طراحی کنترل کننده ترکیبی فازی-عصبی تطبیقی و تناسبی-انتگرالی-مشتقی برای کاهش ارتعاشات سازه در برابر زلزله

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه برق، موسسه آموزش عالی خراسان، مشهد، ایران

2 گروه برق،موسسه آموزش عالی خراسان، مشهد، ایران

چکیده

در این مقاله یک روش جدید مبتنی بر ترکیب سیستم استنتاج فازی-عصبی تطبیقی و کنترل کننده تناسبی- انتگرالی-مشتقی جهت کاهش ارتعاشات سازه ارائه شده است. الگوریتم کنترلی پیشنهادی علاوه بر دارا بودن ویژگی‌های کنترل‌کننده کلاسیک تناسبی-انتگرالی-مشتقی، از ماهیت تطبیقی شبکه عصبی و استنتاجی منطق فازی جهت استخراج توابع عضویت مناسب با توجه به دامنه ارتعاشات سازه نیز بهره می‌برد. به منظور تنظیم کنترل‌کننده پیشنهادی، و نیز برای شناسایی پارامترهای سازه آزمایشگاهی از الگوریتم بهینه‌سازی نهنگ استفاده شده است. با در نظر گرفتن داده‌های واقعی شتاب زمین مربوط به چهار زلزله مشهور عملکرد کنترل‌کننده پیشنهادی بر روی یک سازه چهار طبقه بررسی شده است، سپس نتایج به دست آمده از شبیه‌سازی با کنترل‌کننده‌های مرسوم از قبیل کنترل‌کننده فازی به تنهایی و روش کنترلی مبتنی بر سیستم استنتاج فازی-عصبی تطبیقی مقایسه شده است. با توجه به نتایج به دست آمده از شبیه‌سازی، روش پیشنهادی دارای عملکرد بهتری نسبت به سایر کنترل‌کننده‌های طراحی شده در کاهش جابه‌جایی و شتاب طبقات می‌باشد. همچنین، نتایج نشان دهنده کاهش بیشینه شتاب لرزش طبقات سازه با استفاده از روش کنترل پیشنهادی نسبت به دو روش متداول کنترل فازی و کنترل استنتاج فازی-عصبی تطبیقی به میزان 2/ 36 برای زلزله اِل سنترو، 4/ 35 برای زلزله نورثریج، 7/ 27 برای زلزله آتن و مقدار 5/ 22 درصد برای زلزله مکزیکوسیتی می‌باشد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Design of a Hybrid Adaptive Neuro-Fuzzy Inference System Proportional–Integral– Derivative Controller for Vibration Mitigation of a Structure against Earthquake

نویسندگان [English]

  • Seyed Mahdi Hadad Baygi 1
  • javad faraji 1
  • Ali Karsaz 2
1 dept. of electrical engineering, Khorasan Institute of Higher Education, Mashhad, Iran
2 dept.of electrical engineering, Khorasan Institute of Higher Education, mashhad, Iran
چکیده [English]

This paper proposes a new hybrid controller based on combining adaptive neuro-fuzzy inference system method and proportional–integral–derivative controller, for vibration mitigation of structural system. The proposed controller although has the proportional–integral–derivative controller features, create a fuzzy inference system that has fewer bugs and errors than neural networks in calculations. The whale optimization algorithm is used for optimum tuning of the proposed method and also for identification of parameters related to the experimental structure. Considering four well-known earthquake real data the performance of the proposed controller is evaluated. Then the results are compared with two other controllers namely, fuzzy logic control and adaptive neuro-fuzzy inference system, which are designed for a four-degree of freedom building. The simulation results show that the proposed controller performs better than other strategies which are developed. The results obtained from the simulation show the better performance of the suggested method than the other control methods in reducing the displacement and acceleration of all floors. The results show that the maximum acceleration related to the building’s floors while using proposed method has improvement of 36.3% for the El Centro, 35.4% for the Northridge, 27.7% for the Athens and 22.5% for the Mexico City earthquakes regarding fuzzy control and adaptive neuro-fuzzy inference system control.

کلیدواژه‌ها [English]

  • Active control of structure
  • fuzzy control
  • ANFIS control
  • whale optimization algorithm
  • Hybrid control system
[1]  J. Yao, Concept of structural control, Journal of the Structural Division, 98(st 7) (1972).
[2]  J.N. Yang, A.K. Agrawal, Semi-active hybrid control systems for nonlinear buildings against near-field earthquakes, Engineering structures, 24(3) (2002) 271-280.
[3]  T. Datta, Control of dynamic response of structures, Emerging Trends in Vibration and Noise Engineering, 1 (1996) 101.
[4]  N. Fisco, H. Adeli, Smart structures: part I—active and semi-active control, Scientia Iranica, 18(3) (2011) 275-284.
[5]  B. Samali, M. Al-Dawod, Performance of a five-storey benchmark model using an active tuned mass damper and a fuzzy controller, Engineering Structures, 25(13) (2003) 1597-1610.
[6]  B. Samali, M. Al-Dawod, K.C. Kwok, F. Naghdy, Active control of cross wind response of 76-story  tall building using a fuzzy controller, Journal of engineering mechanics, 130(4) (2004) 492-498.
[7]  S. Pourzeynali, H. Lavasani, A. Modarayi, Active control of high rise building structures using fuzzy logic and genetic algorithms, Engineering Structures, 29(3) (2007) 346-357.
[8]  L. Huo, G. Song, H. Li, K. Grigoriadis, Robust control design of active structural vibration suppression using an active mass damper, Smart materials and structures, 17(1) (2007) 015021.
[9]  N. Fisco, H. Adeli, Smart structures: part II—hybrid control systems and control strategies, Scientia Iranica, 18(3) (2011) 285-295.
[10]  R. Guclu, H. Yazici, Vibration control of a structure with ATMD against earthquake using fuzzy logic controllers, Journal of Sound and Vibration, 318(1-2) (2008) 36-49.
[11]  Y. Shen, A. Homaifar, D.  Chen,  Vibration  control of flexible structures using fuzzy logic and genetic algorithms, in: Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No. 00CH36334), IEEE, 2000, pp. 448-452.
[12]  W. Jung, W. Jeong, S. Hong, S.-B. Choi, Vibration control of a flexible beam structure using squeeze- mode ER mount, Journal of sound and vibration, 273(1-2) (2004) 185-199.
[13]  R.-F. Fung, Y.-T. Liu, C.-C. Wang, Dynamic model of an electromagnetic actuator for vibration control of a cantilever beam with a tip mass, Journal of Sound and Vibration, 288(4-5) (2005) 957-980.
[14]   R. GÜÇLÜ, Fuzzy logic control of vibrations of analytical multi-degree-of-freedom structural systems, Turkish Journal of Engineering and Environmental Sciences, 27(3) (2003) 157-168.
 [15]     R. Guclu, Sliding mode and PID control of a structural system against earthquake, Mathematical and Computer Modelling, 44(1-2) (2006) 210-217.
[16]   R. Guclu, H. Yazici, Fuzzy logic control of a non- linear structural system against earthquake induced vibration, Journal of Vibration and Control, 13(11) (2007) 1535-1551.
[17]  R. Guclu, H. Yazici, Seismic-vibration mitigation of a nonlinear structural system with an ATMD through a fuzzy PID controller, Nonlinear Dynamics, 58(3) (2009) 553.
[18]   C. Collette, S. Chesne, Robust hybrid mass damper, Journal of Sound and Vibration, 375 (2016) 19-27.
[19]   A.-A. Zamani, S. Tavakoli, S. Etedali, Fractional order PID control  design  for  semi-active  control  of smart base-isolated structures: a multi-objective cuckoo search approach, ISA transactions, 67 (2017) 222-232.
[20] N.Aguirre, F. Ikhouane, J. Rodellar, Proportional-plus- integral semiactive control using magnetorheological dampers, Journal of Sound and Vibration, 330(10) (2011) 2185-2200.
[21]   S. Etedali, M.R. Sohrabi, S. Tavakoli, Optimal PD/ PID control of smart base isolated buildings equipped with piezoelectric friction dampers, Earthquake Engineering and Engineering Vibration, 12(1) (2013) 39-54.
[22]  S. Etedali, M.R. Sohrabi, S. Tavakoli, An independent robust modal PID control approach for seismic control of buildings, Journal homepage: http://www. ojceu. ir/ main, 279 (2013) 291.
[23]  R. Subasri, A. Natarajan, S. Sundaram, W. Jianliang, Neural aided discrete PID active controller for non- linear hysteretic base-isolation building, in: 2013 9th Asian Control Conference (ASCC), IEEE, 2013, pp. 1-8.
[24]   S.M. Nigdeli, Effect of feedback on PID controlled active structures under earthquake excitations, Earthquakes and Structures, 6(2) (2014) 217-235.
[25]   W. Yu, S. Thenozhi, X. Li, Stable Active Vibration Control System for Building Structures using PD/PID Control, IFAC Proceedings Volumes, 47(3) (2014) 4760-4765.
[26]   S. Etedali, S. Tavakoli, M.R. Sohrabi, Design of a decoupled PID controller via MOCS for seismic control of smart structures, Earthquakes and Structures, 10(5) (2016) 1067-1087.
[27]    M. Bozorgvar, S.M. Zahrai, Semi-active seismic control of buildings using MR damper and adaptive neural-fuzzy intelligent controller optimized with genetic algorithm, Journal of Vibration and Control, 25(2) (2019) 273-285.
[28]   M. Braz-César, R. Barros, Optimization of a fuzzy logic controller for MR dampers using an adaptive neuro-fuzzy procedure, International Journal of Structural Stability and Dynamics, 17(05) (2017) 1740007.
[29]  Z.Q. Gu, S.O. Oyadiji, Application of MR damper in structural control using ANFIS method, Computers & Structures, 86(3-5) (2008) 427-436.
[30]   K.C. Schurter, P.N. Roschke, Neuro-fuzzy control of  structures  using  magnetorheological  dampers, in: Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148), IEEE, 2001, pp. 1097-1102.
[31]   K.C. Schurter, P.N. Roschke, Neuro-fuzzy control of structures using acceleration feedback, Smart Materials and Structures, 10(4) (2001) 770-779.
[32]    H. Pang, F. Liu, Z. Xu, Variable universe fuzzy control for vehicle semi-active suspension system with MR damper combining fuzzy neural network and particle swarm optimization, Neurocomputing, 306 (2018) 130-140.
[33]   S.P. HADI, THE DESIGN OF THE HYBRID PID- ANFIS CONTROLLER FOR SPEED CONTROL OF BRUSHLESS DC MOTOR, Journal of Theoretical & Applied Information Technology, 71(3) (2015).
[34]  D. Singh, Passenger body vibration control in active quarter car model using ANFIS based super twisting sliding mode controller, Simulation Modelling Practice and Theory, 89 (2018) 100-118.
[35]  U.A. Shaikh, M.K. AlGhamdi, H.A. AlZaher, Novel product ANFIS-PID hybrid controller for buck converters, The Journal of Engineering, 2018(8) (2018) 730-734.
[36]    A. Kharola, A PID BASED ANFIS & FUZZY CONTROL OF INVERTED PENDULUM ON INCLINED PLANE (IPIP), International Journal on Smart Sensing & Intelligent Systems, 9(2) (2016).
[37]   M.I. AL-Saedi, H. Wu, H. Handroos, ANFIS and fuzzy tuning of PID controller for trajectory tracking of a flexible hydraulically driven parallel robot machine, Journal of automation and control engineering, 1(3) (2013) 70-77.
[38]   D. Singh, Modeling and control of passenger body vibrations in active quarter car system: a hybrid ANFIS PID approach, International Journal of Dynamics and Control, 6(4) (2018) 1649-1662.
[39]  R. Hussain, R. Massoud, M. Al-Mawaldi, ANFIS-PID control FES-supported sit-to-stand in paraplegics:(Simulation study), Journal of Biomedical Science and Engineering, 7(04) (2014) 208.
[40] R. Tomar, M. Qureshi, S. Shrivastava, Development of ANFIS Controller and PID Controller for Seismic Vibration Control of Structural System, International Journal of Advanced Engineering Research and Science, 3(11) (2016).
[41]J.-S. Jang, ANFIS: adaptive-network-based fuzzy inference system, IEEE transactions on systems, man, and cybernetics, 23(3) (1993) 665-685.
[42]M.A. Shoorehdeli, M. Teshnehlab, A.K. Sedigh, M.A. Khanesar, Identification using ANFIS with intelligent hybrid stable learning algorithm approaches and stability analysis of training methods, Applied Soft Computing, 9(2) (2009) 833-850.
[43]J.-S.R. Jang, C.-T. Sun, E. Mizutani, Neuro-fuzzy and soft computing-a computational approach to learning and machine intelligence [Book Review], IEEE Transactions on automatic control, 42(10) (1997) 1482-1484.
[44]S. Mirjalili, A. Lewis, The whale optimization algorithm, Advances in engineering software, 95 (2016) 51-67.
[45]M.L. James, G.M. Smith, J. Wolford, P. Whaley, Vibration of mechanical and structural systems: with microcomputer applications, Harper Collins, 1994.