https://oldena.lpnu.ua/handle/ntb/55949| Title: | Neurocontrolled object parameters adjustment by Ackermann’s formula usage |
| Authors: | Nakonechnyi, Markiyan Ivakhiv, Orest Viter, Oleksandr Nakonechnyi, Yuriy |
| Affiliation: | Lviv Polytechnic National University |
| Bibliographic description (Ukraine): | Neurocontrolled object parameters adjustment by Ackermann’s formula usage / Markiyan Nakonechnyi, Orest Ivakhiv, Oleksandr Viter, Yuriy Nakonechnyi // Measuring equipment and metrology. — Lviv : Lviv Politechnic Publishing House, 2020. — Vol 81. — No 1. — P. 22–29. |
| Bibliographic description (International): | Neurocontrolled object parameters adjustment by Ackermann’s formula usage / Markiyan Nakonechnyi, Orest Ivakhiv, Oleksandr Viter, Yuriy Nakonechnyi // Measuring equipment and metrology. — Lviv : Lviv Politechnic Publishing House, 2020. — Vol 81. — No 1. — P. 22–29. |
| Is part of: | Measuring equipment and metrology, 1 (81), 2020 |
| Issue: | 1 |
| Issue Date: | 24-Feb-2020 |
| Publisher: | Видавництво Львівської політехніки Lviv Politechnic Publishing House |
| Place of the edition/event: | Львів Lviv |
| DOI: | doi.org/10.23939/istcmtm2020.01.022 |
| Keywords: | Mechatronics Robotics System Synthesis Dynamics Ackermann's formula Neural network |
| Number of pages: | 8 |
| Page range: | 22-29 |
| Start page: | 22 |
| End page: | 29 |
| Abstract: | Synthesis methods of controllers based on the use of frequency characteristics or root hodographs are considered classic or traditional. Frequency methods are available in practical applications, and most control systems are designed based on various modifications to these methods. A distinctive feature of these methods is the so-called robustness, which means that the characteristics of a closed system are insensitive to the minor errors of the model of the real system. This feature is significant because of the complexity of constructing an accurate model of the real system, as well as the fact that many systems are inherent in all kinds of nonlinearities, which complicate their analysis and synthesis. In recent years, many attempts have been made to develop new methods of synthesis, commonly referred to as modern control theory. One synthesis method is like the root hodograph method, which allows positioning the poles of the closed-loop transfer function at predetermined points. In the article on the basis of information about the desired transient characteristic of the reference, which is obtained on the basis of a dynamic neural network, using the Ackerman formula, a procedure for calculating the coefficient matrix, whose introduction in the structure of the object model provides the specified dynamics of the process. On the base of the reference mathematical model is created the architecture of the corresponding dynamic neural network. During training, there is the target function as a numerical sequence that corresponds to the desired transient characteristic of the system, and the input signal is given in the form of a numerical sequence that reproduces jump function. Using the values of the weight coefficients obtained in the course of learning the neural network, the coefficients of the mathematical model of the reference and the roots of its characteristic equation are calculated, with the following calculation using the Ackerman formula of the coefficients of the matrix, whose values are entered into the structure of the model ensuring the specified dynamics of the process in it. |
| URI: | https://ena.lpnu.ua/handle/ntb/55949 |
| Copyright owner: | © Національний університет “Львівська політехніка”, 2020 |
| References (Ukraine): | [1] M. Shahinpoor, A Robot Engineering Textbook. New Mexico: University of New Mexico Press, Albuquerque, 1984. [2] K. S. Fu, R. C. Gonzalez, C. S. G. Lee, Robotics: Control, Sensing, Vision and Intelligence. McGraw-Hill Education (India) Pvt Limited, 1988. [3] G. Galkin, Matlab & Simulink. Designing of mechatronic systems on a PC. St. Petersburg: Korona-Vek, 2008 (in Russian). [4] G. C. Goodwin, S. F. Graebe, Mario E. Salgado, Control System Design. Upper Saddle River, New Jersey: Prentice Hall, 2001. [5] M. H. Popovich, O. V. Kovalchuk, The theory of automatic control. Textbook. Kyiv: Lybid, 2007 (in Ukrainian). [6] R. C. Dorf, R. H. Bishop, Modern Control Systems, 13th Edition, Pearson Education, 2017. [7] S. Haykin, Neural Network. A Comprehensive Foundation. New York: Macmillan College Publishing Company, 1994. [8] C. L. Phillips, R. D. Harbor. Feedback Control Systems. Upper Saddle River, New Jersey: Prentice Hall, 2000. [9] S. Osowski, Sieci neuronowe do przetwarzania informacji. Warszawa: Oficyna Wydawnicza Politechniki Warszawskiej, 2000. [10] M. A. Novotarskiy, B. B. Nesterenko, “Artificial neural networks: calculations”. Proceedings of the Institute of Mathematics of NAS of Ukraine, vol, 50, Kyiv: Naukova Dumka, 2004 (in Ukrainian). [11] E. V. Bodianskyi, Artificial neural networks: architectures, training, applications. E. V. Bodianskyi and O. G. Rudenko, Eds. Kharkiv: Telemekh, 2004 (in Ukrainian). [12] D. Rutkowska, M. Pilinski, L. Rutkowski, Sieci neuronowe, algorytmy genetyczne i systemy rozmyte. Warszawa - Lodz: Wydawnictwo Naukowe PWN, 2004. [13] O. G. Rudenko, Artificial neural networks: Textbook. O. G. Rudenko and E. V. Bodianskyi, Eds. Kharkiv: SMIT Company LLC, 2006 (in Ukrainian). [14] H. Al-Duwaish, S. Z. Rizvi, “Design of a NeuroController for Multivariable Nonlinear Time-Varying Systems” in WSEAS Transactions on Systems and Control, vol. 5, Issue 9, pp. 711–720, 2010. [15] Y. M. Rashkevich, R. O. Tkachenko, I. H. Tsmots, D. D. Peleshko, Neural-like methods, algorithms, and structures for real-time signal and image processing. Lviv: Lviv Polytechnic Publishing House, 2014 (in Ukrainian). [16] J. E. Ackermann, Der Entwurf Linear Regelungs Systems in Zustandstraum, Regelungstech Process, Datenverarb, 7, 1972. [17] M. Dorozhovets, O. Ivakhiv, V. Mokritskyi, Uniform Converters of Information Support for Mechatronic Systems. Lviv: Publishing House of Lviv Polytechnic National University, 2009 (in Ukrainian). [18] J. Su, M. Nakonechnyi, O. Ivakhiv, A. Sachenko, “Developing the Automatic Control System Based on Neural Controller”, Information Technology and Control, vol. 44, no. 3, pp. 262–270, 2015. [19] M. Nakonechnyi, O. Ivakhiv, Y. Nakonechnyi, Neural Network Control Systems for Nonlinear Objects: Monograph. Lviv: Raster-7, Publishing House, 2017 (in Ukrainian). [20] J. B. Dabney, T. L. Harmen, Mastering Simulink 4. Upper Saddle River. New Jersey: Prentice Hall, 2001. [21] O. Katsuhiko, Modern Control Engineering, 5th edition, Published by Pearson, 2010. |
| References (International): | [1] M. Shahinpoor, A Robot Engineering Textbook. New Mexico: University of New Mexico Press, Albuquerque, 1984. [2] K. S. Fu, R. C. Gonzalez, C. S. G. Lee, Robotics: Control, Sensing, Vision and Intelligence. McGraw-Hill Education (India) Pvt Limited, 1988. [3] G. Galkin, Matlab & Simulink. Designing of mechatronic systems on a PC. St. Petersburg: Korona-Vek, 2008 (in Russian). [4] G. C. Goodwin, S. F. Graebe, Mario E. Salgado, Control System Design. Upper Saddle River, New Jersey: Prentice Hall, 2001. [5] M. H. Popovich, O. V. Kovalchuk, The theory of automatic control. Textbook. Kyiv: Lybid, 2007 (in Ukrainian). [6] R. C. Dorf, R. H. Bishop, Modern Control Systems, 13th Edition, Pearson Education, 2017. [7] S. Haykin, Neural Network. A Comprehensive Foundation. New York: Macmillan College Publishing Company, 1994. [8] C. L. Phillips, R. D. Harbor. Feedback Control Systems. Upper Saddle River, New Jersey: Prentice Hall, 2000. [9] S. Osowski, Sieci neuronowe do przetwarzania informacji. Warszawa: Oficyna Wydawnicza Politechniki Warszawskiej, 2000. [10] M. A. Novotarskiy, B. B. Nesterenko, "Artificial neural networks: calculations". Proceedings of the Institute of Mathematics of NAS of Ukraine, vol, 50, Kyiv: Naukova Dumka, 2004 (in Ukrainian). [11] E. V. Bodianskyi, Artificial neural networks: architectures, training, applications. E. V. Bodianskyi and O. G. Rudenko, Eds. Kharkiv: Telemekh, 2004 (in Ukrainian). [12] D. Rutkowska, M. Pilinski, L. Rutkowski, Sieci neuronowe, algorytmy genetyczne i systemy rozmyte. Warszawa - Lodz: Wydawnictwo Naukowe PWN, 2004. [13] O. G. Rudenko, Artificial neural networks: Textbook. O. G. Rudenko and E. V. Bodianskyi, Eds. Kharkiv: SMIT Company LLC, 2006 (in Ukrainian). [14] H. Al-Duwaish, S. Z. Rizvi, "Design of a NeuroController for Multivariable Nonlinear Time-Varying Systems" in WSEAS Transactions on Systems and Control, vol. 5, Issue 9, pp. 711–720, 2010. [15] Y. M. Rashkevich, R. O. Tkachenko, I. H. Tsmots, D. D. Peleshko, Neural-like methods, algorithms, and structures for real-time signal and image processing. Lviv: Lviv Polytechnic Publishing House, 2014 (in Ukrainian). [16] J. E. Ackermann, Der Entwurf Linear Regelungs Systems in Zustandstraum, Regelungstech Process, Datenverarb, 7, 1972. [17] M. Dorozhovets, O. Ivakhiv, V. Mokritskyi, Uniform Converters of Information Support for Mechatronic Systems. Lviv: Publishing House of Lviv Polytechnic National University, 2009 (in Ukrainian). [18] J. Su, M. Nakonechnyi, O. Ivakhiv, A. Sachenko, "Developing the Automatic Control System Based on Neural Controller", Information Technology and Control, vol. 44, no. 3, pp. 262–270, 2015. [19] M. Nakonechnyi, O. Ivakhiv, Y. Nakonechnyi, Neural Network Control Systems for Nonlinear Objects: Monograph. Lviv: Raster-7, Publishing House, 2017 (in Ukrainian). [20] J. B. Dabney, T. L. Harmen, Mastering Simulink 4. Upper Saddle River. New Jersey: Prentice Hall, 2001. [21] O. Katsuhiko, Modern Control Engineering, 5th edition, Published by Pearson, 2010. |
| Content type: | Article |
| Appears in Collections: | Вимірювальна техніка та метрологія. – 2020. – Випуск 81, №1 |
| File | Description | Size | Format | |
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| 2020v81n1_Nakonechnyi_M-Neurocontrolled_object_22-29.pdf | 358.69 kB | Adobe PDF | View/Open | |
| 2020v81n1_Nakonechnyi_M-Neurocontrolled_object_22-29__COVER.png | 1.46 MB | image/png | View/Open |
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