SOLAR POWERED SPEED CONTROL OF BRUSHLESS ......standalone system, but also suits for hybrid...

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International Journal of Mechanical Engineering and Technology (IJMET) Volume 8, Issue 8, August 2017, pp. 1135–1147, Article ID: IJMET_08_08_113

Available online at http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=8&IType=8

ISSN Print: 0976-6340 and ISSN Online: 0976-6359

© IAEME Publication Scopus Indexed

SOLAR POWERED SPEED CONTROL OF

BRUSHLESS DC MOTOR DRIVE USING PID

FUZZY CONTROLLER

K. Vishnu Murthy

Assistant Professor, Sri Krishna College of Technology, Coimbatore, TamilNadu, India

L. Ashok Kumar, N. Sampathraja

Professor, PSG College of Technology, Coimbatore, Tamil Nadu, India

Y. Dhayaneswaran

R&D Team Manager, Lakshmi Machine Works Limited, Coimbatore, TamilNadu, India

ABSTRACT

In this article, harvesting of renewable energy using artificial intelligence

embedded method analysis has been performed. Since the usage of solar becoming

more popular in recent times and attractive price competitions made viable energy

resources both commercial as well as domestic generation. By adopting artificial

intelligence based methods, the solar energy can be harvested at its maximum

potential. By having soft computing technique based input to the drive train

mechanism, the effective utilization of the solar energy has been performed. In this

paper, the solar energy is used to feed the Brushless DC motor which is operated

using four switch models instead of conventional six switches using PID fuzzy logic

controller to have better speed accuracy. The nerve centre of the PID Fuzzy controller

is to obtain better performance with regard to speed of the controller and to minimize

the computational load torque. In this method, without controlling the signals directly,

function of fuzzy system made to monitor a low level controller and the decision taken

by supervisor can be based on current control performance or operating conditions

depending on the control strategies. The MATLAB/Simulink results have been given

to understand the efficiency of the system and the obtained results from the simulation

shows reduction in using current sensor with better efficiency with minimized

computation load.

Keywords: Brushless DC Motor, PID, Fuzzy Controller, Inverter Drives, Artificial

Intelligence.

Cite this Article: K. Vishnu Murthy, L. Ashok Kumar, N. Sampathraja and

Y. Dhayaneswaran, Solar Powered Speed Control of Brushless DC Motor Drive using

PID Fuzzy Controller, International Journal of Mechanical Engineering and

Technology 8(8), 2017, pp. 1135–1147.

http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=8&IType=8

Solar Powered Speed Control of Brushless DC Motor Drive using PID Fuzzy Controller


1. INTRODUCTION

Now a day’s machinery technologies have attained a high peak usage, efficiency and cost

analysis shoots up and it is of main concerns in the development of low-power motor drives

used in both industry as well as domestic purpose. The high usage of electrical systems

requires to control the losses to rectify the power wastages. Instead of going conventional

power sources, if the adopted technology is towards renewable energy, then it may be

extended to use its full potential and betterment of the society. Since solar radiation provides

most promising and everlasting gist of sources which we have, tapping its better output using

artificial intelligence may have best possible output from it. Based on the solar radiation,

analysing its sunshine, temperature, duration of sunshine, the solar radiation inherent

capabilities to use soft computing methods stands out to be good one of the various types of

methods Radial Basis Function [RBF], Neuro Fuzzy Inference System [ANFIS], and

Multilayer Perceptron MLP, ANFIS stands out to have better results compared to other

methods. Depending on the solar data predicted, ANFIS model may be incorporated to have

better foresee of solar radiation and with its predicted data, it can be used to find out the

efficiency of the PV output. Advent of soft computing techniques paved the way for using

neuro fuzzy model, which is newer techniques for predict the data with ease and convenience

even in an island of geographical location. This typical approach not only confined to

standalone system, but also suits for hybrid system.So, to achieve the maximum power output

at the PV panel or PV array, Maximum power point tracker (MPPT)[1][2] using soft

computing algorithm may be used to gain maximum efficiency at the PV output. MPPT is a

technique used to get maximum power output from PV system by matching the load

resistance and achieves maximum power. According to the field survey, most of the MPPT

algorithm let down in tracking the MPP, which results in reduced efficiency of operation. The

following Figure 1 shows the uniform/varying irradiation curve with respect to

Voltage/Current vs Power Graph and determining the MPP.

Figure 1 P-V and I-V curve of uniform irradiation condition

Figure 2 P-V and I-V curve of varying irradiation condition.

This solar output is then feed on to the Brushless DC motor drive which is controlled by

PID based fuzzy controller.[3][4] In the developed economy countries, almost 1/3rd is mostly

runs at fixed speed operation. If that’s the case, the flow rate is constant, then the throttling or

recirculation losses are often excessive and similar scenario in control of airflow by adjustable

K. Vishnu Murthy, L. Ashok Kumar, N. Sampathraja and Y. Dhayaneswaran


baffles in air-moving plant and in many other constant speed operations there is excessive loss

is been observed. Since the increasing use of Brushless DC motors in both domestic and

industry needs, because of reduced noise, low speed efficiency, long life time, pleasing weight

to power ratio.[5][6] In this drive mechanism, in this voltage control methods, initial current

flow is made such a way that, it should not flow too high on starting, so ultimately initial

surge current is eliminated and smooth pick of acceleration is maintained in the motor.

In conventional control of speed in Brushless DC motor, either normal voltage regulation

or sensor less control employed or PI/PID control is employed. Of all these methods, PI

controlled system causes steady state error and is less responsive to fast changing

environment. [1, 2] Normal PID system when used alone give poor performance to the set

point. So, by incorporating fuzzy login controllers in the same will give better response to the

closed loop control. Since fuzzy controllers don’t need exact model of the system, set of

linguistic may be used to derive the control strategy.[7] These rules may be derived from the

knowledge and properties of system. This interspersed model can be more capable of tackling

plants with parameter uncertainties than conventional controllers. Also the fuzzy controllers d

[38, 39]

2. SOFT COMPUTING TECHNIQUES FOR PV PANEL

Normally, while fixing the PV panel, two types of shade pattern are predominantly used in

GA algorithm. Since partial shading is main issues in solar system because of large number of

panel configured in series and parallel fashion. The panel reconfiguration is the area in which

soft computing techniques may be used for. Su do Ku and total cross tied (TCT) are the two

largely used configuration and with the help of Su do Ku, without the electrical connection

changed, it can reposition the location physically. Its shading pattern is shown below in Fig.3.

Genetic algorithm (GA) employed to array configuration without any shift in physical

connections by interchanging electrical connections to get high output from the panel

string.[5] GA can be applied with proper procreation of population and fitness design function

and shade pattern as shown in Fig. 4.

a) TCT Shade Pattern

b) Su do Ku Shade Pattern

Figure 3 Su Do Ku and TCT Shade pattern of PV Panel.



a) Block diagram of MMPT SOLAR PV

b) GA method Shade Pattern

Figure 4 TCT and SU DO Ku GA Employed Solar PV Configuration

The main task of GA can be defined by following functions

Max(F(i)) = Sum (T) + (Sq/Fq)+ (Sr × Fb) (1)

Where Sum (T) =

Fq =

In – Maximum possible current while bypassing and a – number sod patterns.

3. SOFT COMPUTING TECHNIQUES

With regard to MPPT algorithm, the two regular methods are Perturb and Observe Method

(PO) and Incremental Conductance Method(IC). PO algorithm changes periodically by

varying the voltage or current depending on the external condition and tract the power point.

IC is used to detect the direction of the MPP and small increment is added to find MPP. Both

this conventional MPPT have drawbacks such as steady oscillations, accuracy, convergence

etc. to overcome this problem, Soft computing based fuzzy logic controller is been used to get

better efficiency from MPPT tracker.

3.1. Fuzzy Logic Controller:

In fuzzy logic controllers, the measurement of the input signal is interpreted as a fuzzy

singleton and depending on the type of reasoning linguistic variables can be fuzzified in two

ways, 1. both the input and output linguistic variables will take fuzzy variables as values, 2.

the input linguistic variables are fuzzified as fuzzy variables, while the output linguistic

variables take fuzzy singletons as values. The fuzzification should cover the entire universe of

discourse, and there exists a fuzzy number to represent the fuzzy variable “around zero”. As

for a certain shape of a membership function, narrower membership functions, despite



superiority in faster response and lower steady-state error, may incur larger oscillation, and

thus the system will be unstable especially in noisy environment

It is a set of membership function, is adequate to use empirical methods or knowledge to

determine the mathematical model of the system. It is based on I/O parameter. The formal

structure of controller is given in Figure.5.

Figure 5 Soft Computing Techniques Distribution.

Figure 6 Fuzzy Logic Block Diagram

Figure 7 General Fuzzy Membership Functions.

The voltage and current will be the input to fuzzy controller and duty cycle remains to be

output. Using this presumption, the FLC variable error and its change can be analyzed by

following equation. The membership function for islanding detection is seen in Figure.7. S is

assigned zero, NL around -1, PL to 1 and NM of –0.5 PM of +0.5



Where C(t) and are error and change in error respectively. Once error is computed,

it is then changed to linguistic variables and fuzzy rule table based on the parameters. The one

case study fuzzy table can be created if error is NB and is as follows.

C(t)/∆C(t) NB Z PB

NB NB NB Z

Z NB Z PB

PB Z PB PB

Table 1 Fuzzy Rule Table:

4. CONVENTIONAL SPEED CONTROL OF BRUSHLESS DC MOTOR.

The demanded speed is achieved using the motor controller and it measures the speed of the

motor. Feedback system is good, but more complicated, and may not be required for a simple

robot design. Motors comes in different forms and the speed controller's motor drive output

will be different dependent on these forms [5]-[6].

Commutation assures appropriate rotation of Brushless DC motor, while the speed

depends on the magnitude of the applied voltage. The magnitude is adjusted by PWM

technique and the required speed is achieved by speed controller. The difference between the

actual and required speed is input to the PI controller and, based on this difference, the PI

controller controls the duty cycle of PWM pulses, which corresponds to the voltage amplitude

required to keep the required speed [7].

Figure 8 Conventional Closed Loop Speed control of Brushless DC Motor

The speed controller calculates a Proportional-Integral (PI) algorithm according to the

following equations:

(5)

Conventional six - switch inverter used in Brushless DC motor as illustrated in Fig 2. The

power stage applies in independent mode or complementary mode. In all the modes, three

phase power excites the 2 motor phases simultaneously and the 3rd one is unpowered. Hence,

6 credible voltage vectors are employed to Brushless DC motor [9] [10] using PWM

technique. Fig. 10 shows the configuration of a four-switch inverter for the three-phase

Brushless DC motor as shown in Fig. 10, 2 Capacitors neutrally are used, and other one c

phase is taken beyond the control because it is connected to the midpoint of capacitors. A

conventional PWM scheme for the six-switch inverter is used for the four-switch inverter

topology of the Brushless DC motor drive.



Figure 9 Conventional six - switch inverter used for Brushless DC motor

Figure 10 Four-switch inverter for the three-phase Brushless DC Motor

From Fig.9, the phase current Ic cannot hold at zero, and it causes an additional and

unexpected current, resulting in current distortion in phases a and b, and even in the

breakdown of the system.[20][21][22] The similar complications are rooted by the four-

switch mode, and it causes the voltage vectors to be limited and asymmetric, Table 1 shows

the basic operating principle Brushless DC Motor [11].

5. PROPOSED MODEL FOR SPEED CONTROL OF BRUSHLESS DC

MOTOR.

A. PID Controller

The PID controller is a linear controller. The Proportional value determinates the reaction to

the error, the Integral value determinates the reaction based on the sum of recent errors, and

the Derivative value determinates the reaction based on the rate at which the error has been

changing [5][13].

The packed sum of the above reactions is used to adjust control valve or the power supply

of a heating element. Due to its merits such as simple structure, high efficiency, and easy

implementation, the PID controller is widely used in most servo applications such as

actuation, robotics, machine tools, and so on [16][17].

B. Control System

The control system adopts the double-loop structure. The inner current loop maintains the

rectangular current waveforms, limits the maximum current, and ensures the stability of the

system.[23] The outer speed loop is designed to improve the static and dynamic

characteristics of the system. As the system performance is decided by the outer loop, the



disturbance caused by the inner loop can be limited by the outer loop [18][19]. Thus, the

current loop adopts the conventional PID controller, and the speed loop adopts fuzzy logic

controller. Then, the parameter can be regulated online, and the system is adaptable to

different working conditions. The whole system is shown in Figure 11.

Figure 11a Proposed controller diagram

To implement the fuzzy control strategy, a fuzzy control with 5 rules was selected. The

input is the load current. The output is the amplitude of the auxiliary supply. The membership

function is shown Figure 11 b [1][2]

The membership functions of error are stable, min, max and membership functions of

error variance are no change, speed_re, speed_low, speed_slow, speed_inc.

The rules are,

1) if (Iq is stable) then (u1 is no change)(1)

2) if (Iq is min) then (u1 is speed_re)(1)

3) if (Iq is max) then (u1 is speed_low)(1)

4) if (Iq is stable) then (u1 is speed_slow)(1)

5) if (Iq is stable) then (u1 is speed_inc)(1)

Input

Output

Figure 11b Membership Function for Input and Output



Mode Hall

Values

Working

Phase Current

Conducting

Devices

Mode 1 101 +a,-b ia= I*, ib= -I* VS1, VS4

Mode 2 100 +a,-c ia= I* VS1

Mode 3 110 +b,-c ia= -I* VS3

Mode 4 010 +b,-a ib= I*, ia= -I* VS2, VS3

Mode 5 011 +c,-a ia= -I* VS2

Mode 6 001 +c,-b Ib= -I* VS4

Table 2 Operating Modes of Four Switch Three Phase Brushless DC motor

According to Hall signals, controller 1 works when the motor runs at modes 2, 3, 5, and 6.

The fuzzy logic controller is taken as a speed controller. The speed difference can be

represented as

e(t) = v × − v(t) (6)

Where v∗ is the given speed value and v(t) is the measured speed value at time t. The

output of the fuzzy logic controller I∗ (t) is the threshold value of the current regulator. For

the safety of the system, I∗ (t) cannot pass beyond the maximum setting value. Then, the input

of the current regulator is

ei (t) = I × (t) − ic(t) (7)

A PID controller is used here as a current regulator.

6. SIMULATION OF PROPOSED MODEL

The simulation diagram for speed control of Brushless DC Motor using Fuzzy and PID

control is shown in the Figure 12.

Figure 12a Simulation model of Brushless DC Motor using fuzzy and PID control.

The Simulation Diagram for Pulse Width Modulation inverter is shown in the Fig .13.a

Figure.13a Simulation model using MATLAB for pwm Inverter



The simulation diagram for dq to abc controller circuit is shown in the Fig 13.b

Figure 3b dq to abc Converter

7. SIMULATION RESULT AND ANALYSIS

An experimental setup was made to verify the simulated results under identical conditions.

The input voltage is shown in Fig.14 such that the fundamental voltage Vs = 98V. Similarly,

Fig.15 shows the waveform of Switching Pulses for IGBT.

Figure 14 Input voltage of 98V

Figure 15 Switching pulses for IGBT

The stator voltage of the Brushless DC motor is shown in the Fig. 16.a and the amplitude

is 100V, the stator current of Brushless DC Motor is shown in Fig.16.b

Figure 16a Stator Voltage of Brushless DC Motor



Figure 16b Stator Current of Brushless DC Motor

The Simulation result of torque and speed are shown in the Figure.17.and Figure.18. and

the speed attains steady state at 0.1ms.

Figure 17 Torque curve for Brushless DC motor

Figure 18 Speed curve for Brushless DC Motor

8. CONCLUSION

In this paper, the Soft Computing based GA for MPPT in solar panel configuration coupled

with PID based Fuzzy controller for Brushless DC Motor is employed. A PID controller is

used by the outer loop to develop the performance of speed control. Since only one current

sensor is being used, financial management also gets better with desired output. By inferring

the simulation results, the steady state condition attained in 0.1ms which will be great boost to

the application point of view. Finally, qualified performance was verified by simulation

results under different work conditions, at different speeds, and under different loads.

It should be noted that reducing the quantity of current sensor surely brings some negative

impacts to the control system, such as maximum current limitation in certain modes.

Additionally, the program tends to be complicated because a special algorithm is necessary as

compensation on the reduction of current sensor. Consequently, the software overhead is

increased. For further research, how to improve system reliability and optimize software

design should be the key point to implement the proposed strategy in industrial application



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