TRAJECTORY TRACKING CONTROL OF A TWO WHEELED SELF-BALANCING ROBOT BY USING SLIDING MODE CONTROL

Year 2024, Volume: 12 Issue: 3, 652 - 670, 01.09.2024

Mustafa Doğan , Ümit Önen

https://doi.org/10.36306/konjes.1425949

Abstract

Two-Wheeled Self-Balancing Robots are widely used in various fields today. These systems have a highly unstable nature due to their underactuated structures. On the other hand, parameter uncertainties and external disturbances significantly affect their control performance. The best way to deal with parameter uncertainties that can easily lead controllers to instability is to use robust control methods. Dealing with these uncertainties is particularly crucial in control of underactuated and unstable systems such as Two-Wheeled Self-Balancing Robots. In this study, trajectory tracking control of a two wheeled self-balancing robot by using Sliding Mode Control (SMC) was realized. The chattering problem inherent in the SMC method was eliminated by employing tangent hyperbolic (tanh) switching function instead of signum function. The performance of the SMC controller has been examined under five different cases including external disturbance and various parameter uncertainties and compared with PID and LQR methods. The results showed that the SMC method is much more insensitive to parameter changes than the PID and LQR methods. It has also been observed that all three controllers maintain their stability against disturbance inputs, but the SMC method offers a better control performance.

Keywords

Sliding Mode Control, Two Wheeled Self Balancing Robot, Trajectory Tracking

References

• D. Nemec, D. Adamkovic, M. Hrubos, R. Pirnik, ve M. Mihalik, “Fast Two-Wheeled Balancing Robot”, ss. 1-9, Haz. 2021, doi: 10.1109/ICCC51557.2021.9454659.
• G. C. M. Santo ve C. Garcia, “Construction, Control Design and Bluetooth Trajectory Control of a Self-Balancing Robot”, içinde SBAI 2019, 2019. doi: 10.17648/sbai-2019-111151.
• K. Prakash ve K. Thomas, “Study of controllers for a two wheeled self-balancing robot”, 2016 International Conference on Next Generation Intelligent Systems, ICNGIS 2016, Şub. 2017, doi: 10.1109/ICNGIS.2016.7854009.
• F. Grasser, A. D’Arrigo, S. Colombi, ve A. C. Rufer, “JOE: A mobile, inverted pendulum”, IEEE Transactions on Industrial Electronics, c. 49, sy 1, ss. 107-114, Şub. 2002, doi: 10.1109/41.982254.
• M. Velazquez, D. Cruz, S. Garcia, ve M. Bandala, “Velocity and Motion Control of a Self-Balancing Vehicle Based on a Cascade Control Strategy”, Int J Adv Robot Syst, c. 13, sy 3, Haz. 2016, doi: 10.5772/63933.
• J. X. Xu, Z. Q. Guo, ve T. H. Lee, “Design and implementation of a takagi-sugeno-type fuzzy logic controller on a two-wheeled mobile robot”, IEEE Transactions on Industrial Electronics, c. 60, sy 12, ss. 5717-5728, 2012, doi: 10.1109/TIE.2012.2230600.
• M. El-Bardini ve A. M. El-Nagar, “Interval type-2 fuzzy PID controller for uncertain nonlinear inverted pendulum system”, ISA Trans, c. 53, sy 3, ss. 732-743, 2014, doi: 10.1016/J.ISATRA.2014.02.007.
• O. Begovich, E. N. Sanchez, ve M. Maldonado, “Takagi-Sugeno fuzzy scheme for real-time trajectory tracking of an underactuated robot”, IEEE Transactions on Control Systems Technology, c. 10, sy 1, ss. 14-20, Oca. 2002, doi: 10.1109/87.974334.
• L. Guo, S. A. A. Rizvi, ve Z. Lin, “Optimal control of a two-wheeled self-balancing robot by reinforcement learning”, International Journal of Robust and Nonlinear Control, c. 31, sy 6, ss. 1885-1904, Nis. 2021, doi: 10.1002/RNC.5058.
• A. Unluturk ve O. Aydogdu, “Machine Learning Based Self-Balancing and Motion Control of the Underactuated Mobile Inverted Pendulum with Variable Load”, IEEE Access, c. 10, ss. 104706-104718, 2022, doi: 10.1109/ACCESS.2022.3210540.
• H. M. Omar, A. M. Elalawy, ve H. H. Ammar, “Two-wheeled Self balancing robot Modeling and Control using Artificial Neural Networks (ANN)”, NILES 2019 - Novel Intelligent and Leading Emerging Sciences Conference, ss. 196-200, Eki. 2019, doi: 10.1109/NILES.2019.8909311.
• J. Wu ve S. Jia, “T-S adaptive neural network fuzzy control applied in two-wheeled self-balancing robot”, Proceedings of the 6th International Forum on Strategic Technology, IFOST 2011, c. 2, ss. 1023-1026, 2011, doi: 10.1109/IFOST.2011.6021194.
• M. Önkol ve C. Kasnakoğlu, “Adaptive model predictive control of a two-wheeled robot manipulator with varying mass”, Measurement and Control (United Kingdom), c. 51, sy 1-2, ss. 38-56, Mar. 2018, doi: 10.1177/0020294018758527/ASSET/IMAGES/LARGE/10.1177_0020294018758527-FIG20.JPEG.
• M. S. Mahmoud ve M. T. Nasir, “Robust control design of wheeled inverted pendulum assistant robot”, IEEE/CAA Journal of Automatica Sinica, c. 4, sy 4, ss. 628-638, Eki. 2017, doi: 10.1109/JAS.2017.7510613.
• G. V. Raffo, V. Madero, ve M. G. Ortega, “An application of the underactuated nonlinear ℋ∞ controller to two-wheeled self-balanced vehicles”, Proceedings of the 15th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2010, 2010, doi: 10.1109/ETFA.2010.5641024.
• N. Uddin, “Lyapunov-based control system design of two-wheeled robot”, Proceedings - 2017 International Conference on Computer, Control, Informatics and its Applications: Emerging Trends In Computational Science and Engineering, IC3INA 2017, c. 2018-January, ss. 121-125, Tem. 2017, doi: 10.1109/IC3INA.2017.8251752.
• S. Cheng, H. Liu, ve M. Yao, “An Adaptive Backstepping-Based Controller for Trajectory Tracking of Wheeled Robots”, 2021 4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS), ss. 539-544, May. 2021, doi: 10.1109/ICPS49255.2021.9468124.
• L. Jiang, H. Qiu, Z. Wu, ve J. He, “Active disturbance rejection control based on adaptive differential evolution for two-wheeled self-balancing robot”, Proceedings of the 28th Chinese Control and Decision Conference, CCDC 2016, ss. 6761-6766, Ağu. 2016, doi: 10.1109/CCDC.2016.7532214.
• U. Onen, “Model-Free Controller Design for Nonlinear Underactuated Systems with Uncertainties and Disturbances by Using Extended State Observer Based Chattering-Free Sliding Mode Control”, IEEE Access, c. 11, ss. 2875-2885, 2023, doi: 10.1109/ACCESS.2023.3234864.
• V. T. Nguyen, C. Y. Lin, S. F. Su, ve Q. V. Tran, “Adaptive Chattering Free Neural Network Based Sliding Mode Control for Trajectory Tracking of Redundant Parallel Manipulators”, Asian J Control, c. 21, sy 3, ss. 1-16, Mar. 2019, doi: 10.1002/ASJC.1789.
• W. Junfeng ve Z. Wanying, “Research on control method of two-wheeled self-balancing robot”, Proceedings - 4th International Conference on Intelligent Computation Technology and Automation, ICICTA 2011, c. 1, ss. 476-479, 2011, doi: 10.1109/ICICTA.2011.132.
• F. N. Martins, M. Sarcinelli-Filho, ve R. Carelli, “A Velocity-Based Dynamic Model and Its Properties for Differential Drive Mobile Robots”, Journal of Intelligent and Robotic Systems: Theory and Applications, c. 85, sy 2, ss. 277-292, Şub. 2017, doi: 10.1007/S10846-016-0381-9/METRICS.
• D. Qian ve J. Yi, Hierarchical Sliding Mode Control for Under-actuated Cranes. Springer Berlin Heidelberg, 2015. doi: 10.1007/978-3-662-48417-3.
• M. Tinkir, U. Onen, M. Kalyoncu, ve F. M. Botsali, “Pid and interval type-2 fuzzy logic control of double inverted pendulum system”, 2010 The 2nd International Conference on Computer and Automation Engineering, ICCAE 2010, c. 1, ss. 117-121, 2010, doi: 10.1109/ICCAE.2010.5451988.
• O. Çakır, ve S. Tekin, “Oransal İntegral Türevsel Denetleyici Parametrelerinin Sezgisel Optimizasyon Yöntemleri ile Ayarlanması”, Avrupa Bilim ve Teknoloji Dergisi, sy 23, ss. 9-21, Nis. 2021, doi: 10.31590/EJOSAT.830467.
• Ü. Önen, A. Çakan, ve İ. İlhan, “Particle Swarm Optimization Based LQR Control of an Inverted Pendulum”, ETJ Engineering and Technology Journal, c. 2, ss. 2456-3358, 2017, doi: 10.18535/etj/v2i5.01.