Skip to main content

Advertisement

Log in

Fuzzy force learning controller of flexible wiper system

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Wiper blade of automobile is among those types of flexible system that is required to be operated in quite high velocity to be efficient in high load conditions. This causes some annoying noise and deteriorated vision for occupants. The modeling and control of vibration and low-frequency noise of an automobile wiper blade using soft computing techniques are focused in this study. The flexible vibration and noise model of wiper system are estimated using artificial intelligence system identification approach. A PD-type fuzzy logic controller and a PI-type fuzzy logic controller are combined in cascade with active force control (AFC)-based iterative learning (IL). A multi-objective genetic algorithm is also used to determine the scaling factors of the inputs and outputs of the PID-FLC as well as AFC-based IL gains. The results from the proposed controller namely fuzzy force learning (FFL) are compared with those of a conventional lead–lag-type controller and the wiper bang–bang input. Designing controllers based on classical methods could become tedious, especially for systems with high-order model. In contrast, FFL controller design requires only tuning of some scaling factors in the control loop and hence is much simpler and efficient than classical design methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Arimoto S, Kawamura S, Miyazaki F (1984) Bettering operation of robots by learning. J Robot Syst 1:123–140

    Article  Google Scholar 

  2. Ashish S (2013) Vibration suppression of a cart-flexible pole system using a hybrid controller, proceedings of the 1st international and 16th national conference on machines and mechanisms, IIT Roorkee, India, Dec 18–20

  3. Awang IM, AbuBakar AR, Ghani BA, Rahman RA, Zain MZM (2009) Complex eigenvalue analysis of windscreen wiper chatter noise and its suppression by structural modifications. Int J Veh Struct Syst 1(1–3):24–29

    Google Scholar 

  4. Bristow DA, Tharayil M, Alleyne AG (2006) A survey of iterative learning control. IEEE Control Syst Mag 26:96–114

    Article  Google Scholar 

  5. Chaiyatham T, Ngamroo I (2012) A bee colony optimization based-fuzzy logic-PID control design of electrolyzer for microgrid stabilization. Int J Innov Comput Inform Control 8(9):6049–6066

    Google Scholar 

  6. Chatterjee A, Pulasinghe K, Watanabe K, Izumi K (2005) A particle swarm optimized fuzzy—neural network for voice-controlled robot systems. IEEE Trans Ind Electron 52(6):1478–1489

    Article  Google Scholar 

  7. Chen HC (2008) Optimal fuzzy PID controller design of an active magnetic bearing system based on adaptive genetic algorithms, In: the proceedings of international conference on machine learning and cybernetics pp 2054–2060

  8. Chen S, Billings SA (1989) Representations of non-linear systems: the NARMAX model. Int J Control 49:1013–1032

    Article  MathSciNet  MATH  Google Scholar 

  9. Elman J (1990) Finding structure in time. J. Cognit Sci 14:179–211

    Article  Google Scholar 

  10. Escamilla-Ambrosio PJ, Mort N (2002) A novel design and tuning procedure for PID type fuzzy logic controllers, In: Proceedings of first international IEEE symposium intelligent systems pp 36–41

  11. Hewit JR, Burdess JS (1981) Fast dynamic decoupled control for robotics using active force control. Mech Mach Theory 16(5):535–542

    Article  Google Scholar 

  12. Hung JY (1995) Magnetic bearing control using fuzzy logic. IEEE Trans Ind Appl 31:1492–1497

    Article  Google Scholar 

  13. Kumar R, Khan M (2007) Pole placement techniques for active vibration control of smart structures: a feasibility study. J Vib Acoust 125(5):601–615

    Article  Google Scholar 

  14. Li HX, Gatland HB (1996) Conventional fuzzy control and its enhancement. IEEE Trans Syst Man and Cybern Part B Cyber 26:791–797

    Article  Google Scholar 

  15. Mann GKI, Hu BG, Gosine RG (1999) Analysis of direct action fuzzy PID controller structures. IEEE Trans Syst Man Cyber Part B Cybern 29:371–388

    Article  Google Scholar 

  16. Noshadi A, Mailah M, Zolfagharian A (2010) Active force control of 3-RRR planar parallel manipulator, IEEE international conference on mechanical and electrical technology, Singapore

  17. Noshadi A, Mailah M (2012) Active disturbance rejection control of a parallel manipulator with self learning algorithm for a pulsating trajectory tracking task. Sci Iran 19(1):132–141

    Article  Google Scholar 

  18. Noshadi A, Mailah M, Zolfagharian A (2012) Intelligent active force control of a 3-RRR parallel manipulator incorporating fuzzy resolved acceleration control. Appl Math Model 36(6):2370–2383

    Article  MathSciNet  MATH  Google Scholar 

  19. Noshadi A, Shi J, Lee WS, Shi P, Kalam A (2014) Genetic algorithm-based system identification of active magnetic bearing system: a frequency-domain approach. In: Proceedings of international conference of control and automation (ICCA2014), Taiwan, pp 1281–1286

  20. Prakash R, Anita R (2012) Modeling and simulation of fuzzy logic controller-based model reference adaptive controller. Int J Innov Comput Inform Control 8(4):2533–2550

    Google Scholar 

  21. Sahinkaya MN (2001) Input shaping for vibration-free positioning of exible systems. Proc Instn Mech Eng Part I IMechE 215:467–481

    Google Scholar 

  22. Shaheed MH, Tokhi MO (2002) Dynamic modelling of a single-link of a flexible manipulator: parametric and non-parametric approaches. J Robot 20:93–109

    Google Scholar 

  23. Silva VVR, Fleming PJ, Sugimoto J, Yokoyama R (2008) Multiobjective optimization using variable complexity modelling for control system design. Appl Soft Comput 8:392–401

    Article  Google Scholar 

  24. Srinivasan B, Prasad UR, Rao NJ (1994) Backpropagation through adjoins for the identification of non-linear dynamic systems using recurrent neural models. IEEE Trans Neural Netw 5(2):213–228

    Article  Google Scholar 

  25. Tehrani MG, Mottershead JE, Shenton AT, Ram YM (2011) Robust pole placement in structures by the method of receptances. Mech Syst Signal Process 25(1):112–122

    Article  Google Scholar 

  26. Tokhi MO, Zain MZM (2006) Hybrid learning control schemes with acceleration feedback of a flexible manipulator system. Proc Inst Mech Eng Part I J Syst Control Eng 220(4):257–267

    Article  Google Scholar 

  27. Wang Z, Chau KT (2009) Control of chaotic vibration in automotive wiper systems. Chaos Soliton Fract 39:168–181

    Article  MATH  Google Scholar 

  28. Warwick JK, Kang YH, Mitchell RJ (1999) Genetic least squares for system identification. Soft Comput 3:200–205

    Article  Google Scholar 

  29. Yanyan W, Jiana W, Zhifu Z (2011) Design of intelligent infrared windscreen wiper based on MCU. Proced Eng 15:2484–2488

    Article  Google Scholar 

  30. Zain MZM, Tokhi MO, Mohamed Z (2006) Hybrid learning control schemes with input shaping of a flexible manipulator system. Mechatronics 16:209–219

    Article  Google Scholar 

  31. Zhang T, Li HG (2012) Adaptive pole placement control for vibration control of a smart cantilevered beam in thermal environment. J Vib Control 18(12):1–11

    Google Scholar 

  32. Zitzler E, Thiele L (1999) Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans Evol Comput 3(4):257–271

    Article  Google Scholar 

  33. Zolfagharian A, Noshadi A, Khosravani MR, Zain M (2014) Unwanted noise and vibration control using finite element analysis and artificial intelligence. Appl Math Model 38:2435–2453

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to S. E. Ghasemi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zolfagharian, A., Valipour, P. & Ghasemi, S.E. Fuzzy force learning controller of flexible wiper system. Neural Comput & Applic 27, 483–493 (2016). https://doi.org/10.1007/s00521-015-1869-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-015-1869-0

Keywords

Navigation