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Closed Loop Speed Control of Bldc Motor with Design and Modelling Using Fuzzy Logic Controller

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Annals of R.S.C.B., ISSN:1583-6258, Vol. 25, Issue 3, 2021, Pages. 8056 - 8063 Received 16 February 2021; Accepted 08 March 2021.

Closed Loop Speed Control of Bldc Motor with Design and Modelling Using Fuzzy Logic Controller

TM Navinkumar1, A.Anci Manon Mary2, P.V. Gokila3,A.Saranya4,M.Sasireka5,S.Naganandhini6

1Assistant Professor, Department of EEE, K.RamakrishnanCollege of Engineering,Samayapuram.

E.mail:[email protected]

2Assistant Professor, Department of EEE, Karpagam College of Engineering,Coimbatore.

E.mail:[email protected]

3Assistant Professor, Department of EEE, J.K.K.N College of Engineering and Technology, Komarapalayam.E.mail:[email protected]

4Assistant Professor, Department of EEE , M.Kumarasamy College of Engineering,Karur.

E.mail:[email protected]

5Assistant Professor(Selection Grade), Department of EIE,Kongu Engineering College,Perundurai Email:[email protected].

6Assistant Professor, Department of CSE, PSNA College of Engineering and Technology,Dindigul.

Email:[email protected].

* Corresponding author mail id: [email protected], [email protected]

Abstract— Brushless Less DC motors have certain features such as large initial twisting speed, efficiency and long life and are also referred to as a compatible magnetic motor. Due to the making of similar motor for censorious application it is majorly applied in the industrial area. By analysing the BLDC motor with the normal induction motor and the DC motor has so many advantages such as longevity and no necessity for machine replacement. To stabilize the machine in this paper we have used the Proportional Integral Derivative controller and the sensible controller. In BLDC Motor by providing the gate signal a theta angle value is given. Using MATLAB Simulation features of BLDC vehicles such as rear EMM, speed, current limit.

Keywords— BLDC motor, Fuzzy logic controller, PID controller, Inverter circuit model.

I. INTRODUCTION

The DC motor main problem is maintenance and also the brushes are the main issue.In recent trends the brushless DC motor is the load utilised as it has high efficiency and reliability of the machine leads towards less maintenance [1-4]. The brushless DC motor is activated by electronically commutated method by means of voltage source inverter by modifying the frequency based on load. The rotor position modified by maintaining the electronic switches with stator winding properly energised in correct way so as to get continuous rotating emf on motor. In this way the electromagnetic interference sparking and friction can be eliminated.

By controlling the size and quantity of stator power the motion of the Brushless DC motor is balanced and the ratio of stator power to frequency is always maintained. Compared to a standard regulator the paper can examine high in different explanations [5]. The crossover Fuzzy drive regulator is utilized that changes the regulator to make it work better with a Brushless DC vehicle. Using the PWM process the switching process is generated according to overshoot, oscillation and other conflicting losses [6][11][12]. With the Xilinx FPGA Software 400E processor the incomprehensible control model is used in real time. Under

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Annals of R.S.C.B., ISSN:1583-6258, Vol. 25, Issue 3, 2021, Pages. 8056 - 8063 Received 16 February 2021; Accepted 08 March 2021.

various burden conditions the speed to be kept up is constantly introduced [7][8][9][10]. The various car ratings the proposed system makes for free offset monitoring, ignoring dynamic response and normal frequency.

II. BRUSH LESS DIRECT CURRENT MOTOR

In a Brushless DC motor the vehicle will be continuously rotated by changing the solid state thyristor ON on the motor winding will be enabled [8-16]. Figure 1 shows a basic Brushless DC driver that you drive with a semiconductor switch such as MOSFET. The Brushless DC car works both in three-phase operation and one-phase operation.

The equation regarding the torque and emf equation was given in 2.1-2.3.

Vab = R(ia - ib ) + L d/dt (ia - ib) +(ea - eb ) - (2.1) Vca = R(ic – ia) + L d/dt (ic – ia) + (ec – ea) - (2.2) Vbc = R(ib – ic ) + L d/dt (ib – ic) + (eb – ec) - (2.3) Formula of movement is given as,

Ta = BWm + J d/dtWm + TL - (2.4) The voltage equations becomes,

Vab = R(ia - ib ) + L d/dt (ia - ib) +(ea - eb) - (2.5) Vbc=R(ib –2 ib ) + Ld/dt(ib –2 ib)+(eb –ec) -(2.6)

BLDC motors have hall Effect sensors as position regulator . Contingent upon the need the vehicle speed can be constrained by the MOSFT Gate driver circuit.. With precise speed control acquired by PID and incomprehensible controller. The four-wheel drive BLDC drive produces optimal performance for a variety of speed conditions.

Fig.1 Circuit for brush less DC drive III. CONTROLLERS

A. PID CONTROLLER

• Inference mechanism: The mapping is done between the input and output values by using the

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Annals of R.S.C.B., ISSN:1583-6258, Vol. 25, Issue 3, 2021, Pages. 8056 - 8063 Received 16 February 2021; Accepted 08 March 2021.

fuzzification method and it is allowed by interference mechanism [2]. The Mamdani and the Sugeno method is the most commom methods used for interference mechanism, in this method we are using the Mamdani method.

• Defuzzification: The required Crisp Value is converted from the fuzzy reasoning Mechanism is done by using the Defuzzification method.

Transfer function of PID controller: KP+ K1/S+ Kd S

Fig 2PID Controller. Block diagram

B. FUZZY LOGIC CONTROLLERS

Fig 3 Controller interfacing the system (Fuzzy Logic)

 Fuzzification: By using the predefined membership function the values for fuzzification is choosed.

The exponential, sinusoidal, trapezoidal and triangular are the other membership functions are used for fuzzification.

 Rule Matrix: The fuzzy operators and fuzzy set is explained by Rule matrix and is explained in standard state

 Mamdani and the Sugeno method is the most commom methods used for interference mechanism, in this method we are using the Mamdani method.

 Defuzzification: The required Crisp Value is converted from the fuzzy reasoning Mechanism is done by using the Defuzzification method.

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Annals of R.S.C.B., ISSN:1583-6258, Vol. 25, Issue 3, 2021, Pages. 8056 - 8063 Received 16 February 2021; Accepted 08 March 2021.

IV. SIMULATION MODEL OF SPEED CONTROL OF BLDC MOTOR USING MATLAB

A. CIRCUIT DIAGRAM OF Brushless DC motor.

The Fig 4 explains the capacity circuit for Brushless DC engine speed control by utilizing proficient regulator are fluffy and PID regulator.

Fig.4.Functional circuit for BLDC motor

Fig 5 Brushless DC motor simulation model

The working reliability of BLDC Motor is explained by means of simulink. The duty cycle of the semiconductor device such as MOSFET is controlled by Fuzzy and PI controllers. Stator of the BLDC Fig 6 Brushless DC motor simulation model

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Annals of R.S.C.B., ISSN:1583-6258, Vol. 25, Issue 3, 2021, Pages. 8056 - 8063 Received 16 February 2021; Accepted 08 March 2021.

The major blocks of BLDC model block are Inverter circuit block, Controller block and Subsystem 1.In this paper, fuzzy merged with PID controller was activated Figure 7 shows the proposed system of the three phase brushless DC motor.

V.WAVEFORM ANALYSIS OF CIRCUIT PARAMETERS

Fig 6 waveforms of Stator Current

Fig 7 Speed, Torque and Rotor angle waveforms

Speed/Time table

Speed Vs Time for closed Loop

Reference speed(rpm)

Reference time(s)

1800 0.01

1600 0.02

1400 0.03

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Annals of R.S.C.B., ISSN:1583-6258, Vol. 25, Issue 3, 2021, Pages. 8056 - 8063 Received 16 February 2021; Accepted 08 March 2021.

Table1 Speed Vs Time for closed loop

Fig 8 Speed Vs Time

Figure 8 shows the curve for speed in BLDC Motor at no heap condition where reference is set at 1500 rpm. Time to attain the rated speed of motor is 0.04s at 1500 rpm.

VI. CONCLUSION

By using an unobtrusive sensor and a PID controller on a BLDC motor the speed is controlled under various load conditions. By the output voltage from the inverter is used to control the motor speed. In a different loading mode the gate signal is generated. Using the MATLAB SIMULATION software various features such as current load, output speed and background EMM are analyzed under different load conditions. Vehicle performance is improved at a wider range of load on the proposed system.

REFERENCES

[1] V. Bist and B. Singh, july 2015,‖ A Unity Power Factor Bridgeless Isolated Cuk Converter fed Brushless DC Motor Drive‖, IEEE Transaction on Industrial Electronics, vol. 62, no. 7, pp.4118-4129.

[2] M. Kwon, K.-H.Nam, and B.-H. Kwon, "Photovoltaic power conditioning system with line connection", IEEE Transactions on Industrial Electronics, Vol. 53, No. 4, pp. 1048 – 1054, June 2006.

[3] Stjepan Bogdan, and Zdenko Kovaccic ―Fuzzy Controller design theory and Application‖,@

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[4] T.Gowthamraj, Dr.R.Udhaya Shankar, Dr.Rani Thottungal, ―comparativeanalysisofcukand luoconverterfedbldcmotor‖International journlof applied engineering research,vol.10,no.88, pp.68-72, 2015.

[5] A.Rubaai, D.Ricketts and M.Kankam, ―Experimental verification of a hybrid fuzzy control statergy for a high performance brushless DC motor drive system‖, IEEE Transaction on Indusry Application, Vol.37, No.2, pp.503-512,2001.

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[6] S.K.Saranya, T.Gowthamraj, P.Ranjani, ―THD Analysis in Three Phase-Three Level

VSI with MPPTTrackerandSEPIC ConverterforSolar PV

Array‖,JournalofAdvancedChemistryvol.12,no.16, pp.4895-4901,2016.

[7] Karthikeyan.R, GN.Sachin Amreiss ―PV Based Interleaved Boost Converter for Pumping Applications‖, 2018 International Conference on Intelligent and Advanced System (ICIAS).

[8] Subramanian, S., Mohan, R., Shanmugam, S.K. et al. Speed control and quantum vibration reduction of Brushless DC Motor using FPGA based Dynamic Power Containment Technique. J Ambient Intell Human Comput (2021). https://doi.org/10.1007/s12652-021-02969-5.

[9] S. K. Shanmugam, S. Ramachandran, S. Arumugam, S. Pandiyan, A. Nayyar and E. Hossain,

"Design and Implementation of Improved Three Port Converter and B4-Inverter Fed Brushless Direct Current Motor Drive System for Industrial Applications," in IEEE Access, vol. 8, pp.

149093-149112, 2020, doi: 10.1109/ACCESS.2020.3016011.

[10] Sathish KUMAR SHANMUGAM1 , Karthikeyan MUTHUSAMY2 , Vijayachitra CHENNIPPAN2 , Suresh BALASUBRAMANIAM3 , Sampathkumar RAMASAMY3 , Saravanan SUBRAMANIAN MODELLING OF SOLAR PHOTOVOLTAIC ARRAY FED BRUSHLESS DC MOTOR DRIVE USING ENHANCED DC-DC CONVERTER PROCEEDINGS OF THE ROMANIAN ACADEMY, Series A Volume 20, Number 2/2019, pp. 169–178.

[11]Shanmugam Sathish Kumar, Arumugam Senthilkumar, Palanirajan Gowtham, Ramachandran Meenakumari, Kanagaraj Krishna Kumar Implementation of solar photovoltaic array and battery powered enhanced DC-DC converter using B4-inverter fed brushless DC motor drive system for agricultural water pumping applications. Journal of Vibroengineering, Vol. 20, Issue 2, 2018, p.

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[12]Shanmugam Sathish Kumar, Senthilkumar Arumugam Design and implementation of DC source fed improved dual-output buck-boost converter for agricultural and industrial applications.

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[13] Shanmugam, S.K., Ramachandran, M., Kanagaraj, K.K. and Loganathan, A. (2016) Sensorless Control of Four-Switch Inverter for Brushless DC Motor Drive and Its Simulation.

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[14] V. Krishnaveni, K. Kiruthika and S. S. Kumar, "Design and implementation of low cost four switch inverter for BLDC motor drive with active power factor correction," 2014 International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE), Coimbatore, India, 2014, pp. 1-7, doi: 10.1109/ICGCCEE.2014.6922424.

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Annals of R.S.C.B., ISSN:1583-6258, Vol. 25, Issue 3, 2021, Pages. 8056 - 8063 Received 16 February 2021; Accepted 08 March 2021.

Microcontroller Based BLDC Motor Drive for Commercial Applications. In: Kamalakannan C., Suresh L., Dash S., Panigrahi B. (eds) Power Electronics and Renewable Energy Syste ms.

Lecture Notes in Electrical Engineering, vol 326. Springer, New Delhi.

https://doi.org/10.1007/978-81-322-2119-7_81

[16] Meenakumari Ramachandran Sathishkumar ShanmugamDesign and Implementation of Embedded Processor Based Brushless Motor Drive using Lead Acid Battery as Source with Lithium Ion Capacitor, Indonesian Journal of Electrical Engineering and Computer Science,Volume 14 Issue 3 Pages 455-469.

7)Amin Salih Mohammed, Saravana Balaji B, Saleem Basha M S, Asha P N, Venkatachalam K(2020),FCO — Fuzzy constraints applied Cluster Optimization technique for Wireless AdHoc Networks,Computer Communications, Volume 154,Pages 501-508.

8)Ponmagal, R.S., Karthick, S., Dhiyanesh, B. et al. Optimized virtual network function provisioning technique for mobile edge cloud computing. J Ambient Intell Human Comput (2020).

9)Ramamoorthy, S., Ravikumar, G., Saravana Balaji, B. et al. MCAMO: multi constraint aware multi-objective resource scheduling optimization technique for cloud infrastructure services. J Ambient Intell Human Comput (2020).

10) Basha, A.J., Balaji, B.S., Poornima, S. et al. Support vector machine and simple recurrent network based automatic sleep stage classification of fuzzy kernel. J Ambient Intell Human Comput (2020)

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