Abstract
Anti-sideslip has not been paid much attention by most researchers of wheeled mobile robots. And some existing anti-sideslip path tracking control methods based on switching control have problems such as relying on design experience. To enable the wheeled mobile robot to prevent sideslip and track the reference path at the same time, we propose an anti-sideslip path tracking control method based on a time-varying local model. The principle of this method is to make model predictions and rolling optimizations in the robot coordinate system in each control period. The proposed controller is tested by MATLAB simulation. According to the simulation results, the proposed controller can prevent sideslip when the wheeled mobile robot tracks the reference path. Even if the ground adhesion coefficient is low, the maximum lateral speed of the robot is only 0.2159 m/s. While preventing sideslip, the proposed controller is able to keep the displacement error of path tracking within 0.1681 m. Under the same conditions, the maximum absolute value of the displacement error of the proposed controller is at least 55.15% smaller than that of the controller based on the global model.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Sidek, N., Sarkar, N.: Dynamic modeling and control of nonholonomic mobile robot with lateral slip. In: Third International Conference on Systems (ICONS 2008), pp. 35–40. Cancun, Mexico (2008)
Yang, H., Wang, S., Zuo, Z., Li, P.: Trajectory tracking for a wheeled mobile robot with an omnidirectional wheel on uneven ground. IET Control Theory A. 14(7), 921–929 (2020)
Ibraheem, G.A.R., Azar, A.T., Ibraheem, I.K., Humaidi, A.J.: A novel design of a neural network-based fractional PID controller for mobile robots using hybridized fruit fly and particle swarm optimization. Complexity 2020, 3067024 (2020)
Khalaji, A.K., Jalalnezhad, M.: Robust forward\backward control of wheeled mobile robots. ISA T. 115, 32–45 (2021)
Zhao, L., Jin, J., Gong, J.: Robust zeroing neural network for fixed-time kinematic control of wheeled mobile robot in noise-polluted environment. Math. Comput. Simulat. 185, 289–307 (2021)
Gao, X., Gao, R., Liang, P., Zhang, Q., Deng, R., Zhu, W.: A hybrid tracking control strategy for nonholonomic wheeled mobile robot incorporating deep reinforcement learning approach. IEEE Access 9, 15592–15602 (2021)
Song, X.G.: Mobile System Modeling of Wheeled Robots and Model Learning-Based Research on Tracking Control. Harbin Institute of Technology, Harbin (2015, in Chinese)
Bai, G.X., Liu, L., Meng, Y., Luo, W.D., Gu, Q., Wang, J.P.: Path tracking of wheeled mobile robots based on dynamic prediction model. IEEE Access 7, 39690–39701 (2019)
Bai, G.X., Meng, Y., Liu, L., Luo, W.D., Gu, Q., Li, K.L.: Anti-sideslip path tracking of wheeled mobile robots based on fuzzy model predictive control. Electron. Lett. 56(10), 490–493 (2020)
Bai, G.X., Zhou, L., Meng, Y., Liu, L., Gu, Q., Wang, G.D.: Path tracking of unmanned vehicles based on time-varying local model. Chin. J. Eng. Online published (2022, in Chinese). https://doi.org/10.13374/j.issn2095-9389.2022.03.18.003
Ji, X., He, X., Lv, C., Liu, Y., Wu, J.: A vehicle stability control strategy with adaptive neural network sliding mode theory based on system uncertainty approximation. Veh. Syst. Dyn. 56(6), 923–946 (2018)
Ji, X., Yang, K., Na, X., Lv, C., Liu, Y., Liu, Y.: Feedback game-based shared control scheme design for emergency collision avoidance: a fuzzy-linear quadratic regulator approach. J. Dyn. Syst. Meas. Control Trans. ASME 141(8), 081005 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Bai, G., Meng, Y., Gu, Q., Wang, G., Dong, G., Zhou, L. (2022). An Anti-sideslip Path Tracking Control Method of Wheeled Mobile Robots. In: Liu, H., et al. Intelligent Robotics and Applications. ICIRA 2022. Lecture Notes in Computer Science(), vol 13456. Springer, Cham. https://doi.org/10.1007/978-3-031-13822-5_22
Download citation
DOI: https://doi.org/10.1007/978-3-031-13822-5_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-13821-8
Online ISBN: 978-3-031-13822-5
eBook Packages: Computer ScienceComputer Science (R0)