ROBUST ADAPTIVE CONTROL FOR TWO-WHEELED DIFFERENTIAL MOBILE ROBOT WITH MODEL UNCERTAINTY, EXTERNAL DISTURBANCE AND WHEEL SLIP BASED ON SLIDING MODE CONTROLLER  AND NEURAL NETWORK

Doãn Văn Hóa; Lê Thị Hoàn

doi:10.34238/tnu-jst.12361

ROBUST ADAPTIVE CONTROL FOR TWO-WHEELED DIFFERENTIAL MOBILE ROBOT WITH MODEL UNCERTAINTY, EXTERNAL DISTURBANCE AND WHEEL SLIP BASED ON SLIDING MODE CONTROLLER AND NEURAL NETWORK

About this article

Received: 21/03/25 Revised: 09/07/25 Published: 09/07/25

Authors

1. Doan Van Hoa , University of Economics – Technology for Industries
2. Le Thi Hoan, University of Economics – Technology for Industries

Abstract

This paper presents a robust adaptive control method, focusing on the use of sliding mode control techniques combined with radial basis function neural networks. The method is designed for two-wheeled differential mobile robots, a type of robot commonly used in autonomous mobility applications. One of the strengths of this method is its ability to effectively address the challenges faced by robots, including external environmental disturbances, model uncertainty, and wheel slippage. These factors often cause great difficulties in maintaining stability and control performance for robots. The stability of the closed-loop system has been proven through the application of the Lyapunov criterion, an important tool in control system theory. To test the effectiveness of the proposed control method, simulation results have been performed on the Matlab – Simulink platform.In these experiments, the robot's ability to move stably along various trajectories was tested, including circular and trifolium trajectories. These results not only confirm the rationality of the method but also show its superior performance in maintaining stable control operation for the three-wheeled mobile robot with approximately zero tracking error.

Keywords

Adaptive control; Sliding mode control; Two-wheeled differential mobile robot; Wheel slips; Radial basis function neural network

Full Text:

PDF (Tiếng Việt)

References

[1] S. G. Tzafestas, Introduction to mobile robot control, First edition. Amsterdam: Elsevier, 2014.

[2] H. Yu, N. Sheng, and Z. Ai, “Sliding mode control for trajectory tracking of mobile robots,” 2021 40th Chinese Control Conference (CCC), Shanghai, China, 2021, pp. 13-17.

[3] Y. Cao and J. Pu, “A Novel Zeroing Neural Network Control Scheme for Tracked Mobile Robot Based on an Extended State Observer,” Applied Sciences, vol.14, no.1, 2023, Art. no. 303.

[4] C. Lei, G. Li, and Z. Yang, “Backstepping Sliding Mode Control for Trajectory Tracking of Mobile Robot: An Experimental and Comparative Study,” 2024 IEEE 7th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), vol.7, pp.700-706, 2024.

[5] S. R. Dekhterman, W. R. Norris, D. Nottage, and A. Soylemezoglu, “Hierarchical Rule-Base Reduction Fuzzy Control for Path Tracking Variable Linear Speed Differential Steer Vehicles,” IEEE Transactions on Fuzzy Systems, vol. 33, no. 3, pp. 828-841, 2025.

[6] N. K. Goswami and P. K. Padhy, “Sliding mode controller design for trajectory tracking of a non-holonomic mobile robot with disturbance,” Comput. Electr. Eng., vol. 72, pp. 307-323, 2018.

[7] S. Peng and W. Shi, “Adaptive fuzzy output feedback control of a nonholonomic wheeled mobile robot,” IEEE Access, vol. 6, pp. 43414- 43424, 2018.

[8] D. Huang, J. Zhai, W. Ai, and S. Fei, “Disturbance observer-based robust control for trajectory tracking of wheeled mobile robots,” Neurocomputing, vol. 198, pp. 74-79, 2016.

[9] L. Li, T. Wang, Y. Xia, and N. Zhou, “Trajectory tracking control for wheeled mobile robots based on nonlinear disturbance observer with extended Kalman filter,” J. Franklin Inst., vol. 357, no. 13, pp. 8491-8507, 2020.

[10] T. T. L. Vu, “Research and design controller for mobile robot on the basis of sliding mode control method,” (in Vietnamese), TNU Journal of Science and Technology, vol. 227, no. 8, pp. 95–102, Apr. 2022.

[11] T. T. T. Tran, “Proportional integral derivative sliding mode control for an omni-directional mobile robot,” (in Vietnamese), TNU Journal of Science and Technology, vol. 227, no. 8, pp. 123–130, Apr. 2022.

[12] T. H. Nguyen, T. H. Vo, Q. L. Vo, and H. H. Bui, “Design and development of traction tracking control for mobile robots based on neural networks subject to uncertainty parameters and disturbances,” (in Vietnamese), HUIH Journal, vol. 2024, 2024, Art. no. 286, doi: 10.57001/huih5804.2024.286.

[13] Y. Jiang, L. C. Boon, and W. Danwei, “Integrated Estimation for Wheeled Mobile Robot posture, velocities, and wheel skidding perturbations,” in Proceedings of the IEEE International Conference on Robotics and Automation, 2007, pp. 2355–2360.

[14] G. Baffet, A. Charara, and J. Stephant, “Sideslip angle, lateral force and road fricion estimation in simulations and experiments,” Proceedings of IEEE International Conference on Control Applications, 2006, pp. 903-908.

[15] T. Nguyen and L. Le, “Neural network-based adaptive tracking control for a nonholonomic wheeled mobile robot with unknown wheel slips, model uncertainties, and unknown bounded disturbances,” Turkish J. Electr. Eng. Comput. Sci., vol. 26, no. 1, pp. 378-392, 2018.

[16] V. Q. Ha, S. T. H. Pham, and N. T.-T. Vu, “Adaptive fuzzy type-2 controller for wheeled mobile robot with disturbances and wheelslips,” Journal of Robotics, vol. 2021, pp. 1-11, September 2021.

[17] Y. Jinhua, Y. Suzhen, and J. Xiao, “Trajectory tracking control of WMR based on sliding mode disturbance observer with unknown skidding and slipping,” IEEE 2nd International Conference on Cybernetics, Robotics and Control, 2017, pp. 18-22.

[18] J. Liu, Intelligent control design and MATLAB simulation, Springer Singapore, 2017, doi: 10.1007/978-981-10-5263-7.

DOI: https://doi.org/10.34238/tnu-jst.12361

Refbacks

There are currently no refbacks.



Remember me