Skip to main content
Log in

Meta-Heuristic Tuning of the LQR Weighting Matrices Using Various Objective Functions on an Experimental Flexible Arm Under the Effects of Disturbance

  • Research Article-Mechanical Engineering
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

In this paper, meta-heuristic tuning of LQR weighting matrices using various objective functions on the experimental flexible arm under the effects of disturbance is investigated. The use of flexible and lightweight systems provides certain advantages such as high operating speeds, low electricity consumption, and low initial investment costs. However, such systems are more prone to vibration-related problems than their rigid and heavyweight counterparts. One of the closed-loop control methods used to suppress these vibrations is LQR control, but the success of the controller depends on the choice of the gain and regulator matrices. In this study, the Bees algorithm, a meta-heuristic search algorithm, is used to determine LQR matrices. The system performance is compared with similar studies in the literature. The proposed objective function reveals an improvement of 3.1% in rotor maximum overshoot compared to studies in the literature. Flexible link maximum overshoot has been reduced from 17.8 to 7.1%. In order to validate the applicability and repeatability of the proposed method under the noise and disturbance in real systems, experimental verification tests were conducted using the Quanser Flexible Link system. As a result, the proposed method has been experimentally validated and it is estimated that it may well be a very useful controller design approach in various engineering systems and related control system developments.

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.

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

Similar content being viewed by others

References

  1. Sehgal, S.,: Kumar, H.: Damage and damping ıdentification in a structure through novel damped updating method. Iran. J. Sci. Technol. Trans. Civ. Eng. (2020)

  2. Sehgal, S.; Kumar, H.: Experimental damage identification by applying structural dynamic model updating. J. Theor. Appl. Mech. 49(1), 51–61 (2019)

    Article  Google Scholar 

  3. Mehrjooee, O.; Fathollahi Dehkordi, S.; Habibnejad Korayem, M.: Dynamic modeling and extended bifurcation analysis of flexible-link manipulator. Mech. Based Des. Struct. Mach. pp. 1–24. (2019)

  4. Alkalla, M.G.: Fanni, M.A.: Integrated structure/control design of high-speed flexible robot arms using topology optimization. Mech. Based Des. Struct. Mach. pp. 1–22. (2019)

  5. Zhou, Y.; Jiang, G.; Zhang, C.; Wang, Z.; Zhang, Z.; Liu, H.: Modeling of a joint-type flexible endoscope based on elastic deformation and internal friction. Adv. Robot. 33(19), 985–995 (2019)

    Article  Google Scholar 

  6. Jayaweera, N.; Webb, P.: Metrology-assisted robotic processing of aerospace applications. Int. J. Comput. Integr. Manuf. 23(3), 283–296 (2010)

    Article  Google Scholar 

  7. Yavuz, H.; Mıstıkoğlu, S.; Kapucu, S.: Hybrid input shaping to suppress residual vibration of flexible systems. J. Vib. Control 18(1), 132–140 (2012)

    Article  MathSciNet  Google Scholar 

  8. Bilgic, H.H.; Conker, C.; Yavuz, H.: Fuzzy logic–based decision support system for selection of optimum input shaping techniques in point-to-point motion systems. Proc. Inst. Mech. Eng. Part I J. Syst. Control Eng. (2020)

  9. IEA (20 May 2018). https://www.iea.org/newsroom/news/2011/may/2011-05-18-.html

  10. Conker, C.; Yavuz, H.; Bilgic, H.H.: A review of command shaping techniques for elimination of residual vibrations in flexible-joint manipulators. J. Vibroeng. 18(5), 2947–2958 (2016)

    Article  Google Scholar 

  11. Kjelland, M.B.; Hansen, M.R.: Using input shaping and pressure feedback to suppress oscillations in slewing motion of lightweight flexible hydraulic crane. Int. J. Fluid Power 16(3), 141–148 (2015)

    Article  Google Scholar 

  12. Mansour, T.; Konno, A.; Uchiyama, M.: Modified PID control of a single-link flexible robot. Adv. Robot. 22(4), 433–449 (2008)

    Article  Google Scholar 

  13. Guo, Y.; Ma, B.L.: Global sliding mode with fractional operators and application to control robot manipulators. Int. J. Control 92(7), 1497–1510 (2019)

    Article  MathSciNet  Google Scholar 

  14. Lizarraga, I.; Etxebarria, V.: Combined PD-H∞ approach to control of flexible link manipulators using only directly measurable variables. Cybern. Syst. 34(1), 19–31 (2003)

    Article  Google Scholar 

  15. Yang, Y.L.; Wei, Y.D.; Lou, J.Q.; Fu, L.; Fang, S.; Chen, T.H.: Dynamic modeling and adaptive vibration suppression of a high-speed macro-micro manipulator. J. Sound Vib. 422, 318–342 (2018)

    Article  Google Scholar 

  16. Bastos Jr, G.: A stable reentry trajectory for flexible manipulators. Int. J. Control, pp. 1–12. (2019)

  17. Zhang, X.; Sørensen, R.; Iversen, M.R.; Li, H.: Computationally efficient dynamic modeling of robot manipulators with multiple flexible-links using acceleration-based discrete time transfer matrix method. Robot. Comput. Integr. Manuf. 49, 181–193 (2018)

    Article  Google Scholar 

  18. Mahmoodabadi, M.J.; Shahangian, M.M.: A new multi-objective artificial bee colony algorithm for optimal adaptive robust controller design. IETE J. Res. pp. 1–14. (2019)

  19. Patra, A.K.; Biswal, S.S.; Rout, P.K.:. Backstepping linear quadratic Gaussian controller design for balancing an inverted pendulum. IETE J. Res. pp. 1–15 (2019).

  20. Yin, Y.Z.; Yang, Z.L.; Yin, Z.X.; Xu, F.: Optimal control of LQR for discrete time-varying systems with input delays. Int. J. Syst. Sci. 49(5), 1021–1031 (2018)

    Article  MathSciNet  Google Scholar 

  21. Bilgic, H.H.; Sen, M.A.; Kalyoncu, M.: Tuning of LQR controller for an experimental inverted pendulum system based on the bees algorithm. J. Vibroeng. 18(6), 3684–3694 (2016)

    Article  Google Scholar 

  22. Şen, M.A.; Bilgiç, H.H.; Kalyoncu, M.: ÇiftTersSarkaçSistemininDengeVeKonumKontrolüiçin Arı Algoritmasıile LQR KontrolcüParametrelerininTayini. MühendisveMakina 57(679), 53–62 (2016)

    Google Scholar 

  23. Liu, L.Y.: Stability analysis of a single-link flexible arm driven by a motor of speed reference type. J. Chin. Inst. Eng. 40(4), 296–306 (2017)

    Article  Google Scholar 

  24. Sayahkarajy, M.; Mohamed, Z.; MohdFaudzi, A.A.: Review of modelling and control of flexible-link manipulators. Proc. Inst. Mech. Eng. Part I J. Syst. Control Eng. 230(8), 861–873 (2016)

    Google Scholar 

  25. Subedi, D.; Tyapin, I.; Hovland, G.: Review on modeling and control of flexible link manipulators. (2020)

  26. Sehgal, S.; Kumar, H.: Novel dynamic model updating technique for damped mechanical system. J. Theoret. Appl. Mech. 47(4), 75–85 (2017)

    Article  Google Scholar 

  27. Sehgal, S.; Kumar, H.: Structural dynamic model updating techniques: a state of the art review. Arch. Comput. Methods Eng. 23(3), 515–533 (2016)

    Article  Google Scholar 

  28. Sehgal, S.; Kumar, H.: Development of efficient model updating technique using multi-stage response surfaces and derringer's function. In 2014 Recent Advances in Engineering and Computational Sciences (RAECS) (pp. 1–6). IEEE. (2014)

  29. Ozgoli, S.; Taghirad, H.D.: A survey on the control of flexible joint robots. Asian J. Control 8(4), 332–344 (2006)

    Article  MathSciNet  Google Scholar 

  30. Dwivedy, S.K.; Eberhard, P.: Dynamic analysis of flexible manipulators, a literature review. Mech. Mach. Theory 41(7), 749–777 (2006)

    Article  MathSciNet  Google Scholar 

  31. Quanser Inc 2012 b “SRV02-Series Flexgage-Rotary Flexible link user manual”

  32. Anderson, B.D.; Moore, J.B.: Optimal control: linear quadratic methods. Courier Corporation. (2007)

  33. Oral, Ö.; Çetin, L.; Uyar, E.: A novel method on selection of Q and R matrices in the theory of optimal control. Int. J. Syst. Control 1(2). (2010)

  34. Wang, H.; Yang, S.; Ip, W.H.; Wang, D.: A memetic particle swarm optimisation algorithm for dynamic multi-modal optimisation problems. Int. J. Syst. Sci. 43(7), 1268–1283 (2012)

    Article  MathSciNet  Google Scholar 

  35. Sen, M.A.; Kalyoncu, M.: Grey wolf optimizer based tuning of a hybrid LQR-PID controller for foot trajectory control of a quadruped robot. Gazi Univ. J. Sci. 32(2), 674–684 (2019)

    Google Scholar 

  36. Abdel-razak, M.H.; Ata, A.A.; Mohamed, K.T.; Haraz, E.H.: Proportional–integral-derivative controller with inlet derivative filter fine-tuning of a double-pendulum gantry crane system by a multi-objective genetic algorithm. Eng. Optim. 52(3), 527–548 (2020)

    Article  Google Scholar 

  37. Pham, D.T.; Ghanbarzadeh, A.; Koç, E.; Otri, S.; Rahim, S.; Zaidi, M.: The bees algorithm—a novel tool for complex optimisation problems. In Intelligent Production Machines and Systems (pp. 454–459). Elsevier Science Ltd. (2006)

  38. Pham, D.T.; Ghanbarzadeh, A.; Koc, E.; Otri, S.; Rahim, S.; Zaidi, M.: The bees algorithm. Technical Note, Manufacturing Engineering Centre, Cardiff University, UK (2005)

  39. Sen, M.A.; Kalyoncu, M.: Optimal tuning of a LQR controller for an inverted pendulum using the bees algorithm. J. Autom. Control Eng. 4(5). (2016)

  40. Fahmy, A.A.; Kalyoncu, M.; Castellani, M.: Automatic design of control systems for robot manipulators using the bees algorithm. Proc. Inst. Mech. Eng. Part I J. Syst. Control Eng. 226(4), 497–508 (2012)

    Google Scholar 

  41. Ebubekir, K.: The bees algorithm theory, improvements and applications. Manufacturing Engineering Centre School of Engineering University of Wales; Cardiff United Kingdom. (2010)

  42. Pham, D.T.; Castellani, M.: Benchmarking and comparison of nature-inspired population-based continuous optimisation algorithms. Soft. Comput. 18(5), 871–903 (2014)

    Article  Google Scholar 

  43. Khadanga, R.K.; Padhy, S.; Panda, S.; Kumar, A.: Design and analysis of tilt integral derivative controller for frequency control in an islanded microgrid: a novel hybrid dragonfly and pattern search algorithm approach. Arab. Sci. Eng. 43(6), 3103–3114 (2018)

    Article  Google Scholar 

  44. Baroudi, M.; Saad, M.; Ghie, W.; Kaddouri, A.; Ziade, H.: Vibration controllability and observability of a single-link flexible manipulator. In 2010 7th International Multi-Conference on Systems, Signals and Devices (pp. 1–6). IEEE. (2010)

  45. Rahman, Z. A.; Mat Isa, A.A.; Ali, H.H.; Anuar, M.A.: Control of flexible beam with unmodelled dynamics using second-order pole placement and LQR techniques. In Applied Mechanics and Materials (Vol. 393, pp. 675–682). Trans Tech Publications Ltd. (2013)

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hasan Huseyin Bilgic.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bilgic, H.H., Sen, M.A., Yapici, A. et al. Meta-Heuristic Tuning of the LQR Weighting Matrices Using Various Objective Functions on an Experimental Flexible Arm Under the Effects of Disturbance. Arab J Sci Eng 46, 7323–7336 (2021). https://doi.org/10.1007/s13369-021-05428-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-021-05428-7

Keywords

Navigation