Towards a Novel Hybrid Fuzzy Logic-Based Control Strategy to An Inverted Pendulum System

International Journal of Electrical and Electronics Engineering

© 2023 by SSRG - IJEEE Journal

Volume 10 Issue 12

Year of Publication : 2023

Authors : Quang-Hung Do, Ngoc-Khoat Nguyen

10.14445/23488379/IJEEE-V10I12P103

How to Cite?

Quang-Hung Do, Ngoc-Khoat Nguyen, "Towards a Novel Hybrid Fuzzy Logic-Based Control Strategy to An Inverted Pendulum System," SSRG International Journal of Electrical and Electronics Engineering, vol. 10,  no. 12, pp. 18-26, 2023. Crossref, https://doi.org/10.14445/23488379/IJEEE-V10I12P103

Abstract:

The current study proposes a new solution with a successful control method to balance an inverted pendulum placed on a cart. The control plant is a nonlinear drive system consisting of a freely rotating rod and a small moving 4-wheel vehicle. A novel control strategy is created as a reasonable integration between a fuzzy logic structure built up depending on the Lyapunov stability theory and the Particle Swarm Optimization (PSO) algorithm. The Lyapunov theory has long been known for designing effectual control schemes. Meanwhile, the PSO mechanism, one of the most famous and efficient optimization methods, determines three scaling factors in the control diagram. Various simulation results of the proposed hybrid fuzzy logic controller in four scenarios have been successfully obtained using MATLAB/Simulink software, outperforming several existing counterparts, namely conventional PID, PD- and PI-like fuzzy logic regulators. Promising findings in this study verify the applicability of the proposed control methodology in both theoretical and practical aspects.

Keywords:

IPBCS, Fuzzy logic, Lyapunov theory, PSO, Hybrid controller.

References:

[1] Sriram Shreedharan, Vignesh Ravikumar, and Senthil Kumaran Mahadevan, “Design and Control of Real-Time Inverted Pendulum System with Force-Voltage Parameter Correlation,” International Journal of Dynamics and Control, vol. 9, pp. 1672-1680, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Kent H. Lundberg, and Taylor W. Barton, “History of Inverted-Pendulum Systems,” IFAC Proceedings Volumes, vol. 42, no. 24, pp. 131- 135, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Kavirayani Srikanth, and Gundavarapu V. Nagesh Kumar, “Novel Fuzzy Preview Controller for Rotary Inverted Pendulum under Time Delays,” International Journal of Fuzzy Logic and Intelligent Systems, vol. 17, no. 4, pp. 257-263, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[4] S. Yasunobu, and M. Mori, “Swing up Fuzzy Controller for Inverted Pendulum Based on a Human Control Strategy,” Proceedings of the 6th IEEE International Conference on Fuzzy Systems, Barcelona, Spain, vol. 3, pp. 1621-1625, 1997.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Wei Zhong, and H. Rock, “Energy and Passivity Based Control of the Double Inverted Pendulum on a Cart,” Proceedings of the 2001 IEEE International Conference on Control Applications, Mexico, pp. 896-901, 2001.
[CrossRef] [Google Scholar] [Publisher Link]
[6] C. Sravan Bharadwaj, T. Sudhakar Babu, and N. Rajasekar, “Tuning PID Controller for Inverted Pendulum Using Genetic Algorithm,” Advances in Systems, Control and Automation, pp. 395-404, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Ahmad Ilyas Roose, Samer Yahya, and Hussain Al-Rizzo, “Fuzzy-Logic Control of an Inverted Pendulum on a Cart,” Computers & Electrical Engineering, vol. 61, pp. 31-47, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[8] J. Yi, and N. Yubazaki, “Stabilization Fuzzy Control of Inverted Pendulum System,” Artificial Intelligence in Engineering, vol. 14, no. 2, pp. 153-163, 2000.
[CrossRef] [Google Scholar] [Publisher Link]
[9] C.W. Tao et al., “Fuzzy Hierarchical Swing-Up and Sliding Position Controller for the Inverted Pendulum-Cart System,” Fuzzy Sets and Systems, vol. 159, no. 20, pp. 2763-2784, 2008.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Mahbubeh Moghaddas et al., “Design of Optimal PID Controller for Inverted Pendulum Using Genetic Algorithm,” International Journal of Innovation, Management and Technology, vol. 3, no. 4, pp. 440-442, 2012.
[Google Scholar] [Publisher Link]
[11] Zhijun Li, and Chunquan Xu, “Adaptive Fuzzy Logic Control of Dynamic Balance and Motion for Wheeled Inverted Pendulums,” Fuzzy Sets and Systems, vol. 160, no. 12, pp. 1787-1803, 2009.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Bui Cong Cuong, and Nguyen Doan Phuoc, Fuzzy Sets, Neural Networks and Applications, Science and Technics Publishing House, 2001.
[13] Michael Voskoglou, Fuzzy Sets, Fuzzy Logic and Their Applications, MDPI, pp. 1-366, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Phan Xuan Minh, and Nguyen Doan Phuoc, The Theory of Fuzzy Logic, Science and Technics Publishing House, 2016.
[15] Mahmoud Baklizi, “FLACC: Fuzzy Logic Approach for Congestion Control,” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 10, no. 7, pp. 43-50, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[16] M. Madhusudhan, and H. Pradeepa, “Fuzzy Based Controller to Enhance Transient and Steady State Stability for Multimachine Power System,” International Journal of Engineering Trends and Technology, vol. 70, no. 3, pp. 118-125, 2022.
[CrossRef] [Publisher Link]
[17] Z. Bingul, G.E. Cook, and A.M. Strauss, “Application of Fuzzy Logic to Spatial Thermal Control in Fusion Welding,” IEEE Transactions on Industry Applications, vol. 36, no. 6, pp. 1523-1530, 2000.
[CrossRef] [Google Scholar] [Publisher Link]