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Stabilization and Equilibrium Control for Electrically Cart–Seesaw Systems by Neuro-fuzzy Approach

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Abstract

Background

Compared to completely control systems, super-articulated mechanical systems (SAMS) are controlled under-actuated systems where the dimensions of the control input space are less than the magnitudes of the output.

Purpose

Hence, the major aim of this study is developing an innovative cart–seesaw system which is manipulated by electrical servo actuators and also improving balancing methodology for such apparatus. Controlling the mobile cart on the seesaw is exceptionally hard because it is an under-actuated machinery.

Methods

With the purpose of the stabilization, the famous PID control scheme will be based on the genetic algorithm (GA) to pursue the main optimum parameters, and then enhancement of a fuzzy logic approach to stabilize the nonlinear model. The final controller will be completed by treating an intelligent fuzzy logic controller (FLC) based on adaptive neuro-fuzzy inference system (ANFIS) with GA tuning approach to speed up the effectiveness of controller construction. The knowledge base of fuzzy system was subsequently built on PID performance-related information.

Results

Experimentation shows that applying the suggested new cart–seesaw system (CSS) shows better functioning than the previous pneumatic cart–seesaw (PCS) system in tracking and balancing execution. Furthermore, the balancing approach presented in this paper meaningfully indicates the valuableness of the suggested intelligent controller in suppression oscillation of the seesaw.

Conclusion

The investigation of the proposed system will be useful for control course apparatus and also worthy for the entertainment device.

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References

  1. Uran S, Jezernik K (2002) Control of a ball and beam like mechanism. In: Proceedings of the 7th international workshop on advanced motion control, Maribor, Slovenia, 3–5 July 2002, pp 376-380

  2. Wang G, Tian Y, Hong W (2000) Stabilization and equilibrium control of super articulated ball and beam system. In: Proceedings of the 3rd World congress on intelligent control and automation, Hefei, China, 28 June–2 July 2000, pp 3290–3293

  3. Seto S, Baillieul J (1994) Control problems in super-articulated mechanical systems. IEEE Trans Automat Control 39(12):2442–2453

    Article  MathSciNet  MATH  Google Scholar 

  4. Aguilar-Ibáñez C, Sira-Ramírez H (2007) A linear differential flatness approach to controlling the Furuta pendulum. IMA J Math Control Inf 24(1):31–45

    Article  MathSciNet  MATH  Google Scholar 

  5. Aguilar-Ibáñez C, Sira-Ramírez H, Suárez-Castañón MS (2011) A flatness-based controller for the stabilization of the inverted pendulum. Math Prob Eng Article ID 659685. https://doi.org/10.1155/2011/659685

  6. Keshmiri M, Jahromi AF, Mohebbi A, Amoozgar MH, Xie WF (2012) Modeling and control of ball and beam system using model based and non-model based control approaches. Int J Smart Sens Intell Syst 5(1):14–35

    Google Scholar 

  7. Ho MT, Tu YW, Lin HS (2009) Controlling a ball and wheel system using full-state-feedback linearization—a testbed for nonlinear control design. IEEE Control Syst Mag 29(5):93–101

    Article  MathSciNet  Google Scholar 

  8. Lin J, Huang CJ, Chang J, Wang SW (2010) Active-passive vibration absorber of beam-cart-seesaw system with piezoelectric transducers. J Sound Vib 329(20):4109–4123

    Article  Google Scholar 

  9. Lin J, Guo SY, Chang J (2011) Fuzzy coordinator compensation for balancing control of cart-seesaw system. J Sound Vib 330(26):6296–6310

    Article  Google Scholar 

  10. Lin J, Ding YS, Chang J (2014) Balancing and swinging-up control for cart-pendulum-seesaw system by decomposed fuzzy coordination control. J Vib Control 20(6):925–942

    Article  Google Scholar 

  11. Chung BM, Lee JW, Joo HH, Lim YK (2000) Hybrid fuzzy learning controller for an unstable nonlinear system. Int J Korean Soc Precis Eng 1(1):79–83

    Google Scholar 

  12. Ramos LE, Castillo-Toledo B, Negrete S (1988) Nonlinear regulation of a seesaw-inverted pendulum. Proc IEEE Int Conf Control Appl Trieste Italy 1–4:1399–1403

    Google Scholar 

  13. Subbotin MV (2004) Balancing an inverted pendulum on a seesaw, Project Report, University of California, Santa Barbara, USA

  14. Lin CT, George LCS (1999) Neural fuzzy systems. Prentice Hall, USA

    Google Scholar 

  15. Kalaivani R, Lakshmi P, Rajeswari K (2015) An improved type-2 fuzzy logic approach based sliding mode controller for vehicle active suspension system. J Vib Eng Technol 3(4):431–446

    Google Scholar 

  16. Treetrong J, Sinha JK, Gu F, Ball A (2014) An investigation of genetic algorithm based motor parameter estimation for condition monitoring. J Vib Eng Technol 2(2):97–115

    Google Scholar 

  17. Afkar A, Marzbanrad J, Amirirad Y (2015) Modeling and semi active vertical vibration control of a GA optimized 7DoF driver model using an MR damper in a seat suspension system. J Vib Eng Technol 3(1):37–48

    Google Scholar 

  18. Afkar A, Marzbanrad J, Amirirad Y (2016) Mode control optimized by genetic algorithm for building model. J Vib Eng Technol 4(1):37–48

    Google Scholar 

  19. Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybernet 23(3):665–685

    Article  Google Scholar 

  20. Lin J, Zheng YB (2012) Vibration suppression control of smart piezoelectric rotating truss structure by parallel neuro-fuzzy control with genetic algorithm tuning. J Sound Vib 331(16):3677–3694

    Article  Google Scholar 

  21. Lin J (2005) A vibration absorber of smart structures using adaptive networks in hierarchical fuzzy control. J Sound Vib 287(4–5):683–705

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the National Science Council of the Republic of China, Taiwan for financially supporting this research under Contract No. NSC 96-2221-E-231-017-MY3.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Chien Hsin University of Science and Technology, 229, Jianxing Rd., Zhongli District, Taoyuan City, Taiwan 320, Republic of China

    J. Lin, H. Y. Lai & J. Chang

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  1. J. Lin
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  2. H. Y. Lai
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  3. J. Chang
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Correspondence to J. Lin.

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Lin, J., Lai, H.Y. & Chang, J. Stabilization and Equilibrium Control for Electrically Cart–Seesaw Systems by Neuro-fuzzy Approach. J. Vib. Eng. Technol. 6, 1–11 (2018). https://doi.org/10.1007/s42417-018-0004-9

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  • DOI: https://doi.org/10.1007/s42417-018-0004-9

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