Menu
Publications
2024
2023
2022
2021
2020
2019
2018
2017
2016
2015
2014
2013
2012
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
Editor-in-Chief
Nikiforov
Vladimir O.
D.Sc., Prof.
Partners
doi: 10.17586/2226-1494-2023-23-5-904-910
Trajectory tracking control for mobile robots with adaptive gain
Read the full article ';
Article in English
For citation:
Abstract
For citation:
Zhiqiang C., Duzhesheng L., Krasnov A.Yu., Yanyu L. Trajectory tracking control for mobile robots with adaptive gain. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2023, vol. 23, no. 5, pp. 904–910. doi: 10.17586/2226-1494-2023-23-5-904-910
Abstract
This paper studies the trajectory tracking problem and the controller gain adjustment problem for Wheeled Mobile Robots. The controller gain has a great influence on the robot’s trajectory tracking: it can influence both the tracking accuracy and the tracking speed. Therefore, it is very important to choose a suitable control gain during the controller design process. Current neural network gain controllers have a complex structure and require a lot of calculations to find the optimal value. To solve this problem, we design a trajectory tracking controller with a simple structure with adaptive gain by combining the controller with a neural network. The input to this controller is the robot’s attitude error. The controller has no hidden layer and directly outputs the trajectory tracking control law. Firstly, the kinematic controller is designed based on Lyapunov function method to ensure that the robot moves according to the reference trajectory. Then, the online gain adjustment algorithm is designed by using neural network to realize the fast adjustment of the controller gain and ensure the reliability of the controller. Finally, the backstepping method is utilized to design the velocity tracking controller based on the error between the virtual velocity and the actual velocity. Considering the influence of the external environment, we also design a nonlinear disturbance observer to estimate the total disturbance on the robot. We perform simulation experiment in MATLAB. The result of the experiment shows that the control algorithm proposed in this paper can realize the accurate tracking of the robot on the specified trajectory. The gain adjustment algorithm we designed can find the optimal gain value quickly and efficiently, thus improving the stability and efficiency of the controller. The method can be applied to most mobile robot trajectory tracking problems and solves the problem of control gain adjustment.
Keywords: wheeled mobile robot, trajectory tracking control, online gain estimation, backstepping method, non-linear disturbance observer
References
References
- Xiao X., Liu B., Warnell G., Stone P. Motion planning and control for mobile robot navigation using machine learning: a survey. Autonomous Robots, 2022, vol. 46, no. 5, pp. 569–597. https://doi.org/10.1007/s10514-022-10039-8
- Tzafestas S.G. Mobile robot control and navigation: A global overview. Journal of Intelligent & Robotic Systems, 2018, vol. 91, no. 1, pp. 35–58. https://doi.org/10.1007/s10846-018-0805-9
- Pandey A., Pandey S., Parhi D.R. Mobile robot navigation and obstacle avoidance techniques: A review. International Robotics & Automation Journal, 2017, vol. 2, no. 3, pp. 22. https://doi.org/10.15406/iratj.2017.02.00023
- Kanayama Y., Kimura Y., Miyazaki F., Noguchi T. A stable tracking control method for an autonomous mobile robot. Proc. of the IEEE International Conference on Robotics and Automation, 1990, pp. 384–389. https://doi.org/10.1109/ROBOT.1990.126006
- Sun S. Designing approach on trajectory-tracking control of mobile robot. Robotics and Computer-Integrated Manufacturing, 2005, vol. 21, no. 1, pp. 81–85. https://doi.org/10.1016/j.rcim.2004.04.002
- Xin L., Wang Q., She J., Li Y. Robust adaptive tracking control of wheeled mobile robot. Robotics and Autonomous Systems, 2016, vol. 78, pp. 36–48. https://doi.org/10.1016/j.robot.2016.01.002
- Huang J., Wen C., Wang W., Jiang Z.-P. Adaptive output feedback tracking control of a nonholonomic mobile robot. Automatica, 2014, vol. 50, no. 3, pp. 821–831. https://doi.org/10.1016/j.automatica.2013.12.036
- Shojaei K., Shahri A.M., Tarakameh A., Tabibian B. Adaptive trajectory tracking control of a differential drive wheeled mobile robot. Robotica, 2011, vol. 29, no. 3, pp. 391–402. https://doi.org/10.1017/S0263574710000202
- Huang J., Wen C., Wang W., Jiang Z.-P. Adaptive stabilization and tracking control of a nonholonomic mobile robot with input saturation and disturbance. Systems & Control Letters, 2013, vol. 62, no. 3, pp. 234–241. https://doi.org/10.1016/j.sysconle.2012.11.020
- Jiang Z.P., Nijmeijer H. Tracking control of mobile robots: A case study in backstepping. Automatica, 1997, vol. 33, no. 7, pp. 1393–1399. https://doi.org/10.1016/S0005-1098(97)00055-1
- Fierro R., Lewis F.L. Control of a nonholomic mobile robot: Backstepping kinematics into dynamics. Journal of Robotic Systems, 1997, vol. 14, no. 3, pp. 149–163. https://doi.org/10.1002/(SICI)1097-4563(199703)14:3<149::AID-ROB1>3.0.CO;2-R
- Fukao T., Nakagawa H., Adachi N. Adaptive tracking control of a nonholonomic mobile robot. IEEE Transactions on Robotics and Automation, 2000, vol. 16, no. 5, pp. 609–615. https://doi.org/10.1109/70.880812
- Mudi R.K., Pal N.R. A self-tuning fuzzy PI controller. Fuzzy Sets and Systems, 2000, vol. 115, no. 2, pp. 327–338. https://doi.org/10.1016/S0165-0114(98)00147-X
- Le T.D., Kang H.J., Suh Y.S., Ro Y.-S. An online self-gain tuning method using neural networks for nonlinear PD computed torque controller of a 2-dof parallel manipulator. Neurocomputing, 2013, vol. 116, pp. 53–61. https://doi.org/10.1016/j.neucom.2012.01.047
- Zong Q., Zhao Z.S., Zhang J. Higher order sliding mode control with self-tuning law based on integral sliding mode. IET Control Theory & Applications, 2010, vol. 4, no. 7, pp. 1282–1289. https://doi.org/10.1049/iet-cta.2008.0610
- Sungthong A., Assawinchaichote W. Particle swam optimization based optimal PID parameters for air heater temperature control system. Procedia Computer Science, 2016, vol. 86, pp. 108–111. https://doi.org/10.1016/j.procs.2016.05.027
- Yamada T., Yabuta T. Neural network controller using autotuning method for nonlinear functions. IEEE Transactions on Neural Networks, 1992, vol. 3, no. 4, pp. 595–601. https://doi.org/10.1109/72.143373
- Zeng W., Wang Q., Liu F., Wang Y. Learning from adaptive neural network output feedback control of a unicycle-type mobile robot. ISA Transactions, 2016, vol. 61, pp. 337–347. https://doi.org/10.1016/j.isatra.2016.01.005
- Chen W.H., Ballance D.J., Gawthrop P.J., O'Reilly J. A nonlinear disturbance observer for robotic manipulators. IEEE Transactions on Industrial Electronics, 2000, vol. 47, no. 4, pp. 932–938. https://doi.org/10.1109/41.857974