC motor. It is designed based on motor model parameters as transfer function through the Fuzzy rule based PID controller. The Fuzzy tuned controller contains membership functions and rule viewer for input and output variables. 4.2. Hardware Setup Conventional and Hybrid Fuzzy-PID performance is implemented in real time through NI-DAQ 6009 with PMBLDC motor which is rated at 36V,4A and 4000RPM.Hybrid Fuzzy-PID based BLDC motor speed control system with all conditions is given by the following flow chart (Fig. 12).

Fig.12. Flowchart of PMBLDC Drive.

Fig.13.Block diagram window of Prototype Implementation.



E Gowthaman et al. / Energy Procedia 117 (2017) 1101–1108 E Gowthaman/ Energy Procedia 00 (2017) 000–000

1107 7

The above LabVIEW block diagram (Fig.13) indicates the graphical programming language with the controllers and the indicators which are interconnected by various tool blocks to perform real time speed control of motor through DAQ Device. The desired speed is achieved by conventional & Hybrid Fuzzy-PID controller actions by sending output voltage in the range of 0 to 5 V. 4.3. Results and Discussions This section is illustrated the closed loop control of BLDC motor response using conventional PID & Hybrid Fuzzy-PID. The performance of Hybrid PID controller is compared with conventional PID performance under the consideration of rise time, settling time, dead time and also steady state error. a) Simulation Response of Conventional PID & Hybrid Fuzzy-PID Controller Performance on BLDC Drive: The response of conventional PID controller is drawn between motor speed and simulation time. The simulation responses are obtained for the set point of 1000 RPM which is shown in Fig.14.

Overshoot is present

Fig.14. Conventional PID –Simulation Response

Quick Settling time with absence of overshoot

Fig.15. Hybrid Fuzzy PID -Simulation Response

Speed response of Conventional PID controller for two different set point speeds of 1000 RPM settles after 12.2 milli seconds only with overshoot and undershoot values. The conventional PID controller attained poor performance due to the improper selection of PID parameters with Ziegler Nicholas manual tuning methodology. The Speed response of Hybrid Fuzzy-PID based controller is shown in Fig.15. Setpoint speed value is 1000 RPM is attained after 6 milli seconds with the help of fuzzy rule based selection of PID controller parameters. It is illustrated that, Hybrid Fuzzy-PID controller is reducing 50% of settling time from the settling time of conventional PID controller and also overshoot& undershoot values are not present. From the Simulation results of both conventional and Hybrid PID, Fuzzy-PID provides better performance over the conventional PID controller for the various setpoint speed values of BLDC motor. b) Prototype Model Response of Conventional PID & Hybrid Fuzzy-PID Controller Performance on BLDC Drive: During the implementation and analysis of prototype model response, conventional PID controller requires more time to achieve set point speed than the Hybrid Fuzzy-PID controller.

Overshoot Undershoot Fig.16. Conventional PID - Prototype Model Response

No Overshoot Fig.17. Hybrid Fuzzy PID - Prototype Model Response

The simulated time Vs motor speed curve for conventional PID controller is obtained using LabVIEW platform as shown in Fig.16. Here the set point speed is 1000 RPM and the desired output speed is attained more than 12.2 milli seconds with steady state error value of ±90 RPM. Hybrid Fuzzy-PID controller performance on closed loop control of BLDC drive (prototype model) is explained in Fig.17. Similarly, the Hybrid Fuzzy-PID controller performance is analyzed with the response which was drawn between Motor speed and Simulation time.

E Gowthaman et al. / Energy Procedia 117 (2017) 1101–1108 E. Gowthaman/ Energy Procedia 00 (2017) 000–000

1108 8

To analyze the Hybrid Fuzzy PID controller performance, the set point speed value is taken as 1000 RPM and the desired output speed is attained after 9 milli seconds with 0 RPM as steady state error due to fuzzy rule based selection of PID controller parameters.

Table 7. Tentative Results of Conventional PID Based BLDC Drive

Table 8. Tentative Results of Hybrid PID Based BLDC Drive

Set point speed in RPM

Settling time ts (ms)

Rise time tr (ms)

Dead time td (ms)

Steady State Error

Set point speed in RPM

Settling time ts (ms)

Rise time tr (ms)

Dead time td (ms)

Steady State Error

1000 2000 3000 4000

12.2 17.2 21 22.5

20 32 30 22

60 60 60 60

± 90 RPM ± 75 RPM ± 80 RPM ± 70 RPM

1000 2000 3000 4000

9 9 9 11.8

19 22 26 21

20 20 20 20

0 RPM 0 RPM 0 RPM 0 RPM

5. Conclusion The Conventional PID and Hybrid Fuzzy-PID Controller techniques are successfully implemented for closed loop control of BLDC drive system. The performances of two different digital controllers are analyzed by the investigation of settling time, rise time,dead time and steady state error with simulation results as well as prototype results. From the LabVIEW Platform based simulation and hardware results, it is concluded that, the Hybrid Fuzzy-PID parameters are tuned automatically to meet the desired response and also results are short listed at various set point speeds. The Hybrid Fuzzy-PID controller offers the better performance control over the conventional PID controller. While comparing with conventional PID controller performance, Hybrid Fuzzy PID controller offers better dynamic response, shorter settling time, rise time and zero steady state error. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16]

E Gowthaman and CD Balaji, “Self Tuned PID Based Speed Control of PMDC Drive:, Proceedings of IEEE International Multi Conference on Automation, Computing, Control, Communication &Compressed Sensing, Kerala, India, March 22-23, 2013,pp.686692. A J Chandra and C. R. Venugopal, “Novel design solutions for remote access, acquire and control of laboratory experiments on DC machines”, IEEE Transactions on Instrumentation and Measurement.vol.61, pp: 349–357, 2012. Kishore N and Singh S, “Torque Ripples Control and Speed regulation of Permanent Magnet brushless DC Motor Drive using Artificial Neural Network”, Proceedings of IEEE Recent Advances in Engineering and Computational Sciences (RAECS), Chandigarh, India, March 6-8, 2014,pp.1-6. Kalavathi M S and Reddy CSR, “Performance evaluation of classical and fuzzy logic control techniques for brushless DC motor drive”,Proceedings of IEEE International Power Modulator and High Voltage Conference (IPMHVC)-2012, San Diego, CA, June 3-7, 2012, pp. 488 - 491. XIAO Jin-feng, LI Bi-wen and ZHANG Lei, “Brushless DC Motor Fuzzy-PID Hybrid Control System based on Field Orientation Control”, International Journal of Digital Content Technology and its Applications.vol.7, pp: 438-447, 2013. Dhanya K Panicker and RemyaMol, “Hybrid PI-Fuzzy Controller for Brushless DC motor speed control”, IOSR Journal of Electrical and Electronics Engineering,vol.8,pp:33-43, 2013. P Selvakumar and T Kannadasan, “Studies on BLDC motor for position control using PID-Fuzzy-Neural Network and Anti-windup Controllers”, Australian Journal of Basic and Applied Sciences. vol.7, pp: 851-856, 2013. Oludayo John Oguntoyinbo, “PID Control of Brushless DC Motor and Robot Trajectory Planning and Simulation with MATLAB/SIMULINK,” Vaasa University of Applied Sciences, 2009. Meza J L, Santibaenz V, Soto R and Llama MA, “Fuzzy Self Tuning PID Semi global Regulator for Robot Manipulators”, IEEE Transactions on Industrial Electronics.vol. 59,pp:2709-271,2012. Ahmed Rubaai and P Young, “Extended Kalman Filter-based PI-/PD-Like Fuzzy-Neural-Network Controller for Brushless Drives”, IEEE Transactions on Industry applications.vol.7, pp: 2391 – 2401, 2011. Rubaai, MJ Castro-Sitiriche, and AR Ofoli, “DSP-based laboratory implementation of hybrid fuzzy-PID controller using genetic optimization for high-performance motor drives”, IEEE Transactions on Industry applications.vol.44,pp:1977–1986, 2008. T Orlowska-Kowalska and K Szabat, “Optimization of fuzzy-logic speed controller for DC drive system with elastic joints”, IEEE Transactions on Industrial Applications.vol.40, pp:1138–1144, 2004. A Rubaai, MJ Castro-Sitiriche, and AR Ofoli, “Design and implementation of parallel fuzzy PID controller for high-performance brushless motor drives: an integrated environment for rapid control prototyping”. IEEE Transactions on Industry applications.vol.44, pp:1090–1098,2008. Sahoo SK, Sultana R and Rout M, “Speed Control of DC Motor using Modulus Hugging Approach”, Proceedings of International Conference on Sustainable Energy and Intelligent Systems, Chennai, India, July 20-22 , 2011, pp.523 - 528. Prashant Kumar and Ravi Mishra, “Implementation of FPGA based PID Controller for DC Motor Speed Control System”, Intl.Journal of Engineering Trends and Technology.vol.4, pp: 471-476, 2013. M Saranya and D Pamela, “A Real Time IMC Tuned PID Controller for DC Motor”, Intl. Journal of Recent Technology and Engineering. vol.1, pp: 65-70, 2012.