Study on the Extent of the Impact of Data Set Type on the Performance of ANFIS for Controlling the Speed of DC Motor

Guo Yanling, Mohamed Elhaj Ahmed Mohamed

Abstract


This paper introduces an adaptive neuro-fuzzy inference system (ANFIS) for tracking SEDC motor speed in order to optimize the parameters of the transient speed response by finding out the perfect training data provider for the ANFIS. The controller was adjusted using PI, PD and PIPD to generate data sets to configure the ANFIS rules. The performance of the ANFIS controllers using these the different data sets was investigated. The efficiencies of the three controllers were compared to each other, where the PI, PD, and PIPD configurations were replaced by ANFIS to enhance the dynamic action of the controller. The performance of the proposed configurations was tested under different operating situations. Matlab’s Simulink toolbox was used to implement the designed controllers. The resultant responses proved that the ANFIS based on the PIPD dataset performed better than the ANFIS based on the PI and PD data sets. Moreover, the suggested controller showed a rapid dynamic response and delivered better performance under various operating conditions.

Keywords


adaptive neuro-fuzzy inference system (ANFIS); data sets; FIS and Matlab Simulink; motor control; SEDC motor

Full Text:

PDF

References


Azman, M.A.H., Aris, J.M., Hussain, Z., Samat, A.A.A. & Nazelan, A.M., A Comparative Study of Fuzzy Logic Controller and Artificial Neural Network in Speed Control of Separately Excited DC Motor, 2017 7th IEEE International Conference on Control System, Computing and Engineering (ICCSCE), pp. 336-341, 24-26 Nov., 2017.

Premkumar, K. & Manikandan, B., Bat Algorithm Optimized Fuzzy PD Based Speed Controller for Brushless Direct Current Motor, Engineering Science and Technology, an International Journal, 19(2), pp. 818-840, 2016.

Meshram, P. & Kanojiya, R.G., Tuning of PID Controller Using Ziegler-Nichols Method for Speed Control of DC Motor, Advances in Engineering, International Conference on Science and Management (ICAESM), pp. 117-122, 2012.

Mondal, R., Mukhopadhyay, A. & Basak, D., Embedded System of DC Motor Closed Loop Speed Control Based on 8051 Microcontroller, Procedia Technology, 10, pp. 840-848, 2013.

Al-Mashakbeh, A.S.O., Proportional Integral and Derivative Control of Brushless DC Motor, European Journal of Scientific Research, 35(2), pp. 198-203, 2009.

Basilio, J. & Matos, S., Design of PI and PID Controllers with Transient Performance Specification, IEEE Transactions on Education, 45(4), pp. 364-370, 2002.

Prabu, M.J., Poongodi, P. & Premkumar, K., Fuzzy Supervised Online Coactive Neuro-Fuzzy Inference System-Based Rotor Position Control of Brushless DC Motor, IET Power Electronics, 9(11), pp. 2229-2239, 2016.

Sivarani, T., Jawhar, S.J. & Kumar, C.A., Novel Bacterial Foraging-Based ANFIS for Speed Control of Matrix Converter-Fed Industrial BLDC Motors Operated Under Low Speed and High Torque, Neural Computing and Applications, 29(12), pp. 1411-1434, 2018.

Jang, J. S., ANFIS: Adaptive-Network-Based Fuzzy Inference System, IEEE Transactions on Systems, Man, and Cybernetics, 23(3), pp. 665-685, 1993.

Chaudhary, H., Khatoon, S. & Singh, R., ANFIS Based Speed Control of DC Motor, Second International Innovative Applications of Computational Intelligence on Power, Energy and Controls with Their Impact on Humanity (CIPECH), pp. 63-67, 2016.

Pavankumar, S., Krishnaveni, S., Venugopal, Y. & Babu, Y.K., A Neuro-Fuzzy Based Speed Control of Separately Excited DC Motor, Computational Intelligence and Communication Networks (CICN), International Conference on, pp. 93-98, 2010.

Omar, B., Haikal, A. & Areed, F., Design Adaptive Neuro-Fuzzy Speed Controller for An Electro-Mechanical System, Ain Shams Engineering Journal, 2(2), pp. 99-107, 2011.

Mosavi, M.R., Rahmati, A. & Khoshsaadat, A., Design of Efficient Adaptive Neuro-Fuzzy Controller Based on Supervisory Learning Capable for Speed and Torque Control of BLDC Motor, PRZEGLĄD ELEKTROTECHNICZNY (Electrical Review), R, 88, pp.238-246, 2012.

Zhang, Q.C. & Jiang, M., Adaptive Neuro-Fuzzy Control of BLDCM Based on Back-EMF, Journal of Computer Information Systems, 7(12), pp. 4560-4567, 2011.

Tripura, P. & Babu, Y., Intelligent Speed Control of DC Motor Using ANFIS, Journal of Intelligent & Fuzzy Systems, 26(1), pp. 223-227, 2014.

Premkumar, K. & Manikandan, B., Online Fuzzy Supervised Learning of Radial Basis Function Neural Network Based Speed Controller for Brushless DC Motor, in Power Electronics and Renewable Energy Systems: Springer, pp. 1397-1405, 2015.

Premkumar, K. & Manikandan, B., Speed control of Brushless DC motor Using bat Algorithm Optimized Adaptive Neuro-Fuzzy Inference System, Applied Soft Computing, 32, pp. 403-419, 2015.

Niasar, A., Vahedi, A. & Moghbelli, H., Speed Control of a Brushless DC Motor Drive via Adaptive Neuro-Fuzzy Controller Based on Emotional Learning Algorithm, Proceedings of the Eighth International Conference on Electrical Machines and Systems (ICEMS), 1, pp. 230-234, 2005.

Premkumar, K. & Manikandan, B., Fuzzy PID Supervised Online ANFIS Based Speed Controller for Brushless DC Motor, Neurocomputing, 157, pp. 76-90, 2015.

Mansouri, M., Kaboli, S.H.A., Ahmadian, J. & Selvaraj, J., A Hybrid Neuro-Fuzzy PI Speed Controller for BLDC Enriched with an Integral Steady State Error Eliminator, 2012 IEEE International Conference on Control System, Computing and Engineering (ICCSCE), pp. 234-237, 2012.

Premkumar, K. & Manikandan, B., Adaptive Neuro-Fuzzy Inference System Based Speed Controller for Brushless DC Motor, Neurocomputing, 138, pp. 260-270, 2014.

Khoshnevisan, B., Rafiee, S., Omid, M. & Mousazadeh, H., Development of an Intelligent System Based on ANFIS for Predicting Wheat Grain Yield on the Basis of Energy Inputs, Information Processing in Agriculture, 1(1), pp. 14-22, 2014.

Shieh, C.S., Fuzzy PWM Based on Genetic Algorithm For Battery Charging, Applied Soft Computing, 21, pp. 607-616, 2014.

Premkumar, K. & Manikandan, B., GA-PSO Optimized Online ANFIS Based Speed Controller for Brushless DC Motor, Journal of Intelligent & Fuzzy Systems, 28(6), pp. 2839-2850, 2015.

Ginarsa, I., Soeprijanto, A. & Purnomo, M., Controlling Chaos and Voltage Collapse Using an ANFIS-Based Composite Controller-Static Var Compensator In Power Systems, International Journal of Electrical Power & Energy Systems, 46, pp. 79-88, 2013.

Al-Mashhadany, Y.I., Modeling And Simulation of Adaptive Neuro-Fuzzy Controller for Chopper-Fed Dc Motor Drive, 2011 IEEE Applied Power Electronics Colloquium (IAPEC), pp. 110-115, 2011.

Premkumar, K. & Manikandan, B., Stability and Performance Analysis of ANFIS Tuned PID Based Speed Controller for Brushless DC Motor, Current Signal Transduction Therapy, 13(1), pp. 19-30, 2018.

Premkumar, K., Manikandan, B.V. & Kumar, C.A., Antlion Algorithm Optimized Fuzzy PID Supervised On-line Recurrent Fuzzy Neural Network Based Controller for Brushless DC Motor, Electric Power Components and Systems, 45(20), pp. 2304-2317, 2017.




DOI: http://dx.doi.org/10.5614%2Fj.eng.technol.sci.2019.51.1.6

Refbacks

  • There are currently no refbacks.