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\title{Performance Analysis of BLDC Motor Drive using New Simulation Model with Fuzzy and ANFIS Speed Controllers}
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\begin{document}

             \author[1]{C. Subba Rami  Reddy}

             \affil[1]{  1. KSRM College of Engineering, Kadapa, A.P-516003, 2. JNTUH, kukatpally, hyderabad, A.P}

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\date{\small \em Received: 6 December 2013 Accepted: 2 January 2014 Published: 15 January 2014}

\maketitle


\begin{abstract}
        


This paper presents the mathematical model of the brushless DC (BLDC) motor drive fed by the hysteresis current controlled inverter, which is designed using the new switching function concept. The developed simulation model is applied with the fuzzy logic controller (FLC) and adaptive neuro fuzzy inference system (ANFIS) controller as speed controllers to enhance the performance of the BLDC motor drive system. The complementary strengths of FLC and neural networks are combined together to obtain the ANFIS controller. The ANFIS controller is trained by the data of closed loop BLDC motor drive system simulated with PI controller. The ANFIS controller avoids the selection of fuzzy control rules and tuning of membership functions in the manual manner as done in FLC. A comparative study of different performance specifications is proposed between FLC and ANFIS speed controller as applied to the BLDC motor drive system. The simulation results show that the ANFIS controller is more effective as compared to FLC during most of the operating conditions considered.

\end{abstract}


\keywords{BLDC motor, switching function, FLC, ANFIS controller, performance specifications.}

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\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
Ow a days, the brushless dc (BLDC) motors are very widely used in many applications because of high efficiency, high power density, silent operation, long operating life, low maintenance and good dynamic performance \hyperref[b0]{[1]}\hyperref[b1]{[2]}\hyperref[b2]{[3]}\hyperref[b3]{[4]}\hyperref[b4]{[5]}. Lot of research work is progressing towards the BLDC motor drive due to the increased demand of such systems in the market \hyperref[b5]{[6]}\hyperref[b6]{[7]}\hyperref[b25]{[8]}\hyperref[b8]{[9]}. However, the use of BLDC motors is restricted due to the complexity involvement in the speed control for applications with the variable speed and varying loads \hyperref[b9]{[10]}\hyperref[b10]{[11]}\hyperref[b11]{[12]}\hyperref[b12]{[13]}. The classical controllers with fixed gains may not work properly due to the changes in plant dynamics with ageing process, parameter variations and external disturbances \hyperref[b13]{[14]}\hyperref[b14]{[15]}\hyperref[b15]{[16]}.\par
Fuzzy control is one of the most interesting techniques that can be well applied for the control of imprecise, nonlinear and ill-defined systems \hyperref[b16]{[17]}\hyperref[b17]{[18]}\hyperref[b18]{[19]} {\ref [20]}\hyperref[b19]{[21]}. The selection of membership functions and the development of fuzzy control rules is a difficult task without the information from the skilled operator. More over the fuzzy control rules could not be defined properly if the skilled operator is unable to give the exact instructions about the process operation. The artificial neural networks have the learning capability which gains the knowledge of a process automatically from the sample input and output relationship of the process \hyperref[b20]{[22]}\hyperref[b21]{[23]}. This paper introduces the implementation fuzzy logic controller (FLC) and adaptive neuro fuzzy inference system (ANFIS) controller for the speed control of BLDC motor drive system to improve the performance at different operating conditions. This paper is organized in the following manner. Section II illustrates the description of BLDC motor drive that consists of speed controller, current controller, BLDC motor and inverter. Section III presents the simulation results of the drive with FLC as speed controller. Section IV explains the application of ANFIS speed controller to the BLDC motor drive system along with the simulation results. Section V gives the comparative performance of BLDC motor drive for different operating conditions between FLC and ANFIS controller. Finally the main concluding remarks are given in section VI. 
\section[{II.}]{II.} 
\section[{Description of B Motor Drive System}]{Description of B Motor Drive System}\par
The block diagram of BLDC motor drive system with the combination of FLC and the neural network as speed controller is shown in figure 1. The drive system consists of BLDC motor, rotor position sensor, combined FLC and neural network known as ANFIS controller and three phase voltage source inverter.\par
There are two basic control loops in the description of the drive system known as speed control loop and current control loop \hyperref[b22]{[24]}\hyperref[b23]{[25]}. The speed control loop is the outer control loop which consists of BLDC motor, rotor position sensor, speed controller, reference current generator, hysteresis controller and three phase voltage source inverter. The current control loop is the inner control loop which consists of reference current generator, hysteresis current controller and three phase voltage source inverter. The back electro motive force (EMF) wave forms of BLDC motor are in trapezoidal shape. These trapezoidal waveforms for all the three phases are built based on the rotor position (?).      sudden load of 0.662 N-m is applied at 0.05 second and withdrawn at 0.12 second. The motor speed is changed at 0.15 second to a value equal to the set speed but in the opposite direction.   
\section[{LDC}]{LDC} 
\section[{Implementation of Motor Drive with A Controller}]{Implementation of Motor Drive with A Controller}\par
The properties of both fuzzy control and artificial neural networks are combined to produce the ANFIS controller. The FLC is provided with the learning capability by the structure of neural network that helps the FLC to select the best possible ranges for the input and output membership functions that match the sample input and output data. The response of BLDC motor drive with the proportional-integral (PI) controller is used as sample input and output data to train FLC. The basic structure of ANFIS network obtained through the MATLAB is shown in figure \hyperref[fig_7]{11}. The trained membership functions with the best possible ranges for error (e) and change in error (Î?"e) are shown in figure \hyperref[fig_8]{12}. The chosen fuzzy inference system for FLC is Takagi-Sugeno-Kang type of fuzzy inference system. The selected membership functions are the generalized bell shaped one. The parameters of membership functions are tuned by both the back propagation and least squares method through the neural networks structure. The structure of neural network is helping in tuning and selection process of membership functions and fuzzy control rules respectively thereby eliminating the use of human expert. The updating of parameters through the learning process is facilitated by the gradient vector.    
\section[{Global Journal of Researches in Engineering ( ) F Volume XIV Issue}]{Global Journal of Researches in Engineering ( ) F Volume XIV Issue} 
\section[{Comparison of Performance Indices between and Controller}]{Comparison of Performance Indices between and Controller}\par
A comparison of FLC and ANFIS controller is carried out in terms of different performance indices at various set speeds for different operating conditions. The table \hyperref[tab_1]{2} shows the comparison for the performance specifications of settling time, steady state error, peak overshoot and time taken to reach the set speed in the reverse direction between FLC and ANFIS controller. The table \hyperref[tab_1]{2} shows the comparison for the error constants of integral of absolute error (IAE), integral of squared error (ISE), integral of time weighted absolute error (ITAE) and integral of time weighted squared error (ITSE) between FLC and ANFIS controller. The time taken by the BLDC motor to settle at the set speed from standstill is observed for FLC and ANFIS controller at different set speeds of 3500, 3100, 2600, 2100, 1600 and 1100 rpm. It is found that the drive is taking less time with ANFIS controller for all the speeds. ANFIS controller is giving less overshoot at all speeds except at 3100 rpm as compared to FLC. The steady state error is almost equal in both the cases. 
\section[{b) Speed Reversal and Load Perturbation}]{b) Speed Reversal and Load Perturbation}\par
The drive rotating at set speed is suddenly reversed to rotate in the opposite direction. The ANFIS controller is attaining the set speed in the reverse direction quickly at most of the speeds. When the drive is running under steady state condition, a rated load of 0.662 N-m is applied at time of 0.05 second and withdrawn at 0.12 second. The dip in the speed is slightly high with ANFIS controller as compared to FLC. 
\section[{VI.}]{VI.} 
\section[{Conclusion}]{Conclusion}\par
The simulation model of BLDC motor drive is developed to study the transient and steady state performance with FLC and ANFIS controller. The use of expert in selection of fuzzy control rules and tuning the membership functions is eliminated. The ANFIS controller is giving better performance during the different operating conditions at all speeds as compared to FLC except during the load perturbation where the performance is slightly decreased. \begin{figure}[htbp]
\noindent\textbf{1}\includegraphics[]{image-2.png}
\caption{\label{fig_0}Fig. 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2}\includegraphics[]{image-3.png}
\caption{\label{fig_1}Figure 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3}\includegraphics[]{image-4.png}
\caption{\label{fig_2}Figure 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4}\includegraphics[]{image-5.png}
\caption{\label{fig_3}Figure 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-6.png}
\caption{\label{fig_4}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{567}\includegraphics[]{image-7.png}
\caption{\label{fig_5}Figure 5 :Figure 6 :Figure 7 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{89}\includegraphics[]{image-8.png}
\caption{\label{fig_6}Figure 8 :Figure 9 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{11}\includegraphics[]{image-9.png}
\caption{\label{fig_7}Figure 11 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{12}\includegraphics[]{image-10.png}
\caption{\label{fig_8}Figure 12 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{101315}\includegraphics[]{image-11.png}
\caption{\label{fig_9}Figure 10 :Fig. 13 :Fig. 15 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1618}\includegraphics[]{image-12.png}
\caption{\label{fig_10}Fig. 16 :Fig. 18 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.09754098360655737\textwidth}P{0.09754098360655737\textwidth}P{0.09754098360655737\textwidth}P{0.13934426229508196\textwidth}P{0.16721311475409836\textwidth}P{0.05573770491803279\textwidth}P{0.16721311475409836\textwidth}P{0.027868852459016394\textwidth}}
LN\tabcellsep LN\tabcellsep LN\tabcellsep LN\tabcellsep \multicolumn{2}{l}{MN SN}\tabcellsep \multicolumn{2}{l}{SN ZE}\\
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SN\tabcellsep LN\tabcellsep MN\tabcellsep SN\tabcellsep SN\tabcellsep ZE\tabcellsep \multicolumn{2}{l}{SP MP}\\
ZE\tabcellsep LN\tabcellsep MN\tabcellsep SN\tabcellsep ZE\tabcellsep SP\tabcellsep \multicolumn{2}{l}{MP LP}\\
SP\tabcellsep MN\tabcellsep SN\tabcellsep ZE\tabcellsep SP\tabcellsep SP\tabcellsep \multicolumn{2}{l}{MP LP}\\
MP\tabcellsep SN\tabcellsep ZE\tabcellsep SP\tabcellsep \multicolumn{4}{l}{MP MP MP LP}\\
LP\tabcellsep ZE\tabcellsep SP\tabcellsep SP\tabcellsep MP\tabcellsep LP\tabcellsep LP\tabcellsep LP\end{longtable} \par
 
\caption{\label{tab_0}Table 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2} \par 
\begin{longtable}{}
\end{longtable} \par
 
\caption{\label{tab_1}Table 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3} \par 
\begin{longtable}{}
\end{longtable} \par
 
\caption{\label{tab_2}Table 3 :}\end{figure}
 			\footnote{Year 2014 © 2014 Global Journals Inc. (US)} 			\footnote{© 2014 Global Journals Inc. (US)} 		 		\backmatter  			  				\begin{bibitemlist}{1}
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\end{bibitemlist}
 			 		 	 
\end{document}
