Specific Features Pertinent to Identification of Dynamic Objects Using Pulse Testing Sequences
DOI:
https://doi.org/10.24160/1993-6982-2018-3-116-125Keywords:
sequence of rectangular testing pulses, amplitude-frequency response, identification, linear dynamic objectAbstract
The specific features pertinent to identifying linear dynamic objects by using a testing sequence of rectangular pulses are analyzed. It is assumed that the roots of the characteristic equation of the identified object lie in the real negative quadrant of the complex plane. The article analyzes a procedure for estimating the discrete points of the amplitude-frequency responses (AFR) of linear dynamic objects using the amplitude spectrum of a single pulse from a periodic pulse sequence on the pulse period and amplitude spectrum of the signal at the identified object output. The sources of errors in the estimates of the dynamic object’s AFR points are determined. The conditions that take into consideration the object inertia and make it possible to estimate the AFR intermediate points through artificially elongating the observed signal’s period by zero values are found. The advantages and disadvantages of the proposed method for identifying linear objects are outlined. The obtained results make it possible to estimate changes in the positions of the object’s AFR points during its operation in systems with adaptive controllers and in diagnostic systems. In adaptive controllers, the application of one test pulse to an object can replace the generation of several harmonics with certain frequencies. This technique makes it possible to study the position of several check points of the AFR and use this information to diagnose the state of the object and to adjust the parameters in adaptive controllers.
References
2. Rao G.P., Unbehauen H. Identification of Continuous-time Systems // IEEE Control Theory and Appl. 2006. V. 153. No. 2. Pp. 185—220.
3. Штейнберг Ш.Н. Идентификация в системах управления. М.: Энергоатомиздат, 1987.
4. Jouffroy J., Reger J. Finite-time Simultaneous Parameter and State Estimation Using Modulating Functions// IEEE Conf. Control Appl. 2015. Pp. 394—399.
5. Ротач В.Я. Теория автоматического управления. М.: Изд-во МЭИ, 2004.
6. Мазуров В.М., Литюга А.В., Синцын А.В. Развитие технологий адаптивного управления в SCADA системе TRACE MODE // Приборы и системы управления, контроль, диагностика. 2002. № 1. С. 17—22.
7. Frohr F., Orttenburger F. Introduction to Electronic Control Engineering. London: Siemens, 1982.
8. Perlman I. The Electroretinogram: ERG [Электрон. ресурс] http://www.webvision.ERG. (дата обращения 01.06.2017).
9. Marmor F. е. а. SCEV Standard for Full-field Clinical Electroretinography // Doc Ophthalmol. 2009. V. 118. Рp. 69—77.
10. Zueva M., Tsapenko I., Vaskov S. The Components of Human and Rabbit 8-Hz and 12-Hz Flicker Erg as a Function on Intensity, Size and Position of Stimuli // Abstracts of 42nd ISCEV Symp. 2004. P. 132
11 Колосов О.С. и др. Оценка частотных свойств динамического объекта с использованием импульсных тестирующих сигналов // Мехатроника, автоматизация, управление. 2017. № 4. С. 219—226.
12. Попов Е.П. Прикладная теория процессов управления в нелинейных системах. М.: Наука, 1973.
13. Ungarala S., Co T.B. Time-varying System Identification Using Modulating Functions and Spline Models with Application to Bio-processes // Computers and Chemical Eng. 2000. No. 24 (12). Pр. 2739—2753.
14. Анго А. Математика для электро- и радиоинженеров. М.: Наука, 1967.
15. Сергиенко А.Б. Цифровая обработка сигналов. СПб.: БХВ-Петербург, 2013.
16. Применение цифровой обработки сигналов /под ред. А.М. Рязанцева. М.: Мир, 1980.
17. Anisimov D.N. е. а. Diagnosis of the Current State of Dynamic Objects and Systems with Complex Structures by Fuzzy Logic Using Simulation Models // Sci. and Techn. Information Proc. 2013. V. 40. No. 6. Рp. 365—374.
18. Кореневский Н.А., Снопков В.Н., Бурмака А.А., Рябкова Е.Б. Проектирование медицинских интеллектуальных систем поддержки принятия решений на основе нечетких информационных технологий // Врач и информационные технологии. 2013. № 6. C. 48—53.
---
Для цитирования: Колосов О.С., Пронин А.Д. Особенности идентификации динамических объектов импульсными тестирующими последовательностями // Вестник МЭИ. 2018. № 3. С. 116—125. DOI: 10.24160/1993-6982-2018-3-116-125.
#
1. Ginsberg K.S., Basanov D.M. Identifikatsiya i Zadachi Upravleniya. Identifikatsiya Sistem i Zadachi Upravleniya: Plenarnye Doklady IV Mezhdunar. Konf. M.: In-t Problem Upr. im. V.A. Trapeznikova RAN, 2005:56—63. (in Russian).
2. Rao G.P., Unbehauen H. Identification of Conti- nuous-time Systems. IEEE Control Theory and Appl. 2006;153;2:185—220.
3. Shteynberg Sh.N. Identifikatsiya v Sistemakh Upravleniya. M.: Energoatomizdat, 1987. (in Russian).
4. Jouffroy J., Reger J. Finite-time Simultaneous Parameter and State Estimation Using Modulating Functions. IEEE Conf. Control Appl. 2015:394—399.
5. Rotach V.YA. Teoriya Avtomaticheskogo Upravleniya. M.: Izd-vo MPEI, 2004. (in Russian).
6. Mazurov V.M., Lityuga A.V., Sintsyn A.V. Razvitie Tekhnologiy Adaptivnogo Upravleniya v SCADA Sisteme TRACE MODE. Pribory i sistemy Upravleniya, Kontrol', Diagnostika. 2002; 1:17—22. (in Russian).
7. Frohr F., Orttenburger F. Introduction to Electronic Control Engineering. London: Siemens, 1982.
8. Perlman I. The Electroretinogram: ERG [Elektron. Resurs] http://www.webvision.ERG. (Data Obrashcheniya 01.06.2017).
9. Marmor F. е. а. SCEV Standard for Full-field Clinical Electroretinography. Doc Ophthalmol. 2009;118:69—77.
10. Zueva M., Tsapenko I., Vaskov S. The Compo- nents of Human and Rabbit 8-Hz and 12-Hz Flicker Erg as a Function on Intensity, Size and Position of Stimuli. Abstracts of 42nd ISCEV Symp. 2004:132
11 Kolosov O.S. i dr. Otsenka Chastotnykh Svoystv Dinamicheskogo Ob"ekta s Ispol'zovaniem Impul'snykh Testiruyushchikh Signalov. Mekhatronika, Avtomatiza- tsiya, Upravlenie. 2017;4:219—226. (in Russian).
12. Popov E.P. Prikladnaya Teoriya Protsessov Upravleniya v Nelineynykh Sistemakh. M.: Nauka, 1973. (in Russian).
13. Ungarala S., Co T.B. Time-varying System Identification Using Modulating Functions and Spline Models with Application to Bio-processes. Computers and Chemical Eng. 2000;24 (12):2739—2753.
14. Ango A. Matematika dlya Elektro- i Radioinzhe nerov. M.: Nauka, 1967. (in Russian).
15. Sergienko A.B. Tsifrovaya Obrabotka Signalov. SPb.: BKHV-Peterburg, 2013.(in Russian).
16. Primenenie Tsifrovoy Obrabotki Signalov / pod red. A.M. Ryazantseva. M.: Mir, 1980. (in Russian).
17. Anisimov D.N. е. а. Diagnosis of the Current State of Dynamic Objects and Systems with Complex Structures by Fuzzy Logic Using Simulation Models. Sci. and Techn. Information Proc. 2013;40;6:365—374.
18. Korenevskiy N.A., Snopkov V.N., Burmaka A.A., Ryabkova E.B. Proektirovanie Meditsinskikh Intellektual'nykh Sistem Podderzhki Prinyatiya Resheniy na Osnove Nechetkikh Informatsionnykh Tekhnologiy. Vrach i Informatsionnye Tekhnologii. 2013;6:48—53. (in Russian).
---
For citation: Kolosov O.S., Pronin A.D. Specific Features Pertinent to Identification of Dynamic Objects Using Pulse Testing Sequences. MPEI Vestnik. 2018;3:116—125. (in Russian). DOI: 10.24160/1993-6982-2018-3-116-125.
