A Neural Network Controller Design for the Mecanum Wheel Mobile Robot
Received: 6 February 2023 | Revised: 26 February 2023 | Accepted: 4 March 2023 | Online: 2 April 2023
Corresponding author: Thai Nguyen
Abstract
Advanced controllers are an excellent choice for the trajectory tracking problem of Wheeled Mobile Robots (WMRs). However, these controllers pose a challenge to the hardware structure of WMRs due to the controller's complex structure and the large number of calculations needed. In that context, designing a controller with a simple structure and a small number of computations but good real-time performance is necessary in order to improve the tracking accuracy for the WMRs without requiring high hardware architecture. In this work, a neural network controller with a simple structure for the trajectory-tracking of a Mecanum-Wheel Mobile robot (MWMR) based on a reference controller is proposed. A two-layer feedforward neural network is designed as a tracking controller for the robot. The neural network is trained with a sample input-output data set so that the error between the neural network output and the reference control signal of the supervisory controller is minimal. The neural network parameters are trained to update over time. The simulation results verified the effectiveness of the neural network controller, whose parameters are tuned adaptively to ensure a fast convergence to the desired Bézier trajectory.
Keywords:
tracking control, neural networks, Bézier trajectory, mecanum wheel mobile robotDownloads
References
M. Szeremeta and M. Szuster, "Neural Tracking Control of a Four-Wheeled Mobile Robot with Mecanum Wheels," Applied Sciences, vol. 12, no. 11, Jan. 2022, Art. no. 5322. DOI: https://doi.org/10.3390/app12115322
J. Qian, B. Zi, D. Wang, Y. Ma, and D. Zhang, "The Design and Development of an Omni-Directional Mobile Robot Oriented to an Intelligent Manufacturing System," Sensors, vol. 17, no. 9, Sep. 2017, Art. no. 2073. DOI: https://doi.org/10.3390/s17092073
N. H. Thai, T. T. K. Ly, H. Thien, and L. Q. Dzung, "Trajectory Tracking Control for Differential-Drive Mobile Robot by a Variable Parameter PID Controller," International Journal of Mechanical Engineering and Robotics Research, pp. 614–621, 2022. DOI: https://doi.org/10.18178/ijmerr.11.8.614-621
N. H. Thai, T. T. K. Ly, and L. Q. Dzung, "Trajectory tracking control for mecanum wheel mobile robot by time-varying parameter PID controller," Bulletin of Electrical Engineering and Informatics, vol. 11, no. 4, pp. 1902–1910, Aug. 2022. DOI: https://doi.org/10.11591/eei.v11i4.3712
Ching-Chih Tsai, Hsiao-Lang Wu, and Ying-Ru Lee, "Intelligent Adaptive Motion Controller Design for Mecanum Wheeled Omnidirectional Robots with Parameter Variations," International Journal of Nonlinear Sciences and Numerical Simulation, vol. 11, no. Supplement, pp. 91–96, Dec. 2010. DOI: https://doi.org/10.1515/IJNSNS.2010.11.S1.91
X. Lu, X. Zhang, G. Zhang, J. Fan, and S. Jia, "Neural network adaptive sliding mode control for omnidirectional vehicle with uncertainties," ISA Transactions, vol. 86, pp. 201–214, Mar. 2019. DOI: https://doi.org/10.1016/j.isatra.2018.10.043
Z. Sun, S. Hu, D. He, W. Zhu, H. Xie, and J. Zheng, "Trajectory-tracking control of Mecanum-wheeled omnidirectional mobile robots using adaptive integral terminal sliding mode," Computers & Electrical Engineering, vol. 96, Dec. 2021, Art. no. 107500. DOI: https://doi.org/10.1016/j.compeleceng.2021.107500
V. Alakshendra and S. S. Chiddarwar, "Adaptive robust control of Mecanum-wheeled mobile robot with uncertainties," Nonlinear Dynamics, vol. 87, no. 4, pp. 2147–2169, Mar. 2017. DOI: https://doi.org/10.1007/s11071-016-3179-1
T. Zhao, X. Zou, and S. Dian, "Fixed-time observer-based adaptive fuzzy tracking control for Mecanum-wheel mobile robots with guaranteed transient performance," Nonlinear Dynamics, vol. 107, no. 1, pp. 921–937, Jan. 2022. DOI: https://doi.org/10.1007/s11071-021-06985-0
H. Medjoubi, A. Yassine, and H. Abdelouahab, "Design and Study of an Adaptive Fuzzy Logic-Based Controller for Wheeled Mobile Robots Implemented in the Leader-Follower Formation Approach," Engineering, Technology & Applied Science Research, vol. 11, no. 2, pp. 6935–6942, Apr. 2021. DOI: https://doi.org/10.48084/etasr.3950
B. Kasmi and A. Hassam, "Comparative Study between Fuzzy Logic and Interval Type-2 Fuzzy Logic Controllers for the Trajectory Planning of a Mobile Robot," Engineering, Technology & Applied Science Research, vol. 11, no. 2, pp. 7011–7017, Apr. 2021. DOI: https://doi.org/10.48084/etasr.4031
T. Nguyen Hong, Thai, H. Trinh Thi Thu, and K. Ly, "Path tracking control for car-like robots by pid controller with time-varying parameters," Science & Technology Development Journal – Engineering and Technology, vol. 5, no. 3, pp. 1642–1650, Dec. 2022.
M. Jiang, L. Chen, Y. Wang, and H. Wu, "Adaptive Backstepping Control for Mecanum-Wheeled Omnidirectional Vehicle Using Neural Networks," IEEJ Transactions on Electrical and Electronic Engineering, vol. 17, no. 3, pp. 378–386, 2022. DOI: https://doi.org/10.1002/tee.23521
D. Wang, W. Wei, Y. Yeboah, Y. Li, and Y. Gao, "A Robust Model Predictive Control Strategy for Trajectory Tracking of Omni-directional Mobile Robots," Journal of Intelligent & Robotic Systems, vol. 98, no. 2, pp. 439–453, May 2020. DOI: https://doi.org/10.1007/s10846-019-01083-1
E. Malayjerdi, H. Kalani, and M. Malayjerdi, "Self-Tuning Fuzzy PID Control of a Four-Mecanum Wheel Omni-directional Mobile Platform," in Iranian Conference on Electrical Engineering (ICEE), Mashhad, Iran, Feb. 2018, pp. 816–820. DOI: https://doi.org/10.1109/ICEE.2018.8472568
G. Cao, X. Zhao, C. Ye, S. Yu, B. Li, and C. Jiang, "Fuzzy adaptive PID control method for multi-mecanum-wheeled mobile robot," Journal of Mechanical Science and Technology, vol. 36, no. 4, pp. 2019–2029, Apr. 2022. DOI: https://doi.org/10.1007/s12206-022-0337-x
M. Boukens and A. Boukabou, "Design of an intelligent optimal neural network-based tracking controller for nonholonomic mobile robot systems," Neurocomputing, vol. 226, pp. 46–57, Feb. 2017. DOI: https://doi.org/10.1016/j.neucom.2016.11.029
D. Janglová, "Neural Networks in Mobile Robot Motion," International Journal of Advanced Robotic Systems, vol. 1, no. 1, Mar. 2004, Art. no. 2. DOI: https://doi.org/10.5772/5615
P. Bozek, Y. L. Karavaev, A. A. Ardentov, and K. S. Yefremov, "Neural network control of a wheeled mobile robot based on optimal trajectories," International Journal of Advanced Robotic Systems, vol. 17, no. 2, Mar. 2020, Art. no. 1729881420916077. DOI: https://doi.org/10.1177/1729881420916077
R. Fierro and F. L. Lewis, "Control of a nonholonomic mobile robot using neural networks," IEEE Transactions on Neural Networks, vol. 9, no. 4, pp. 589–600, Jul. 1998. DOI: https://doi.org/10.1109/72.701173
J. Velagic, N. Osmic, and B. Lacevic, "Neural Network Controller for Mobile Robot Motion Control," International Journal of Intelligent Systems and Technologies, vol. 3, no. 3, pp. 127–132, Jan. 2008.
M. Fouzia, N. Khenfer, and N. E. Boukezzoula, "Robust Adaptive Tracking Control of Manipulator Arms with Fuzzy Neural Networks," Engineering, Technology & Applied Science Research, vol. 10, no. 4, pp. 6131–6141, Aug. 2020. DOI: https://doi.org/10.48084/etasr.3648
N. H. Thai and T. T. K. Ly, "NURBS Curve Trajectory Tracking Control for Differential-Drive Mobile Robot by a Linear State Feedback Controller," in Advances in Engineering Research and Application, 2022, pp. 685–696. DOI: https://doi.org/10.1007/978-3-030-92574-1_71
L. T. K. Trịnh and H. Thien, "Bézier trajectory tracking control of The Omnidirectional Mobile Robot based on a linear time- varying state feedback controller," VNUHCM Journal of Science and Technology Development, vol. 25, no. 2, pp. 2444–2452, Aug. 2022.
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Copyright (c) 2023 Trinh Thi Khanh Ly , Nguyen Thi Thanh, Hoang Thien , Thai Nguyen
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