i-manager Publications

i-manager's Journal on Instrumentation and Control Engineering (JIC)

Current Issue Vol. 12 Issue 1

Most Cited

Diagnosis of Air-Gap Eccentricity Fault for Inverter Driven Induction Motor Drives in the Transient Condition
Modelling and Simulation Study of a Helicopter with an External Slung Load System
Comparative Study of Single Phase Power Inverters Based on Efficiency and Harmonic Analysis
LabVIEW Based Design and Analysis of Fuzzy Logic, Sliding Mode and PID Controllers for Level Control in Split Range Plant
Trichotomous Exploratory Data Analysis [Tri–EDA]: A Post Hoc Visual Statistical Cumulative Data Analysis Instrument Designed to Present the Outcomes of Trichotomous Investigative Models

Volume 7 Issue 2 February - April 2019

Research Paper

Investigation of Genetic Algorithm Tuned PI Controller for the Non-Linear Conical Tank Level Process

Arivalahan R.* , Balaji S., Tamil Arasan P.*

*-***Department of Electrical and Electronics Engineering, SRM Valliammai Engineering College, Chennai, Tamil Nadu, India.

Arivalahan, R., Balaji, S., and Arasan, T. P. (2019). Investigation of Genetic Algorithm Tuned PI Controller for the Non-Linear Conical Tank Level Process.i-manager's Journal on Instrumentation and Control Engineering, 7(2), 1-8. https://doi.org/10.26634/jic.7.2.16725

Abstract

Recently, the PID Controllers are widely used in industries for nearly a century due to its features, such as simplicity, flexibility, and efficiency. Recently, the control concepts of non-linear processes in the industries are turned towards the attention of the intelligent controllers, such as Genetic Algorithm tuned PI Controllers, Neural Networks tuned PI Controller, Model Predictive Controller, Nonlinear Adaptive Controller, Fuzzy Logic based Controller, Neuro tuned Predictive Controller, etc. This paper focuses on the Investigation of Genetic Algorithm tuned PI Controller for the Nonlinear Conical tank Level Process. The Conical tank is a highly nonlinear process in which the variation in the area of cross section of the process tank level system with change in shape. In the above work, the Genetic Algorithm tuned PI Controller is especially designed for the control of nonlinear conical tank level process for the exact level maintenance. Also, the designed Genetic Algorithm tuned PI Controller is compared with Conventional PI Controller in Servo operation and Regulatory operation.

References

[1]. Åström, K. J., & Hägglund, T. (2001). The future of PID control. Control Engineering Practice, 9(11), 1163-1175. https://doi.org/10.1016/S0967-0661(01)00062-4

[2]. Bhaba, P. K., Sathishbabu, S., Asokan, A., & Karunanithi, T. (2007). Real time implementation of Wiener Model PI (WMPI) controller in a conical tank liquid level process. Applied Sciences, 7(15), 2194-2197. https://doi.org/10. 3923/jas.2007.2194.2197

[3]. Bhaba, P. K., & Somasundaram, S. (2009). Real time implementation of a new CDM-PI control scheme in a conical tank liquid level maintaining system. CCSE Modern Applied Science, 3(5), 38-45.

[4]. Bhuvaneswari, N. S., Uma, G., & Rangaswamy, T. R. (2008). Neuro based model reference adaptive control of a conical tank level process. Control and Intelligent Systems, 36(1), 98-106. https://doi.org/10.2316/Journal. 201.2008.1.201-1895

[5]. Coon, C., Cohen, G. H. & Coon, G. A. (1953). Theoretical consideration of retarded control. Transactions of the ASME, 75(1), 827-834.

[6]. David, L. (1991). Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York.

[7]. Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization and Machine Learning, First Edition. Addison Wesley Publishing Company, USA.

[8]. Holland, J. H. (1992). Genetic Algorithms. Scientific American, 267(1), 66-73.

[9]. Ingimundarson, A., & Hägglund, T. (2002). Performance comparison between PID and dead-time compensating controllers. Journal of Process Control, 12(8), 887-895. https://doi.org/10.1016/S0959- 1524(02)00017-3

[10]. Levine, W. S. (2000). The Control Handbook. FL:CRC press.

[11]. Liptak, B. G. (1995). Process Control, Instrumentation rd Engineers Handbook (3 Ed). UK: Butterworth and Heinemann Company.

[12]. Madhubala, T. K., Boopathy, M., Chandra, J. S., & Radhakrishnan, T. K. (2004, January). Development and tuning of fuzzy controller for a conical level system. In Proceedings of International Conference on Intelligent Sensing and Information Processing, 2004 (pp. 450-455). IEEE. https://doi.org/10.1109/ICISIP.2004.1287699

[13]. Manic, K. S., Sivakumar, R., Nerthiga, V., Akila, R., & Balu, K. (2009). Design of plant estimator model using neural network. IJCSNS, 9(6), 142-147.

[14]. Nithya, S., Gour, A. S., Sivakumaran, N., Radhakrishnan, T. K., Balasubramanian, T., & Anantharaman, N. (2008). Design of intelligent controller for non-linear processes. Asian Journal of Applied Sciences, 1(1), 33-45. https://doi.org/10.3923/ajaps. 2008.33.45

[15]. Skogestad, S. (2003). Simple analytic rules for model reduction and PID controller tuning. Journal of Process Control, 13(4), 291-309. https://doi.org/10.1016/S0959- 1524(02)00062-8

[16]. Valarmathi, K., Devaraj, D., & Radhakrishnan, T. K. (2007). Adaptive enhanced genetic algorithm base proportional integral controller tuning for pH process. Instrumentation Science and Technology, 35(6), 619-635. https://doi.org/10.1080/10739140701651607

[17]. Ziegler, J. G., & Nichols, N. B. (1942). Optimum settings for automatic controllers. Transactions of ASME, 64(11), 759-765.

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Research Paper

A Study on Effect of Added Contaminants in Lubricants by Using Wear Debris and Vibration Analysis Technique

Vikram * , Vishal Lawande**

*-** Department of Mechanical Engineering, Padmashri Dr. V. V. P. Institution of Technology and Engineering (Polytechnic) Loni, Uttar Pradesh, India.

Yendhe, V., and Lawande, V. (2019). A Study on Effect of Added Contaminants in Lubricants by Using Wear Debris and Vibration Analysis Technique. i-manager's Journal on Instrumentation and Control Engineering, 7(2), 9-17. https://doi.org/10.26634/jic.7.2.15957

Abstract

Condition monitoring of machines is determination of condition of machine and its change with time. Working condition of machines may be analyzed by measuring physical parameters like: vibration, noise, wear debris, temperature, oil contamination, etc. Wear debris and vibration condition monitoring have great importance in machinery maintenance and fault diagnosis. This paper deals with effective analysis of both combined vibration condition monitoring and wear debris in machinery maintenance and fault diagnosis. Both techniques have their own merits and demerits associated with monitoring and fault diagnosis of machinery. However, it is seen from the past practical experience that using these techniques independently gives a small portion of machine fault diagnosis. By combining both the techniques in machine fault diagnosis it can provide more reliable information. The objective of this paper is to analyse the co-relation between techniques, achieved by experimenting worm gear box at different operating conditions driven by electric motor. Initially, the worm gear box runs under normal conditions. A number of tests has been performed with different contaminant particles added to various lubricants. Wear debris and condition monitoring techniques were studied and results obtained were compared.

References

[1]. Ebersbach, S., Peng, Z., & Kessissoglou, N. J. (2006). The investigation of the condition and faults of a spur gearbox using vibration and wear debris analysis techniques. Wear, 260(1-2), 16-24. https://doi.org/10. 1016/j.wear.2004.12.028

[2]. Edmonds, J., Resner, M. S., & Shkarlet, K. (2000, March). Detection of precursor wear debris in lubrication systems. In 2000 IEEE Aerospace Conference. Proceedings (Cat. No. 00TH8484) (Vol. 6, pp. 73-77). IEEE. https://doi.org/10. 1109/AERO.2000.877884

[3]. Govindarajan, N., & Gnanamoorthy, R. (2008). Ferrography - A procedure for measuring wear rate. Indian Journal of Engineering and Materials Sciences (IJEMS), 15(5), 377-381.

[4]. Heidarbeigi, K., Ahmadi, H., & Omid, M. (2010). Adaptive vibration condition monitoring techniques for local tooth damage in gearbox. Modern Applied Science, 4(7), 104-110.

[5]. Hongbo, F., Yingtang, Z., Guoquan, R., & Fei, C. (2009, August). Study on oil detection technology based on inductive wear debris sensor. In 2009 9^th International Conference on Electronic Measurement & Instruments. IEEE. https://doi.org/10.1109/ICEMI.2009. 5274434

[6]. Liu, Y., Li, X., Cao, X., & Xiong, W. (2009, August). Online monitoring system of vehicle lubricants quality based on the permittivity. In 2009 International Conference on Mechatronics and Automation (pp. 2591-2595). IEEE. https://doi.org/10.1109/ICMA.2009.5246753

[7]. Mortier, R. M., Orszulik, S. T., & Fox, M. F. (Eds.). (2010). Chemistry and Technology of Lubricants (Vol. 107115). New York: Springer.

[8]. Murali, S. V., Choy, F. K., Zhe, J., Carletta, J., & Xia, X. (2008, January). Oil debris detection using static and dynamic capacitance measurements. In STLE/ASME 2008 International Joint Tribology Conference (pp. 241- 243). American Society of Mechanical Engineers Digital Collection. https://doi.org/10.1115/IJ TC2008-71280

[9]. Peng, Z., & Kessissoglou, N. (2003). An integrated approach to fault diagnosis of machinery using wear debris and vibration analysis. Wear, 255(7-12), 1221- 1232. https://doi.org/10.1016/S0043-1648(03)00098-X

[10]. Peng, Z., Kessissoglou, N. J., & Cox, M. (2005). A study of the effect of contaminant particles in lubricants using wear debris and vibration condition monitoring techniques. Wear, 258 (11-12), 1651-1662. https://doi.org/10.1016/j.wear.2004.11.020

[11]. Pouzar, M., Černohorský, T., & Krejčová, A. (2001). Determination of metals in lubricating oils by X-ray fluorescence spectrometry. Talanta, 54(5), 829-835. https://doi.org/10.1016/S0039-9140(01)00338-1

[12]. Roylance, B. J., & Hunt, T. M. (1999). The Wear Debris Analysis. Coxmoor Publishing Company, Oxford, UK.

[13]. Scheffer, C., & Girdhar, P. (2004). Practical Machinery Vibration Analysis and Predictive Maintenance. Elsevier.

[14]. Sondhiya, O. P., & Gupta, A. K. (2012). Wear debris analysis of automotive engine lubricating oil using by ferrography. International Journal of Engineering and Innovative Technology, 2(5), 46-54.

[15]. Yarrow A. S. (1993). An overview of mechanical condition monitoring by wear debris analysis. Australian Society of Sugar Cane Technologists, 83-88.

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Research Paper

PD/PID-Fuzzy Logic Controller Based Tracking Control of 2-Link Robot Manipulator

Amit Kumar* , Shrey Kasera, Lal Bahadur Prasad*

*-*** Department of Electrical Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, Uttar Pradesh, India.

Kumar, A., Kasera, S., and Prasad, L. B. (2019). PD/PID-Fuzzy Logic Controller Based Tracking Control of 2-Link Robot Manipulator. i-manager's Journal on Instrumentation and Control Engineering, 7(2), 18-25. https://doi.org/10.26634/jic.7.2.16658

Abstract

Robot manipulators are multi-link nonlinear system with higher uncertainties and disturbances. To obtain desired accuracy and high performance, the controlling of robot should be in real time. Due to higher uncertainties and disturbances like variation in pay load frictions, the performance and accuracy of the system is highly deviated from desired performance and accuracy. This paper presents PD Fuzzy logic controller based scheme to track the desired trajectory of 2-link robot manipulator. In this PD fuzzy logic controller based scheme, the output of the system is fed to Fuzzy logic controller as input. The error and change in error is given as input to Fuzzy logic controller and this control scheme is capable in compensation of disturbances. In MATLAB environment, the simulation is performed and the reference trajectory of robotic system is tracked with minimum error.

References

[1]. Botsali, F. M., Tinkir, M., Kalyoncu, M., & Önen, Ü. (2010, February). Fuzzy logic trajectory control of flexible robot manipulator with rotating prismatic joint. In 2010 The 2^ndInternational Conference on Computer and Automation Engineering (ICCAE) (Vol. 3, pp. 35-39). IEEE. https://doi.org/10.1109/ICCAE.2010.5451849

[2]. Chien, C. (1990). Fuzzy logic in control systems: Fuzzy logic controller. IEEE Trans. Syst. Man Cybern. Part II, 20(2), 419-435.

[3]. Edwards, M. (1984). Robots in industry: An overview. Applied Ergonomics, 15(1), 45-53. https://doi.org/10. 1016/S0003-6870(84)90121-2

[4]. Jimenez, A., & Al Hadithi, B. M. (Eds.) (2009). Robot manipulators: Trends and development. Intech Open https://doi.org/10.5772/218

[5]. Kelly, R. (1997). PD control with desired gravity compensation of robotic manipulators: A review. The International Journal of Robotics Research, 16(5), 660- 672. https://doi.org/10.1177%2F027836499701600505

[6]. Khan, M. F., ul Islam, R., & Iqbal, J. (2012, October). Control strategies for robotic manipulators. In 2012 International Conference of Robotics and Artificial Intelligence (pp. 26-33). IEEE. https://doi.org/10.1109/ICR AI.2012.6413422

[7]. Lochan, K., & Roy, B. K. (2015). Control of two-link 2- dof robot manipulator using fuzzy logic techniques: A review. In Proceedings of Fourth International Conference on Soft Computing for Problem Solving (pp. 499-511). Springer, New Delhi. https://doi.org/10.1007/97 8-81-322-2217-0_41

[8]. Mohammed, R. H., Bendary, F., & Elserafi, K. (2016). Trajectory tracking control for robot manipulator using fractional order-fuzzy-PID controller. International Journal of Computer Applications, 134 (15), 22-29. https://doi.org/10.5120/ijca2016908155

[9]. Ouyang, P. R., & Zhang, W. J. (2004, September). Development of an adaptive learning PD control for robotic system applications. In Proceedings of the 2004 IEEE International Conference on Control Applications, 2004. (Vol. 1, pp. 394-399). IEEE. https://doi.org/10.1109/ CCA.2004.1387243

[10]. Ouyang, P. R., & Zhang, W. J. (2008, June). Comparison of PD-based Controllers for robotic Manipulators. In ASME 2004 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (pp. 23-31). American Society of Mechanical Engineers Digital Collection. https://doi.org/10.1115/DETC2004-57015

[11]. Prabhu, S. M., & Garg, D. P. (1998). Design of a fuzzy logic based robotic admittance controller. Intelligent Automation & Soft Computing, 4(2), 175-189. https://doi.org/10.1080/10798587.1998.10750730

[12]. Purwar, S., Kar, I. N., & Jha, A. N. (2005). Adaptive control of robot manipulators using fuzzy logic systems under actuator constraints. Fuzzy Sets and Systems, 152(3), 651-664. https://doi.org/10.1016/j.fss.2004.11. 012

[13]. Rastogi, E., & Prasad, L. B. (2015, December). Comparative performance analysis of PD/PID computed torque control, filtered error approximation based control and NN control for a robot manipulator. In 2015 IEEE UP Section Conference on Electrical Computer and Electronics (UPCON) (pp. 1-6). IEEE. https://doi.org/10. 1109/UPCON.2015.7456706

[14]. Spong, M. W., Hutchinson, S., & Vidyasagar, M. (2012). Robot Modeling and Control. New York: John Wiley & Sons, pp.122-131.

[15]. Su, Y. X., Sun, D., & Duan, B. Y. (2005). Design of an enhanced nonlinear PID controller. Mechatronics, 15(8), 1005-1024. https://doi.org/10.1016/j.mechatronics. 2005.03.003

[16]. Sumathi, P. (2005, July). Precise tracking control of robot manipulator using fuzzy logic. In IEEE International Conference on Mechatronics, 2005, ICM'05 (pp. 852-857). IEEE. https://doi.org/10.1109/ICMECH.2005.1529373

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Research Paper

Design and Implementation of Microcontroller based Low Cost Control and Data Acquisition System for Electric Drives

Sandeep Sanjay Kulkarni* , Jayant J. Mane, Sanjay Baburao Kulkarni*

*-*** Department of Electrical Engineering, Finolex Academy of Management and Technology, Ratnagiri, Maharashtra, India.

Kulkarni, S. S., Mane, J. J., and Kulkarni, S. B. (2019). Design and Implementation of Microcontroller based Low Cost Control and Data Acquisition System for Electric Drives. i-manager's Journal on Instrumentation and Control Engineering, 7(2), 26-31. https://doi.org/10.26634/jic.7.2.16762

Abstract

From many years in order to maximize the working productivity of the industrial equipment, the control and data acquisition systems have always been available for modernized industrial architecture. However, to increase the reliability of the plant/process, these systems have always tended to be expensive on small and medium scale purposes. Similarly, electric drives find many applications in industries as well as household purposes. Also, for a greater productivity of electric drives, control becomes necessary and will always benefit the end user. This paper presents a microcontrollerbased control and data acquisition system for electric DC drives that is low cost, modular, and can be installed in stages determined by budget constraints or process requirements. The presented system includes a supervisory desktop computer with specifically developed Data Acquisition software. Arduino UNO Microcontroller Development Board is used for drive speed and directional control using Pulse Width Modulation (PWM) technique. Data transfer from desktop to controller and controller to desktop is done using the Serial Port Connectivity (SPC) of the microcontroller. Microsoft Visual Studio is used to develop the necessary GUI Software System. VB.NET is used as the backend programming language for drive control and data acquisition. The data acquired can be stored into Microsoft Excel Workbook for further analysis and the speed v/s voltage characteristics can be saved as an image file.

References

[1]. Arduino Strings. (n.d). Retrieved from https://www. arduino.cc/reference/en/language/variables/datatypes/ string/

[2]. Guozhen, H., Changsong, C., & Shanxu, D. (2009, May). Solutions for SCADA system communication reliability in photovoltaic power plants. In 2009 IEEE 6^th International Power Electronics and Motion Control Conference (pp. 2482-2485). IEEE. https://doi.org/10. 1109/IPEMC.2009.5157821

[3]. Jones, G. E., Hammond, P. S., & Thurston, R. R. (1990, December). A low cost SCADA system for kilns and furnaces. In IEE Colloquium on Advanced SCADA and Energy Management Systems (pp. 2-1). IET.

[4]. Radvanovsky, R., & Brodsky, J. (2013). Handbook of SCADA/Control Systems Security. NY: CRC Press.

[5]. Sagi, M., & Varga, E. (2017, November). DHT based SCADA architecture. In 2017 25^th Telecommunication Forum (TELFOR) (pp. 1-4). IEEE. https://doi.org/10. 1109/TELFOR.2017.8249471

[6]. Windows App Tutorials. (n.d). How to build a guage control for universal Windows App. Retrieved from http://windows-apptutorials.com/windows-10/ui-windows- 10/how-to-build-a-gauge-control-for-universal-windowsapp/

[7]. Yin, H., Jia, R., Ma, F., & Wang, D. (2018, October). Wind turbine condition monitoring based on SCADA data analysis. In 2018 IEEE 3^rdAdvanced Information Technology, Electronic and Automation Control Conference (IAEAC) (pp. 1101-1105). IEEE. https://doi.org/ 10.1109/IAEAC.2018.8577523

[8]. Youngblood, T. (2015). Make a digital voltmeter using an Arduino. In All About Circuits. Retrieved form https://www.allaboutcircuits.com/projects/make-a-digital- voltmeter-using-the-arduino/

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Research Paper

Implementation of Nature-Inspired Optimization Algorithms for BLDC Motor Drive Control

Manoj Kumar Merugumalla*

Department of Electrical and Electronics Engineering, Tirumala Engineering College, Jonnalagadda, Andhra Pradesh, India.

Merugumalla, M. K. (2019). Implementation of Nature-Inspired Optimization Algorithms for BLDC Motor Drive Control. i-manager's Journal on Instrumentation and Control Engineering, 7(2), 32-41. https://doi.org/10.26634/jic.7.2.16717

Abstract

Nature-inspired optimization algorithms are presented in this paper for the control of brushless direct current (BLDC) motor drive. These algorithms have a number of advantages over classical methods for solving complex optimization problems. The algorithms in this class are based on perceptive behaviour of well ordered members of the population. This type of nature can be found among birds, fishes and insects, such as ants, bees, and the like. These algorithms have mastery over classical methods for solving complex and badly corroborated problems. The key feature of the population algorithms is that the search strategy should be simple and independent. No agent should have dominance over the entire search process. The commutation is implemented electronically in this motor drive using power electronic switches to modify current in the windings based on rotor position. The BLDC motor is just a reversed DC commutator motor, as its conductors remain stationary while magnet rotates facilitating the internal or external position sensors in the motors to sense the actual position of rotor. The rotor position can also be found by the measurement of variations in the Back emf, which is known as sensorless control method. This method reduces the cost as well as the size of the motor as it does not require sensors for determining rotor position. Matlab/simulink is used for modelling drive system. The simulation results with the proposed optimization algorithms are effective in controlling the speed of the drive system.

References

[1]. Baszynski, M., & Pirog, S. (2013). A novel speed measurement method for a high-speed BLDC motor based on the signals from the rotor position sensor. IEEE Transactions on Industrial Informatics, 10(1), 84-91. https://doi.org/10.1109/TII.2013.2243740

[2]. Cui, C., Liu, G., Wang, K., & Song, X. (2014). Sensorless drive for high-speed brushless DC motor based on the virtual neutral voltage. IEEE Transactions on Power Electronics, 30(6), 3275-3285. https://doi.org/10.11 09/TPEL.2014.2337292

[3]. Goel, S., Goel, N., & Gupta, D. (2014). Unconstrained optimisation through bat algorithm. International Journal of Intelligent Engineering Informatics, 2(4), 259-270. https://doi.org/10.1504/IJIEI.2014.067162

[4]. Hung, C. W., Lin, C. T., Liu, C. W., & Yen, J. Y. (2007). A variable-sampling controller for brushless DC motor drives with low-resolution position sensors. IEEE Transactions on Industrial Electronics, 54(5), 2846-2852. https://doi.org/ 10.1109/TIE.2007.901303

[5]. Jan, R. M., Tseng, C. S., & Liu, R. J. (2008). Robust PID control design for permanent magnet synchronous motor: A genetic approach. Electric Power Systems Research, 78(7), 1161-1168. https://doi.org/10.1016/ j.epsr.2007.09.011

[6]. Juniku, I., & Marango, P. (2015). PID design with bioinspired intelligent algorithms for high order systems. International Journal of Mathematics and Computers in Simulation, vol, 9, 44-52.

[7]. Kennedy, J., & Eberhart, R. (1995). Particle Swarm Optimization. Proceedings of the IEEE International Conference on Neural Networks, 4, 1942-1948. http://doi.org/10.1109/ICNN.1995.488968

[8]. Kim, T. H., & Ehsani, M. (2004). Sensorless control of the BLDC motors from near-zero to high speeds. IEEE Transactions on Power Electronics, 19(6), 1635-1645. https://doi.org/10.1109/TPEL.2004.836625

[9]. Krishnan, R. (2001). Electric Motor Drives: Modeling, Analysis and Control. Upper Saddle River: Prentice Hall.

[10]. Lin, C. L., & Jan, H. Y. (2002, November). Evolutionarily multiobjective PID control for linear brushless DC motors. In IEEE 2002 28^th Annual Conference of the Industrial Electronics Society. IECON 02 (Vol. 3, pp. 2033- 2038). IEEE. https://doi.org/10.1109/IECON.2002.1185 285

[11]. Mansouri, M., Kaboli, S. H. A., Ahmadian, J., & Selvaraj, J. (2012, November). A hybrid Neuro-Fuzzy - PI speed controller for BLDC enriched with an integral steady state error eliminator. In 2012 IEEE International Conference on Control System, Computing and Engineering (pp. 234-237). IEEE. https://doi.org/10.1109/ ICCSCE.2012.6487147

[12]. Merugumalla, M. K., & Navuri, P. K. (2016). Sensorless control of BLDC motor using bio-inspired optimization algorithm and classical methods of tuning PID controller. imanager's Journal on Instrumentation & Control Engineering, 5(1), 16-23. https://doi.org/10.26634/ jic.5.1.10349

[13]. Premkumar, K., & Manikandan, B. V. (2016). Bat algorithm optimized fuzzy PD based speed controller for brushless direct current motor. Engineering Science and Technology, an International Journal, 19(2), 818-840. https://doi.org/10.1016/j.jestch.2015.11.004

[14]. Stirban, A., Boldea, I., & Andreescu, G. D. (2012). Motion-sensorless control of BLDC-PM motor with offline FEM-information-assisted position and speed observer. IEEE Transactions on Industry Applications, 48(6), 1950- 1958. https://doi.org/10.1109/TIA.2012.2226194

[15]. Xue, F., Cai, Y., Cao, Y., Cui, Z., & Li, F. (2015). Optimal parameter settings for Bat algorithm. International Journal of Bio-Inspired Computation, 7(2), 125-128. https://doi.org/10.1504/IJBIC.2015.069304

[16]. Yang, X. S. (2013). Bat algorithm: Literature review and applications. International Journal Bio-Inspired Computation, 5(3), 141-149. https://doi.org/10.1504/ IJBIC.2013.055 093

[17]. Yu, K. W., & Hu, S. C. (2006, October). An application of AC servo motor by using Particle Swarm Optimization based sliding mode controller. In 2006 IEEE International Conference on Systems, Man and Cybernetics (Vol. 5, pp. 4146-4150). IEEE. https://doi.org/10.1109/ICSMC. 2006.384784

[18]. Zhang, J., Zhuang, J., & Du, H. (2009). Self-organizing genetic algorithm based tuning of PID controllers. Information Sciences, 179(7), 1007-1018. https://doi.org/ 10.1016/j.ins.2008.11.038

i-manager's Journal on Instrumentation and Control Engineering (JIC)

Current Issue Vol. 12 Issue 1

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Volume 7 Issue 2 February - April 2019

Investigation of Genetic Algorithm Tuned PI Controller for the Non-Linear Conical Tank Level Process

Arivalahan R.* , Balaji S.**, Tamil Arasan P.***

*-***Department of Electrical and Electronics Engineering, SRM Valliammai Engineering College, Chennai, Tamil Nadu, India.

Arivalahan, R., Balaji, S., and Arasan, T. P. (2019). Investigation of Genetic Algorithm Tuned PI Controller for the Non-Linear Conical Tank Level Process.i-manager's Journal on Instrumentation and Control Engineering, 7(2), 1-8. https://doi.org/10.26634/jic.7.2.16725

Abstract

References

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A Study on Effect of Added Contaminants in Lubricants by Using Wear Debris and Vibration Analysis Technique

Vikram * , Vishal Lawande**

*-** Department of Mechanical Engineering, Padmashri Dr. V. V. P. Institution of Technology and Engineering (Polytechnic) Loni, Uttar Pradesh, India.

Yendhe, V., and Lawande, V. (2019). A Study on Effect of Added Contaminants in Lubricants by Using Wear Debris and Vibration Analysis Technique. i-manager's Journal on Instrumentation and Control Engineering, 7(2), 9-17. https://doi.org/10.26634/jic.7.2.15957

Abstract

References

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PD/PID-Fuzzy Logic Controller Based Tracking Control of 2-Link Robot Manipulator

Amit Kumar* , Shrey Kasera**, Lal Bahadur Prasad***

*-*** Department of Electrical Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, Uttar Pradesh, India.

Kumar, A., Kasera, S., and Prasad, L. B. (2019). PD/PID-Fuzzy Logic Controller Based Tracking Control of 2-Link Robot Manipulator. i-manager's Journal on Instrumentation and Control Engineering, 7(2), 18-25. https://doi.org/10.26634/jic.7.2.16658

Abstract

References

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Design and Implementation of Microcontroller based Low Cost Control and Data Acquisition System for Electric Drives

Sandeep Sanjay Kulkarni* , Jayant J. Mane**, Sanjay Baburao Kulkarni***

*-*** Department of Electrical Engineering, Finolex Academy of Management and Technology, Ratnagiri, Maharashtra, India.

Kulkarni, S. S., Mane, J. J., and Kulkarni, S. B. (2019). Design and Implementation of Microcontroller based Low Cost Control and Data Acquisition System for Electric Drives. i-manager's Journal on Instrumentation and Control Engineering, 7(2), 26-31. https://doi.org/10.26634/jic.7.2.16762

Abstract

References

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Implementation of Nature-Inspired Optimization Algorithms for BLDC Motor Drive Control

Manoj Kumar Merugumalla*

Department of Electrical and Electronics Engineering, Tirumala Engineering College, Jonnalagadda, Andhra Pradesh, India.

Merugumalla, M. K. (2019). Implementation of Nature-Inspired Optimization Algorithms for BLDC Motor Drive Control. i-manager's Journal on Instrumentation and Control Engineering, 7(2), 32-41. https://doi.org/10.26634/jic.7.2.16717

Abstract

References

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Arivalahan R.* , Balaji S., Tamil Arasan P.*

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