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Reseach Article

Control of Non-Linear Inverted Pendulum using Fuzzy Logic Controller

by Arpit Jain, Deep Tayal, Neha Sehgal
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 69 - Number 27
Year of Publication: 2013
Authors: Arpit Jain, Deep Tayal, Neha Sehgal
10.5120/12141-8278

Arpit Jain, Deep Tayal, Neha Sehgal . Control of Non-Linear Inverted Pendulum using Fuzzy Logic Controller. International Journal of Computer Applications. 69, 27 ( May 2013), 7-11. DOI=10.5120/12141-8278

@article{ 10.5120/12141-8278,
author = { Arpit Jain, Deep Tayal, Neha Sehgal },
title = { Control of Non-Linear Inverted Pendulum using Fuzzy Logic Controller },
journal = { International Journal of Computer Applications },
issue_date = { May 2013 },
volume = { 69 },
number = { 27 },
month = { May },
year = { 2013 },
issn = { 0975-8887 },
pages = { 7-11 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume69/number27/12141-8278/ },
doi = { 10.5120/12141-8278 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:31:25.072849+05:30
%A Arpit Jain
%A Deep Tayal
%A Neha Sehgal
%T Control of Non-Linear Inverted Pendulum using Fuzzy Logic Controller
%J International Journal of Computer Applications
%@ 0975-8887
%V 69
%N 27
%P 7-11
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper proposes an intelligent control approach towards Inverted Pendulum in mechanical engineering. Inverted Pendulum is a well known topic in process control and offering a diverse range of research in the area of the mechanical and control engineering. Fuzzy controller is an intelligent controller based on the model of fuzzy logic i. e. it does not require accurate mathematical modelling of the system nor complex computations and it can handle complex and non linear systems without linearization. Our objective is to implement a Fuzzy based controller and demonstrate its application to Inverted Pendulum. Model design and simulation are done in MATLAB SIMULINK® software.

References
  1. Kosko, B. (1994). "Fuzzy systems as universal approximators," IEEE Trans. Comput, vol. 43, no. 11.
  2. Ying, H. , Ding, Y. , Li, S. , and Shao, S. (1999). "Fuzzy systems as universal approximators," IEEE Trans. Syst. , man, Cybern – Part A: Syst. Hum. , vol 29, no. 5.
  3. Weiestrass, K. (1885). " mathematische Werke, Band 3, Abhandlungen III," pp. 1 – 37, esp. p. 5, Sitzunsber. Koniglichen preuss. Akad. Wiss. , July 9 and July 30.
  4. Stone, M. H. (1937). "Applications of the theory of Boolean rings to general topology," Trans. Am. Math. Soc. , vol. 41, pp. 375 – 481, esp. pp. 453 – 481.
  5. Ben-Haim, Y. (2001). Information Gap Decision Theory: Decisions Under Severe Uncertainty, Series on Decision and Risk, Academic Press, London.
  6. K. Ogata, Modern Control Engineering, 4th ed, Pearson Education (Singapore) Pvt. Ltd. , New Delhi, 2005, ch. 12.
  7. K. Ogata, System Dynamics, 4th ed, Pearson Education (Singapore) Pvt. Ltd. , New Delhi, 2004.
  8. J. R. White, System Dynamics: Introduction to Design and Simulation of Controlled Systems, Online literature.
  9. Ajit K. Mandal, Introduction to Control Engineering, New Age International Pub. , New Delhi, 2000, ch. 13.
  10. Roland S. Burns, Advanced Control Engineering, Elsevier – Butterworth Heinemann, 2001, ch. 10.
  11. Astrom K. J. , and McAvoy Thomas J. , "Intelligent Control", J. Proc. Cont. 1992, Vol2, no 3, pp 115 – 127.
  12. T. I. Liu, E. J. Ko, and J. Lee, "Intelligent Control of Dynamic Systems", Journal of the Franklin Institute, Vol. 330, No. 3, pp. 491 – 503, 1993.
  13. Kevin M. Passino, and Stephen Yurkovich, Fuzzy Control, Addison Wesley longman, Inc. , California, 1998.
  14. M. A. Denai, F. Palis, and A. Zeghbib, "Modelling and control of non-linear systems using soft computing techniques", Elsevier Journal of Applied Soft Computing, vol. 7, 2007, pp 728 – 738.
  15. Lal Bahadur Prasad, Krishna Pratap Singh, and Hema Latha Javvaji, "Simulation of Neuro-Fuzzy Position Controller for Induction Motor Drive using Simulink", Proceedings of XXXI National Systems Conference, NSC 2007, P-49, Dec. 14 – 15, 2007, MIT Manipal, India.
  16. G. Ray, S. K. Das, and B. Tyagi, "stabilization of Inverted Pendulum via Fuzzy Control", IE(I) Journal-EL, vol. 88, Sept. 2007, pp. 58 – 62.
  17. C. W. Tao, J. S. Taur, C. M. Wang, and U. S. Chen, "Fuzzy hierarchical swing-up and sliding position controller for the inverted pendulum-cart system", Elsevier Journal: Fuzzy Sets and Systems, vol. 159, 2008, pp. 2763 – 2784.
  18. Yanmei Liu, Zhen Chen, Dingyun Xue, and Xinhe Xu, "Real-Time Controlling of Inverted Pendulum by Fuzzy Logic", Proceedings of the IEEE International Conference on Automation and Logistics, Shenyang, China, August 2009, pp. 1180 – 1183.
  19. Hari Om Gupta, Lal Bahadur Prasad and Barjeev Tyagi, "Intelligent Control of Non Linear Inverted Pendulum Dynamical System with Disturbance Input Using Fuzzy Logic Systems", International Conference On Recent Advancements in Electrical Electronics and Control Engineering, 2011.
  20. Ashab Mishra, and Capt. Dr. Sarfraz Hussain, "Robust Controller for Nonlinear and Unstable system: Inverted Pendulum", ASME Journal of Control & Design Simulation, pp 49-60, vol. 55, No. 3, 4. 2000.
  21. Ashab Mishra, Iram Mahboob and Capt. Dr. Sarfraz Hussain, "Flexible Broom Balancing". ASME Journal of C & D Simulation, vol 56, No 1, 2. 2001.
  22. Raymond T. Stefani, Bahram Shahian, Late Clement J. Savant, and Late Gene Hosteller, "Design of Feedback Control Systems", Oxford University Press, 2002.
  23. Aleem Ahmad Khan, and kashan Hussain, "Comparative Performance Analysis between Fuzzy Logic Controller (FLC) and PID Controller for an Inverted Pendulum", International Journal of Electrical, Electronics and Computer Systems (IJEECS), Vol 10, October 2012.
Index Terms

Computer Science
Information Sciences

Keywords

Inverted Pendulum Fuzzy logic Fuzzy controller