Adaptive Control for Uncertain Nonlinear Systems based on Fuzzy Logic

Adaptive Control for Uncertain Nonlinear Systems based on Fuzzy Logic

  IJETT-book-cover           
  
© 2023 by IJETT Journal
Volume-71 Issue-5
Year of Publication : 2023
Author : Vu Ngoc Dan, To Van Binh
DOI : 10.14445/22315381/IJETT-V71I5P228

How to Cite?

Vu Ngoc Dan, To Van Binh, "Adaptive Control for Uncertain Nonlinear Systems based on Fuzzy Logic ," International Journal of Engineering Trends and Technology, vol. 71, no. 5, pp. 266-271, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I5P228

Abstract
Nowadays, robot manipulators are applied a lot in practice, it gradually replaces humans to perform boring, repetitive or life-threatening toxic environments. However, the robot is an uncertain nonlinear object, so it is difficult to model accurately, so it is not easy to control the robot to work stably. This paper presents an adaptive control method for uncertain nonlinear systems based on fuzzy logic. The controller is applied to control the robot manipulator to operate accurately, ensuring stability and good quality during the robot's work. The stability of the whole system is rigorously proven mathematically based on the Lyapunov theory. Finally, the simulation results of the robot manipulator system on Matlab-Simuink software show the effectiveness of the proposed method.

Keywords
Robot manipulator, Adaptive control, Fuzzy logic, Nonlinear systems, Uncertainty model.

References
[1] Jose Alvarez-Ramirez, Rafael Kelly, and Ilse Cervantes, “Semiglobal Stability of Saturated Linear PID Control for Robot Manipulators,” Automatica, vol. 39, pp. 989-995, 2003.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Julio E. Normey-Rico et al., “Mobile Robot Path Tracking Using a Robust PID Controller,” Control Engineering Practice, vol. 9, no. 11, pp. 1209–1214, 2001.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Ching-Chih Tsai et al., “Kinematics Control of an Omnidirectional Mobile Robot,” Proceedings of 2005 CACS Automatic Control Conference, Taiwan, 2005.
[Google Scholar] [Publisher Link]
[4] Y. Kanayama et al., “A Stable Tracking Control Method for an Autonomous Mobile Robot,” Proceedings, IEEE International Conference on Robotics and Automation, vol. 1, pp. 384-389, 1990.
[CrossRef] [Google Scholar] [Publisher Link]
[5] G. I. R. K. Galgamuwa et al., “Simplified Controller for Three Wheeled Omni Directional Mobile Robot,” 2015 IEEE 10th International Conference on Industrial and Information Systems (ICIIS), pp. 314-319, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Bong Seok Park et al., “Adaptive Tracking Control of Nonholonomic Mobile Robots Considering Actuator Dynamics: Dynamic Surface Design Approach,” 2009 American Control Conference, pp. 3860-3865, 2009.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Altan Onat, and Metin Ozkan, “Dynamic Adaptive Trajectory Tracking Control of Nonholonomic Mobile Robots Using Multiple Models Approach,” Advanced Robotics, vol. 29, no. 14, pp. 913–928, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Trong-Toan Tran, Shuzhi Sam Ge, and Wei He, “Adaptive Control for an Uncertain Robotic Manipulator with Input Saturations,” Control theory and Technology, vol. 14, no. 2, pp. 113–121, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Seung-Jae Cho et al., “Adaptive Time-Delay Control with a Supervising Switching Technique for Robot Manipulators,” Transactions of the Institute of Measurement and Control, vol. 39, no. 9, pp. 1374–1382, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Koshy George, and Karpagavalli Subramanian, “Adaptive Control of a Class of Nonlinear Time-Varying Systems with Multiple Models,” Control theory and Technology, vol. 14, no. 4, pp. 323–334, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Mingyue Cui et al., “Extended State Observer-Based Adaptive Sliding Mode Control of Differential-Driving Mobile Robot with Uncertainties,” Nonlinear Dynamics, vol. 83, no. (1-2), pp. 667–683, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Xiaohui Yang et al., “Disturbance Observer Based on Biologically Inspired Integral Sliding Mode Control for Trajectory Tracking of Mobile Robots,” IEEE Access, vol. 7, pp. 1–1, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Yongfu Li et al., “Integral-Sliding-Mode Braking Control for Connected Vehicle Platoon: Theory and Application,” IEEE Transactions on Industrial Electronics, vol. 66, no. 6, pp. 4618 - 4628, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Niraj Kumar Goswami, and Prabin Kumar Padhy, “Sliding Mode Controller Design for Trajectory Tracking of a Non-Holonomic Mobile Robot with Disturbance,” Computers & Electrical Engineering, vol. 72, pp. 307–323, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Jun-yong Zhai, and Zhi-bao Song, “Adaptive Sliding Mode Trajectory Tracking Control for Wheeled Mobile Robots,” International Journal of Control, vol. 92, no. 10, pp. 2255-2262, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Kang Liu, “Adaptive Sliding Mode Based Disturbance Attenuation Tracking Control for Wheeled Mobile Robots,” International Journal of Control, Automation and Systems, vol. 18, no. 5, pp. 1288–1298, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[17] O Anil Kumar, and Ch Rami Reddy, "Hybrid Neuro-Fuzzy Controller Based Adaptive Neuro-Fuzzy Inference System Approach for Multi-Area Load Frequency Control of Interconnected Power System," SSRG International Journal of Electrical and Electronics Engineering, vol. 3, no. 1, pp. 17-25, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Han-Xiong Li et al., “An Improved Robust Fuzzypid Controller with Optimal Fuzzy Reasoning,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 35, no. 6, pp. 1283-1294, 2005.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Basil M. Al-Hadithi, Fernando Matía, and Agustín Jiménez, “Fuzzy Controller for Robot Manipulators,” Foundations of Fuzzy Logic and Soft Computing, pp. 688–697.
[CrossRef] [Publisher Link]
[20] N. Kanagaraj, and Mujahed Al-Dhaifall, K S Nisar, “Design of Intelligent Fuzzy Fractional-Order PID Controller for Pressure Control Application,” 2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Lei Zhang, “Fuzzy Controllers Based on Optimal Fuzzy Reasoning for Missile Terminal Guidance,” 45th AIAA Aerospace Sciences Meeting and Exhibit, 2007.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Meilan Zhou et al., “Fuzzy PID Control and Simulation Analysis of Cruise Control System,” Information and Automation, vol. 86, pp. 289–295, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Vishal Verma, and Renu Bhardwaj, “Reduced Rule Base Fuzzy Controller for Performance Enhancement of Inline PI Controller,” 2012 IEEE International Conference on Power and Energy (PECon), pp. 446-451, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Jakubiec Beata, “Fuzzy Logic Controller for Robot Manipulator Control System,” 2018 Applications of Electromagnetics in Modern Techniques and Medicine (PTZE), pp. 77-80, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Jinkun Liu, Intelligent Control Design and MATLAB Simulation, Springer, 2018.
[Google Scholar] [Publisher Link]
[26] Chi Nguyen Van, and Hoang Dang Danh, "Cascade Adaptive Control for Active Suspension System," SSRG International Journal of Electrical and Electronics Engineering, vol. 6, no. 9, pp. 1-7, 2019.
[CrossRef] [Publisher Link]
[27] Li-Xin Wang, A Course in Fuzzy System and Control, Prentice-Hall International, Inc, 1996.
[Google Scholar] [Publisher Link]
[28] Aquib Mustafa, Narendra K. Dhar, and Nishchal K Verma, “Event-Triggered Sliding Mode Control for Trajectory Tracking of Nonlinear Systems,” IEEE/CAA Journal of Automatica Sinica, vol. 7, no. 1, pp. 307-314, 2019.
[CrossRef] [Google Scholar] [Publisher Link]