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IMPLEMENTASI IDENTIFIKASI SISTEM METODE BLACK BOX PADA MOTOR DC MENGGUNAKAN CORRELATION ANALYSIS DAN MODEL ARX

*Fakhruddin Mangkusasmito  -  STr.Teknik Listrik Industri, Sekolah Vokasi, Universitas Diponegoro, Indonesia
Dista Yoel Tadeus  -  STr.Teknik Listrik Industri, Sekolah Vokasi, Universitas Diponegoro, Indonesia
Arkhan Subari orcid  -  STr.Teknik Listrik Industri, Sekolah Vokasi, Universitas Diponegoro, Indonesia

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Abstract
The identification system is a procedure to obtain a model of a system, then of the model will be validated to see the accuracy of the model is obtained, compared to the input-output results obtained from the experiments. In the identification system itself known two methods, the method of "non-parametric" and "parametric". On the other hand, DC motor is a type of motor that uses direct electric current to produce rotational mechanical energy and is widely used in various applications. In this study, input and output data acquisition will be performed on the Feedback brand DC motor, and the data will be processed so that the system model is obtained by the black box method. The data will then be processed with the help of MATLAB software. The input signal used in this experiment is Pseudorandom binary sequence (PRBS), which is used because it has a wide frequency range. The signal is generated by an arduino uno microcontroller. From the test results it was found that the validation of the non parametric system model with the correlation analysis method has a fitness value of = 83.19%, while the validation of the parametric system model with the least square ARX method has a fitness value of = 80.59%.
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Keywords: Identification System, DC Motor; PRBS; correlation analyisis; ARX Model; MATLAB

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  1. L. Ljung, 2010, System identification, in The Control Systems Handbook: Control System Advanced Methods, Second Edition
  2. K. J. Keesman, 2011, System identification: An introduction, in Advanced Textbooks in Control and Signal Processing
  3. K. Ogata, 2002, Modern Control Engineering
  4. Nusantoro, G., Muslim, M., & W., T, 2013, Identifikasi Sistem Plant Suhu dengan Metode Recursive Least Square, Jurnal EECCIS, 6(1), pp. 67-74
  5. M. Ehmer and F. Khan, 2012, A Comparative Study of White Box, Black Box and Grey Box Testing Techniques, Int. J. Adv. Comput. Sci. Appl
  6. P. Potočnik, B. Vidrih, A. Kitanovski, and E. Govekar, 2019, Neural network, ARX, and extreme learning machine models for the short-term prediction of temperature in buildings, Build. Simul
  7. X. Tian, H. Peng, F. Zhou, and X. Peng, 2019, RBF-ARX model-based fast robust MPC approach to an inverted pendulum, ISA Trans
  8. N. J. Gogtay and U. M. Thatte, 2017, Principles of correlation analysis, J. Assoc. Physicians India
  9. B. Mogharbel, L. Fan, and Z. Miao, 2015, Least squares estimation-based synchronous generator parameter estimation using PMU data, in IEEE Power and Energy Society General Meeting
  10. J. Griffin, 1982, DC motors, Electron. Power
  11. A. Kiruthika, A. A. Rajan, and P. Rajalakshmi, 2013, Mathematical modelling and speed control of a sensored brushless DC motor using intelligent controller, in 2013 IEEE International Conference on Emerging Trends in Computing, Communication and Nanotechnology, ICE-CCN 2013
  12. D. L. Gabriel, J. Meyer, and F. Du Plessis, 2011, Brushless DC motor characterisation and selection for a fixed wing UAV, in IEEE AFRICON Conference
  13. H. K. Samitha Ransara and U. K. Madawala, 2013, Modelling and analysis of a low cost Brushless DC motor drive, in Proceedings of the IEEE International Conference on Industrial Technology
  14. S. Rambabu, Modeling And Control Of A Brushless Dc Motor, 207AD
  15. S. Sdudhe and A. G. Thosar, 2014, Mathematical Modelling And Simulation Of Three Phase Bldc Motor Using Matlab/Simulink
  16. M. Nizam, A. Mujianto, H. Triwaloyo, and Inayati, 2013, Modelling on BLDC motor performance using artificial neural network (ANN), in Proceedings of the 2013 Joint International Conference on Rural Information and Communication Technology and Electric-Vehicle Technology, rICT and ICEV-T 2013
  17. I. Colak, M. Sahin, and Z. Esen, 2013, Artificial neural networks controller algorithm developed for a Brushless DC Motor, in Proceedings - 2013 12th International Conference on Machine Learning and Applications, ICMLA 2013
  18. F. Alonge, R. Rabbeni, M. Pucci, and G. Vitale, 2015, Identification and Robust Control of a Quadratic DC/DC Boost Converter by Hammerstein Model, IEEE Trans. Ind. Appl

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