Trajectory Tracking via Backstepping Controller with PID or SMC for Mobile Robots

Year 2023, , 120 - 134, 28.02.2023

Sinan Yigit , Aziz Sezgin

https://doi.org/10.16984/saufenbilder.1148158

Cited By: 1

Abstract

Mobile robot concept is one of the most commonly used nonholonomic system for industrial and academic autonomous applications. There are many types of mobile robot design concepts and control strategies which have been continuously developed by researchers. In this study, two wheeled differential drive mobile robot (DDMR) is used for trajectory tracking study under different conditions. Reference trajectory, dynamic and kinematic motion models of DDMR are defined as mathematical expressions in computer software. For tracking the reference trajectory, error between current pose and reference pose was decreased by sliding mode controller (SMC) and proportional–integral–derivative controller (PID) with kinematic based backstepping controller (KBBC) respectively. A reference path which consists of sinusoidal and linear parts tracked by both controller combinations in first simulation to examine controller tracking performances. In order to examine and compare; responsiveness, stability and robustness of the controllers, an additional mass which affects motion dynamics of DDMR vertically added to the mobile robot body during trajectory tracking application. All results and discussions are comparatively stated at the end of the study with related error figures and evaluations.

Keywords

Differential drive mobile robot, PID and sliding mode control, kinematic based backstepping control, trajectory tracking, robustness test

References

• [1] M. S. A. Mahmud, M. S. Z. Abidin, Z. Mohamed, M. K. I. Abd Rahman, M Iida, “Multi-objective path planner for an agricultural mobile robot in a virtual greenhouse environment,” Computers and Electronics in Agriculture, 157, 2019.
• [2] H. Li, A. V. Savkin. “An algorithm for safe navigation of mobile robots by a sensor network in dynamic cluttered industrial environments,” Robotics and Computer-Integrated Manufacturing, 54: 65-82, 2018.
• [3] Q. V. Dang, I. Nielsen, S. Bøgh, G. Bocewicz, “Modelling and scheduling autonomous mobile robot for a real-world industrial application,”IFAC Proceedings Volumes, 46(9), 2098-2103, 2013.
• [4] D. Pazderski, “Application of transverse functions to control differentially driven wheeled robots using velocity fields,” Bulletin of the Polish Academy of Sciences, Technical Sciences 64.4, 2016.
• [5] P. Panahandeh, K. Alipour, B. Tarvirdizadeh, A. Hadi, “A kinematic Lyapunov-based controller to posture stabilization of wheeled mobile robots,” Mechanical Systems and Signal Processing, 134, 106319, 2019.
• [6] T. Hellström, “Kinematics equations for differential drive and articulated steering,” Department of Computing Science, Umeå University, 2011.
• [7] L. Armesto, V. Girbés, A. Sala, M. Zima, V. Šmídl, “Duality-based nonlinear quadratic control: Application to mobile robot trajectory-following,” IEEE Transactions on Control Systems Technology, 23(4),1494-1504, 2015.
• [8] H. Taheri, C. X. Zhao, “Omnidirectional mobile robots, mechanisms and navigation approaches,” Mechanism and Machine Theory, 153, 103958, 2020.
• [9] F. Demirbaş, M. Kalyoncu, “Differential drive mobile robot trajectory tracking with using pid and kinematic based backstepping controller,” Selçuk Üniversitesi Mühendislik, Bilim ve Teknoloji Dergisi, 5(1), 1-15, 2017.
• [10] R. Dhaouadi, A. A. Hatab, “Dynamic modelling of differential-drive mobile robots using lagrange and newton-euler methodologies: A unified framework,” Advances in Robotics & Automation, 2(2), 1-7, 2013.
• [11] Y. Kanayama, Y. Kimura, F. Miyazaki, T. Noguchi, “A stable tracking control method for an autonomous mobile robot,” Proceedings., IEEE International Conference on Robotics and Automation, OH, USA, pp. 384-389 vol.1, 1990.
• [12] N. Wada, S. Tagami, M. Saeki, “Path-following control of a mobile robot in the presence actuator constraints, ” Advanced Robotics, 21(5-6), 645-659, 2007.
• [13] O. Mohareri, “Mobile robot trajectory tracking using neural networks,” PhD Thesis, 2009.
• [14] Y. Z. Arslan, A. Sezgin, N. Yagiz, “Improving the ride comfort of vehicle passenger using fuzzy sliding mode controller,” Journal of Vibration and Control 21.9: 1667-1679, 2015.