Balancing and Trajectory Tracking Control for Two-Wheeled Self-Balancing Robot
Balancing and Trajectory Tracking Control for Two-Wheeled Self-Balancing Robot |
||
![]() |
![]() |
|
© 2024 by IJETT Journal | ||
Volume-72 Issue-7 |
||
Year of Publication : 2024 | ||
Author : Nguyen Cao Cuong, Hoang Dinh Co |
||
DOI : 10.14445/22315381/IJETT-V72I7P105 |
How to Cite?
Nguyen Cao Cuong, Hoang Dinh Co, "Balancing and Trajectory Tracking Control for Two-Wheeled Self-Balancing Robot," International Journal of Engineering Trends and Technology, vol. 72, no. 7, pp. 45-57, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I7P105
Abstract
The Two-Wheeled Self-Balancing Robot (TWSBR) system is a system widely applied in automatic control experiments. This is a highly theoretical and practical Multi-Input and Multi-output (MIMO) system that has been applied in life. However, most of the research only revolves around balance control using trial-and-error search algorithms or simple mathematical equations. There are not many detailed studies on system mathematical equations and main application algorithms based on model understanding. This article presents a method to design and control a self-balancing two-wheeled robot. The main contents include the design of a robust adaptive controller based on the nominal model for TWSBR. The goal of the proposed controller is to stabilize the TWSBR body tilt angle and control the desired trajectory tracking for the robot. Simulation results show that the proposed controller has good quality and is robust to disturbances.
Keywords
Two-Wheeled Self-Balancing Robot, Adaptive controller, Robust controller, Trajectory tracking control, Robot.
References
[1] Suhardi Azliy Bin Junoh, “Two-Wheeled Balancing Robot Controller Designed Using PID,” Universiti Tun Hussein Onn Malaysia, 2015.
[Google Scholar] [Publisher Link]
[2] Jorge Solis et al., “Development of the Two-Wheeled Inverted Pendulum Type Mobile Robot WV-2R for Educational Purposes,” 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, St. Louis, MO, USA, pp. 2347-2352, 2009.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Vicky Mudeng et al., “Design and Simulation of Two-Wheeled Balancing Mobile Robot with PID Controller,” International Journal of Sustainable Transportation Technology, vol. 3, no. 1, pp. 12-19, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Junfeng Wu, and Wanying Zhang, “Design of Fuzzy Logic Controller for Two-Wheeled Self-Balancing Robot,” Proceedings of 2011 6th International Forum on Strategic Technology, Harbin, China, pp. 1266-1270, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Yan-Hou Wen, Yu-Sheng Lin, and Yih-Guang Leu, “Design and Implementation of the Balance of Two-Wheeled Robots,” 2013 International Conference on Advanced Robotics and Intelligent Systems, Tainan, Taiwan, pp. 130-134, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Junfeng Wu, Wanying Zhang, and Shengda Wang, “A Two-Wheeled Self-Balancing Robot with the Fuzzy PD Control Method,” Mathematical Problems in Engineering, pp. 1-13, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[7] M.A. Akmal, N.F. Jamin, and N.M. Abdul Ghani, “Fuzzy Logic Controller for Two Wheeled EV3 LEGO Robot,” 2017 IEEE Conference on Systems, Process and Control, Meleka, Malaysia, pp. 134-139, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Shui-Chun Lin, Ching-Chih Tsai, and Wen-Lung Luo, “Adaptive Neural Network Control of a Self-balancing Two-wheeled Scooter,” IECON 2007 - 33rd Annual Conference of the IEEE Industrial Electronics Society, Taipei, Taiwan, pp. 868-873, 2007.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Chenxi Sun, Tao Lu, and Kui Yuan, “Balance Control of Two-Wheeled Self-Balancing Robot Based on Linear Quadratic Regulator and Neural Network,” 2013 Fourth International Conference on Intelligent Control and Information Processing, Beijing, China, pp. 862-867, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Ali Unluturk, and Omer Aydogdu, “Adaptive Control of Two-Wheeled Mobile Balance Robot Capable to Adapt Different Surfaces Using A Novel Artificial Neural Network–Based Real-Time Switching Dynamic Controller,” International Journal of Advanced Robotic Systems, vol. 14, no. 2, pp. 1-9, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Arunit Maity, and Sarthak Bhargava, “Design and Implementation of a Self-Balancing Two-Wheeled Robot Driven by a Feed-Forward Backpropagation Neural Network,” International Research Journal of Engineering and Technology, vol. 7, no. 9, pp. 3876-3881, 2020.
[Google Scholar] [Publisher Link]
[12] Fuquan Dai et al., “A Two-Wheeled Inverted Pendulum Robot with Friction Compensation,” Mechatronics, vol. 30, pp. 116-125, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Carlos Aguilar Ibañez, O. Gutiérrez Frias, and M. Suárez Castañón, “Lyapunov-Based Controller for the Inverted Pendulum Cart System,” Nonlinear Dynamics, vol. 40, pp. 367-374, 2005.
[CrossRef] [Google Scholar] [Publisher Link]
[14] O. Octavio Gutiérrez Frías, “Lyapunov Method for the Controlling of the Two Wheels Inverted Pendulum,” 2011 8th International Conference on Electrical Engineering, Computing Science and Automatic Control, Merida City, Mexico, pp. 1-5, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Chong Chee Soon et al., “Sliding Mode Controller Design with Optimized PID Sliding Surface Using Particle Swarm Algorithm,” Procedia Computer Science, vol. 105, pp. 235-239, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[16] İlyas Eker, “Second-Order Sliding Mode Control with Experimental Application,” ISA Transactions, vol. 49, no. 3, pp. 394-405, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Ming Yue et al., “Indirect Adaptive Fuzzy Control for a Nonholonomic/Underactuated Wheeled Inverted Pendulum Vehicle Based on a Data-Driven Trajectory Planner,” Fuzzy Sets and Systems, vol. 290, pp. 158-177, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Mou Chen, “Robust Tracking Control for Self-Balancing Mobile Robots Using Disturbance Observer,” IEEE/CAA Journal of Automatica Sinica, vol. 4, no. 3, pp. 458-465, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Nguyen Gia Minh Thao, Duong Hoai Nghia, and Nguyen Huu Phuc, “A PID Backstepping Controller for Two-Wheeled Self-Balancing Robot,” International Forum on Strategic Technology, Ulsan, Korea (South), pp. 76-81, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[20] 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]
[21] Ching-Chih Tsai, Hsu-Chih Huang, and Shui-Chun Lin, “Adaptive Neural Network Control of a Self-Balancing Two-Wheeled Scooter,” IEEE Transactions on Industrial Electronics, vol. 57, no. 4, pp. 1420-1428, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Liangyong Wang, Tianyou Chai, and Lianfei Zh, “Neural-Network-Based Terminal Sliding-Mode Control of Robotic Manipulators Including Actuator Dynamics,” IEEE Transactions on Industrial Electronics, vol. 56, no. 9, pp. 3296-3304, 2009.
[CrossRef] [Google Scholar] [Publisher Link]
[23] J. Park, and I.W. Sandberg, “Universal Approximation Using Radial-Basis-Function Networks,” Neural Computation, vol. 3, no. 2, pp. 246–257, 1991.
[CrossRef] [Google Scholar] [Publisher Link]
[24] S.S. Ge, and C. Wang, “Adaptive Neural Control of Uncertain MIMO Nonlinear Systems,” IEEE Transactions on Neural Networks, vol. 15, no. 3, pp. 674–692, 2004.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Chenguang Yang et al., “Neural-Learning-Based Telerobot Control with Guaranteed Performance,” IEEE Transactions on Cybernetics, vol. 47, no. 10, pp. 3148–3159, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Zhijun Li, Chenguang Yang, and Liping Fan, Advanced Control of Wheeled Inverted Pendulum Systems, Springer London, pp. 1-218, 2012.
[Google Scholar] [Publisher Link]
[27] Rongxin Cui, Ji Guo, and Zhaoyong Mao, “Adaptive Backstepping Control of Wheeled Inverted Pendulums Models,” Nonlinear Dynamics, vol. 79, no. 1, pp. 501–511, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Vo Ba Viet Nghia et al., “Adaptive Neural Sliding Mode Control for Two Wheel Self Balancing Robot,” International Journal of Dynamics and Control, vol. 10, no. 771-784, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[29] F. Grasser et al., “JOE: A Mobile, Inverted Pendulum,” IEEE Transactions on Industrial Electronics, vol. 49, no. 1, pp. 107-114, 2002.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Jinkun Liu, Radial Basis Function (RBF) Neural Network Control for Mechanical Systems: Design, Analysis and Matlab Simulation, Springer Berlin Heidelberg, pp. 1-365, 2013.
[Google Scholar] [Publisher Link]
[31] Shuzhi S. Ge, C.C. Hang, and L.C. Woon, “Adaptive Neural Network Control of Robot Manipulators in Task Space,” IEEE Transactions on Industrial Electronics, vol. 44, no. 6, pp. 746-752, 1997.
[CrossRef] [Google Scholar] [Publisher Link]