|Article title||METHOD OF HYBRID CONTROL BASED ON THE ADAPTIVE SYSTEM OF NEURO-FUZZY INFERENCE|
|Authors||V. V. Ignatyev, V. M. Kureychik, O. B. Spiridonov, A. S. Ignatyeva|
|Section||SECTION II. DESIGNING MANAGEMENT INFORMATION AND AUTOMATED SYSTEMS|
|Month, Year||09, 2017 @en|
|Abstract||The key purpose of the work is development of a method of control that allows simplifying, automating and unifying the process of design of the hybrid systems which are a basis of modern automation. To achieve a definite purpose, a method of control of a technical object based on the construction of an adaptive system of neuro-fuzzy inference is developed. The objects of the system of neuro-fuzzy inference are the classical and fuzzy models of control. Information exchange between models is provided by means of the developed hybrid control system. The result of the interaction of the two models is the automatic formation of the base of fuzzy controller rules based on knowledge about the control object obtained with its control using the classical controller. In the developed adaptive system of neuro-fuzzy inference signals of error and control in the classical model are used as data for creation a hybrid network. Signals of error and control in a fuzzy model with automatically generated fuzzy inference rules are used as data to verify the created hybrid network in order to detect the fact of its retraining. Thus, during the control of a technical object by means of a hybrid system, the knowledge of an expert in the subject area for adjusting the parameters of the fuzzy controller are completely eliminated, that allows to control difficultly formalizable objects in conditions of uncertainty. To obtain reliable research results, a hybrid control system, consisting of classical and fuzzy models is developed. Numerical values of the error and control signals are obtained at discrete timepoints as a result of the interaction of the two models. Special files for creating and testing a hybrid network in the form of numerical matrixes are generated. The hybrid network is developed in the ANFIS editor of the MATLAB package. The generated structure of the FIS fuzzy inference system of the Sugeno type is graphically shown. The visualization of the dependence of training and verification errors from the number of training cycles is given. The surface of the fuzzy inference system is constructed, that allows estimating the dependence of the output variable on the input.|
|Keywords||Automation, control; hybrid network; classical model; fuzzy model; base of fuzzy rules; adaptation; neuro-fuzzy inference; training.|
|References||1. Kolesnikov A.V. Gibridnye intellektual'nye sistemy: teoriya i tekhnologiya razrabotki [Hybrid intelligent systems: theory and technology of development]. St. Petersburg: Izd-vo SPbGTU, 2001, 600 p.
2. Demenkov N.P. Nechetkoe upravlenie v tekhnicheskikh sistemakh: ucheb. posobie [Fuzzy control in technical systems: a training manual]. Moscow: Izd-vo MGTU im. N.E. Baumana, 2005, 200 p.
3. Leonenkov A.V. Nechetkoe modelirovanie v srede MATLAB i fuzzyTECH [Fuzzy modeling in the MATLAB and fuzzyTECH environment]. St. Petersburg: BKhV – Peterburg, 2005, 736 p.
4. Thanana Nuchkrua, Thananchai Leephakpreeda. Fuzzy Self-Tuning PID Control of Hydrogen-Driven Pneumatic Artificial Muscle Actuator, Journal of Bionic Engineering, 2013,
Vol. 10, pp. 329-340.
5. SHI Dequan, GAO Guili, GAO Zhiwei, XIAO Peng. Application of expert fuzzy pid method for temperature control of heating furnace, Procedia Engineering, 2012, Vol. 29, pp. 257-261.
6. Zhiqiang Yang, Jimin Zhang, Zhongchao Chen, Baoan Zhang. Semi-active control of high-speed trains based on fuzzy PID control, Procedia Engineering, 2011, Vol. 15, pp. 521-525.
7. Mann G.K.I., Gosine R.G. Three-dimensional min–max-gravity based fuzzy PID inference analysis and tuning, Fuzzy Sets and Systems, 2005, Vol. 156, pp. 300-323.
8. Wu Y., Jiang H., Zou M. The Research on Fuzzy PID Control of the Permanent Magnet Linear Synchronous Motor, Physics Procedia, 2012, Vol. 24, pp. 1311-1318.
9. Abbasi E., Mahjoob M. J., Yazdanpanah R. Controlling of Quadrotor UAV Using a Fuzzy System for Tuning the PID Gains in Hovering Mode, Fourth International Conference on Advances in Computer Engineering – ACE 2013. – Frankfurt, Germany, 2013. Int. j. adv. robot. syst, 2013, Vol. 10, 380:2013.
10. Kai Ou, Ya-Xiong Wang, Zhen-Zhe Li, Yun-De Shen, Dong-Ji Xuan. Feedforward fuzzy-PID control for air flow regulation of PEM fuel cell system, International journal of hydrogen energy, 21 September 2015, Vol. 40, Issue 35, pp. 11686-11695.
11. Ahmet Karli, Vasfi Emre Omurlu, Utku Buyuksahin, Remzi Artar, Ender Ortak. Self tuning fuzzy PD application on TI TMS320F 28335 for an experimental stationary quadrotor. Available at: http://ieeexplore.ieee.org/document/6151404/ (accessed 23 April 2017).
12. Hamed Beirami, Ali Zargar Shabestari, Mohammad Mahdi Zerafat. Optimal PID plus fuzzy controller design for a PEM fuel cell air feed system using the self-adaptive differential evolution algorithm, International journal of hydrogen energy, 10 August 2015, Vol. 40, Issue 30, pp. 9422-9434.
13. Jahedi G., Ardehali M.M. Genetic algorithm-based fuzzy-PID control methodologies for enhancement of energy efficiency of a dynamic energy system, Energy Conversion and Management, 2011, Vol. 52, pp. 725-732.
14. Ignat'ev V.V., Spiridonov O.B. Gibridnyy algoritm formirovaniya bazy pravil ne-chetkogo regulyatora [Hybrid algorithm of formation of base of rules of the fuzzy controller], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2015, No. 11 (172),
15. Ignat'ev V.V. Sintez sistem gibridnogo upravleniya na osnove ob"edineniya klassicheskoy i nechetkoy modeley ob"ekta [Synthesis of hybrid control systems based on combining classical and fuzzy object models], Materiály VIII mezinárodní vědecko – praktická konference «Dny vědy – 2012». – Díl 94. Technické vědy: Praha. Publishing House «Education and Science» s.r.o – 88 stran, pp. 54-57.
16. Ignatyev V.V. Fuzzy control system in an automatic and automated production [Fuzzy control system in an automatic and automated production], Materiali za 9-a mezhdunarodna nauchna praktichna konferentsiya, «Klyuchovi v"prosi v s"vremennata nauka», 2013, Vol. 36. Tekhnologii. Sofiya. «Byal GRAD-BG» OOD, pp. 41-43.
17. Ignatyev V.V., Finaev V.I. The use of hybrid regulator in design of control systems, World Applied Sciences Journal, 2013, Vol. 23 (10), pp. 1291-1297. ISSN 1818-4952 © IDOSI Publications, 2013. DOI: 10.5829/idosi.wasj.2013.23.10.13144.
18. Kruglov V.V., Dli M.I., Golubov R.Yu. Nechetkaya logika i iskusstvennye neyronnye seti [Fuzzy logic and artificial neural networks]. Moscow, 2004, 224 p.
19. D'yakonov V. MATLAB: uchebnyy kurs [MATLAB: training course]. St. Petersburg: Piter, 2001, 560 p.
20. D'yakonov V., Kruglov V. Matematicheskie pakety rasshireniya MATLAB. Spetsial'nyy spravochnik [Mathematical expansion packages MATLAB. Special reference book]. St. Petersburg: Piter, 2001, 480 p.