|Article title||SOFTWARE DEVELOPMENT IN AUTOMATION OF GENERATION PROCESS OF CONTROL RULES IN SYSTEMS WITH FUZZY LOGIC BASED ON DATA OF CLASSICAL PI, PID CONTROLLERS|
|Authors||V. V. Ignatyev, D. A. Beloglazov, V. M. Kureychik, O. B. Spiridonov, A. S. Ignatyeva|
|Section||SECTION IV. MONITORING AND CONTROL IN TECHNICAL SYSTEMS|
|Month, Year||03, 2018 @en|
|Abstract||The paper proposes a method to improve the quality of control of technical objects, the problem of automation of which was solved with the use of the classical control theory and such controllers as PI, PD, PID. The basic idea of the method consists in the gradual replacement of these controllers by their hybrid analogs realized with the use of the fuzzy logic apparatus (fuzzy – PI, fuzzy - PID, etc.). At the first stage, statistical information is collected about the inputs and outputs of the controller used (system error value , error integral θdt, magnitude of the generated control action U). The stage is considered complete when a set of data describing the operation of the controller for all modes of its operation will be created for the automated object. The next step involves the use of the obtained statistical data for synthesizing the base of the control rules of the hybrid controller(s). The solution of this problem is possible in a manual mode, but the process is very labor-consuming, it is associated with a significant risk of errors, manifested in the creation of duplicate or, more critically contradictory, control rules. The identification of these errors may require the developer of great effort and does not guarantee a successful outcome. In this connection, the problem of developing and using specialized software becomes urgent, which was done in this work. As a result, the duration of the developer"s time costs decreased by 98%, and the risk of errors was reduced to zero. For ease of use, the result of applying the proposed software is presented in the form of a *.fis file, which can be opened and analyzed in Fuzzy Logic Toolbox. In the future, the software application under consideration can be transferred from the MATLAB modeling environment to any other programming language.|
|Keywords||Automation; control; hybrid controller; software application; MATLAB; classical model; fuzzy model; fuzzy rule base.|
|References||1. Polkovnikova N.A., Kureychik V.M. Neyrosetevye tekhnologii, nechetkaya klasterizatsiya i geneticheskie algoritmy v ekspertnoy sisteme SBIS [Neural network technologies, fuzzy clustering and genetic algorithms in the expert system], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2014, No. 7 (156), pp. 7-15.
2. Kolesnikov A.V. Gibridnye intellektual'nye sistemy: teoriya i tekhnologiya razrabotki [Hybrid intelligent systems: theory and technology of development]. Saint Petersburg: Izd-vo SPbGTU, 2001, 600 p.
3. Demenkov N.P. Nechetkoe upravlenie v tekhnicheskikh sistemakh: uchebnoe posobie [Fuzzy control in technical systems: a training manual]. Moscow: Izd-vo MGTU im. N.E. Baumana, 2005, 200 p.
4. Leonenkov A.V. Nechetkoe modelirovanie v srede MATLAB i fuzzyTECH [Fuzzy modeling in the MATLAB and fuzzyTECH environment]. Saint Petersburg: BKhV – Peterburg, 2005, 736 p.
5. Shtovba S.D. Proektirovanie nechetkikh sistem sredstvami MATLAB [Designing of fuzzy systems by means of MATLAB]. Moscow: Goryachaya liniya – Telekom, 2007, 288 p.
6. 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 June 2018).
7. 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.
8. 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.
9. 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.
10. 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.
11. Wu Y., Jiang H., Zou M. The Research on Fuzzy PID Control of the Permanent Magnet Linear Synchronous Motor // Physics Procedia. – 2012. – Vol. 24. – P. 1311-1318.
12. 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 p.
13. 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.
14. 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.
15. Ignat'ev V.V., Spiridonov O.B. Gibridnyy algoritm formirovaniya bazy pravil nechetkogo 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), pp. 177-186.
16. Ignat'ev V.V., Kureychik V.M., Spiridonov O.B., Ignat'eva A.S. Metod gibridnogo upravleniya na osnove adaptivnoy sistemy neyronechetkogo vyvoda [Method of hybrid control based on the adaptive system of neuro-fuzzy inference], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2017, No. 9 (194), pp. 124-132.
17. Ignat'ev V.V., Spiridonov O.B., Kureychik V.M., Kovalev A.V., Ignat'eva A.S. Metod gibridnogo upravleniya v intellektual'nykh sistemakh na osnove PID i PID-FUZZY-regulyatorov [Method of hybrid control in intelligent systems based on PID and PID-FUZZY-controllers], Vestnik RGRTU [Vestnik RSRTU], 2017, No. 62, pp. 110-118. ISSN 1995-4565. DOI: 10.21667/1995-4565-2017-62-4-110-118.
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]. Saint 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]. Sait Petersburg: Piter, 2001, 480 p.