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Keywords

Four mecanum wheels mobile robot Model reference adaptive control PID Trajectory tracking, Mobile robot control

Document Type

Research Paper

Abstract

This paper presents a practical investigation into the design and implementation of a multi-stage controller, which integrates a Model Reference Adaptive Controller (MRAC) with a Proportional-Integral-Derivative (PID) controller for trajectory tracking control in a Four Mecanum Wheels Mobile Robot (FMWMR). The MRAC adaptively updates the control parameters based on the difference between the actual and desired behaviors of the mobile robot. In contrast, the PID controller manages the angular velocities of the wheels. The mobile robot has been designed and constructed with two holders to facilitate the transportation of logistical items and constrain the weight to the center of the mobile robot to reduce the effect of the inertia force. The distinctive motion characteristics of the FMWMR are elucidated by explaining its kinematic and dynamic models. The control signals for the FMWMR motors are derived from the output of the multi-stage controller. The mobile robot operates through serial communication between MATLAB/Simulink and Arduino hardware. Experimental results demonstrating the control of the (FMWMR) by the proposed controller as it follows a Sine-Spiral trajectory are presented, highlighting the performance of the multi-stage controller monitoring the behavior of velocities and motor torques and illustrating the real-time adaptive variation of the MRAC control parameters.

References

H. D. Quang, T. L. Tran, T. L. Manh, C. N. Manh, T. N. Nhu, N. B. Duy, Design a nonlinear MPC controller for autonomous mobile robot navigation system based on ROS, International Journal of Mechanical Engineering and Robotics Research, 11 (2022) 379-388. https://doi.org/10.18178/ijmerr.11.6.379-388 H. A. Najim, I. S. Kareem, and W. E. Abdul-Lateef, Omnidirectional mobile robot with navigation using SLAM, Eng. Technol. J., 41 (2023) 196-202. https://doi.org/10.30684/etj.v41i1.2252 Z. Yuan, Y. Tian, Y. Yin, S. Wang, J. Liu, L. Wu, Trajectory tracking control of a four mecanum wheeled mobile platform: an extended state observer‐based sliding mode approach, IET Control Theory Appl., 14 (2020) 415-426. http://dx.doi.org/10.1049/iet-cta.2018.6127 M. Alfiyan and R. D. Puriyanto, Mecanum 4 omni wheel directional robot design system using PID Method, J. Fuzzy Syst. Control, 1 (2023) 6-13. http://dx.doi.org/10.59247/jfsc.v1i1.27 G. Cao, G. Cao, X. Zhao, C. Ye, S. Yu, B. Li and C. Jiang, Fuzzy adaptive PID control method for multi-mecanum-wheeled mobile robot,  J. Mech. Sci. Technol., 36 (2022) 2019-2029. http://dx.doi.org/10.1007/s12206-022-0337-x R. Chotikunnan, P. Chotikunnan, N. Thongpance, T. Puttasakul, V. Pititheeraphab, M. Sangworasil, Application of PID control system in mecanum wheelchair, International Journal of Membrance Science Technology, 10, 2023, 3519-3529. http://dx.doi.org/10.15379/ijmst.v10i3.3395 N. H. Thai, and T.T. K. Ly, Trajectory tracking control for mecanum wheel mobile robot by time-varying parameter PID controller, Bull. Electr. Eng. Inf., 11 (2022) 1902-1910. http://dx.doi.org/10.11591/eei.v11i4.3712 S. Allahyari, H. Rahmanei, and S. A. A. Moosavian, Multi-aspect Optimal Sliding Mode Controller for a Mecanum Wheeled Robot, 11th RSI International Conference on Robotics and Mechatronics, 2023. http://dx.doi.org/10.1109/ICRoM60803.2023.10412554 T. Guo, Trajectory tracking control of the Mecanum wheeled mobile robot based on the SMC methods, Proceedings, Sixth International Conference on Electromechanical Control Technology and Transportation, 12081, 2022. https://doi.org/10.1117/12.2624231 Z. Li, Z. Li, Z. Sun, B. Chen, A Nonsingular Fast Terminal Sliding Mode Control Scheme for Mecanum-Wheels Omnidirectional Mobile Robots, 2023 5th International Conference on Robotics, Intelligent Control and Artificial Intelligence, 2023, 50-56. http://doi.org/10.1109/RICAI60863.2023.10489050 J. Mowlaee, A. Sharghi, and R. A. Togh, Design of fixed-time terminal sliding mode control for robot with mecanum wheels, J. Nonlinear Sys. Electr. Eng., 8 (2022) 19-37. Z. Sun, S. Hu, D. He, W. Zhu, H. Xie, J. Zheng, Trajectory-tracking control of Mecanum-wheeled omnidirectional mobile robots using adaptive integral terminal sliding mode, Comput. Electr. Eng., 96 (2021) 107500. https://doi.org/10.1016/j.compeleceng.2021.107500 Z. Sun, H. Xie, J.Zheng, Z. Man and D. He, Path-following control of Mecanum-wheels omnidirectional mobile robots using nonsingular terminal sliding mode, Mech. Syst. Signal Process., 147 (2021) 107128. https://doi.org/10.1016/j.ymssp.2020.107128 D. Wang,  Y. Gao, W. Wei, Q.Yu, Y. Wei, W. Li, Z. Fan,  Sliding mode observer-based model predictive tracking control for Mecanum-wheeled mobile robot, ISA Trans., 151 (2024) 51-61. https://doi.org/10.1016/j.isatra.2024.05.050 P. S. Yadav, V. Agrawal, J. C.Mohanta, M. D.Faiyaz Ahmed, A robust sliding mode control of mecanum wheel-chair for trajectory tracking,  Mater. Today: Proc.,  56 (2022)  623-630. http://dx.doi.org/10.1016/j.matpr.2021.12.398 M. Crenganiș, R. E. Breaz, S. G. Racz, C. E. Girjob, C. M. Biris, et al., Fuzzy Logic-Based Driving Decision for an Omnidirectional Mobile Robot Using a Simulink dynamic model, Appl. Sci., 14 (2024) 3058. https://doi.org/10.3390/app14073058 Felizardo C., Oscar C., Prometeo C.-A., Omnidirectional four wheel mobile robot control with a type-2 fuzzy logic behavior-based strategy, Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms: Theory and Applications, 49-62, 2020. H.-C. Huang and J.-J. Xu, Evolutionary machine learning for optimal polar-space fuzzy control of cyber-physical mecanum vehicles, Electronics, 9 (2020) 1945. https://doi.org/10.3390/electronics9111945 D. N. Minh, H. D. Quang, N. D. Phuong, T. N. Manh, and D. Nam Bui, An Adaptive fuzzy dynamic surface control tracking algorithm for mecanum wheeled mobile robot, Int. J. Mech. Eng. Rob. Res., 12 (2023) 354-361. https://doi.org/10.18178/ijmerr B. Anil, M. Pandey, and S. Gajbhiye, Finite-Time Trajectory Tracking of a Four wheeled Mecanum Mobile Robot, arXiv preprint arXiv:2410.06762, (2024) 1-16. https://doi.org/10.48550/arXiv.2410.06762 Z. Sun, S. Hu, N. Li, D. He, Trajectory-following control of mecanum-wheeled AGV using fuzzy nonsingular terminal sliding mode, 2020 4th CAA International Conference on Vehicular Control and Intelligence, 2020, 342-347. https://doi.org/10.1109/CVCI51460.2020.9338561 T. T. Thuong, V. T. Ha, V. Q. Vinh, N. T. Hien, Design adaptive fuzzy dynamic surface controller combined with DWA algorithm in motion control for mecanum wheels omnidirectional mobile robots, International Conference on Engineering Research and Applications, 943, 2023. https://doi.org/10.1007/978-3-031-62238-0_36 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 Dyn., 107 (2022) 921-937. https://doi.org/10.1007/s11071-021-06985-0 T. T. K. Ly, N. T. Thanh, H. Thien, T. Nguyen, A Neural network controller design for the mecanum wheel mobile robot, Eng. Technol. Appl. Sci. Res., 13 (2023) 10541-10547. https://doi.org/10.48084/etasr.5761 M. Szeremeta and M. Szuster, Neural tracking control of a four-wheeled mobile robot with mecanum wheels, Appl. Sci., 12 (2022) 5322. https://doi.org/10.3390/app12115322 Q. Jia, C. Chang, S. Liu, L. Zhang, S. Zhang, Motion control of omnidirectional mobile robot based on fuzzy PID, 2019 Chinese Control And Decision Conference, 2019, 5149-5154. https://doi.org/10.1109/CCDC.2019.8833047 M. J. Mohamed, and M. Abbas, Design a fuzzy PID controller for trajectory tracking of mobile robot, Eng. Technol. J., 36 A1 (2018) 100-110. https://doi.org/10.30684/etj.2018.136785 A. Alsharkawi, M. Al-Fetyani, E. M. Ijaabo, H. Khasawneh, Adaptive Neuro-Fuzzy Inference System for a Three-Wheeled Omnidirectional Mobile Robot, 2020 3rd International Conference on Applied Engineering, 2020, 1-6. http://doi.org/10.1109/ICAE50557.2020.9350379 A. Al-Araji and N. Yousif, A Cognitive nonlinear trajectory tracking controller design for wheeled mobile robot based on hybrid bees-PSO algorithm, Eng. Technol. J., 35A (2017) 609-616. https://doi.org/10.30684/etj.2017.131978 M. J. Mohamed, and M.K. Hamza, Design PID neural network controller for trajectory tracking of differential drive mobile robot based on PSO, Eng. Technol. J., 37 A (2019) 574-583. https://doi.org/10.30684/etj.37.12A.12 N. Zijie, Z. peng, Y. Cui. Z. Jun, PID control of an omnidirectional mobile platform based on an RBF neural network controller, Ind. Robot. Int. J. Robot. Res. Appl., 49 (2022) 65-75. http://dx.doi.org/10.1108/IR-01-2021-0015 H. Fu, Y. Li, Y. Wang, Z. Zhang, Omnidirectional mobile robot active disturbance rejection control2018 IEEE International Conference on Mechatronics and Automation, 2018, 227 - 232. https://doi.org/10.1109/ICMA.2018.8484414 K. D. H. Thi, M. C. Nguyen, H. T. Vo, V. M. Tran, D. D. Nguyen, A. D. Bui, Trajectory tracking control for four-wheeled omnidirectional mobile robot using Backstepping technique aggregated with sliding mode control 2019 First International Symposium on Instrumentation, Control, Artificial Intelligence, and Robotics, 2019, 131-134. https://doi.org/10.1109/ICA-SYMP.2019.8646041 D. Wang, W. Wei. Y. Yebah, Y. li, Y. Ga, A robust model predictive control strategy for trajectory tracking of omni-directional mobile robots, J. Intell. Rob. Sys., 98 (2020) 439-453. https://doi.org/10.1007/s10846-019-01083-1 S. F. Hasana and H. M. Alwan, Modeling and Control of Wheeled Mobile Robot with Four Mecanum Wheels, Eng. Technol. J., 39 (2021) 779-789. https://doi.org/10.30684/etj.v39i5A.1926 F. Dong, D. Jin, X. Zhao, J. Han, Adaptive robust constraint following control for omnidirectional mobile robot: An indirect approach, IEEE Access, 9 (2021) 8877-8887. https://doi.org/10.1109/ACCESS.2021.3049913 A. Madhloom, F.A. Raheem, and A.R. Kareem, A modified Kalman filter-based mobile robot position measurement using an accelerometer and wheels encoder, Eng. Technol. J., 40 (2022) 267-274. https://doi.org/10.30684/etj.v40i1.2082 R. J. Salman, H. M. Alwan, M. A. Yousif, A. M. Abdullah, Computed torque-NN-GWO dynamic hybrid control of manipulator robotic arm, Iraq. J. Comput. Commun. Control Sys. Eng., 24 (2024) 16. https://doi.org/10.33103/uot.ijccce.24.2.1 E. Maulana, M. A. Muslim, and V. Hendrayawan, Inverse kinematic implementation of four-wheels mecanum drive mobile robot using stepper motors, in 2015 international seminar on intelligent technology and its applications, 2015. http://dx.doi.org/10.1109/ISITIA.2015.7219952 Klancar, G., Andrej Z, Sašo B., Igor Š., Wheeled mobile robotics: from fundamentals towards autonomous systems, 2017. S. F. Hasan, and H.M. Alwan, Enhancing Tilt-Integral-Derivative Controller to Motion Control of Holonomic Wheeled Mobile Robot by Using New Hybrid Approach, IOP Conference Series: Materials Science and Engineering, 1094 (2021) 012097. http://dx.doi.org/10.1088/1757-899X/1094/1/012097 I. Moreno-Caireta, E. Celaya, and L. Ros, Model predictive control for a Mecanum-wheeled robot navigating among obstacles, IFAC-Pap., 54 (2021) 119-125. http://dx.doi.org/10.1016/j.ifacol.2021.08.533 V. Alakshendra, and S.S. Chiddarwar, A robust adaptive control of mecanum wheel mobile robot: simulation and experimental validation, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2016, 5606-5611. http://dx.doi.org/10.1109/IROS.2016.7759824 Tzafestas, S.G., Introduction to mobile robot control, Athens, Greece, 2013. Jagan, N., Control systems, BS Publications Hyderabad, 2008. Ogata, K., Modern control engineering, 680 E Colorado Bvld, Suite 180, Pasadena, CA 91101, 2020. Yucelen, T., Model Reference Adaptive Control, in Wiley Encyclopedia of Electrical and Electronics Engineering, John Wiley & Sons, In, 1-13, 2019. Socha, L., Linearization methods for stochastic dynamic systems, Springer Science & Business Media, Vol. 730. 2007. Eugene, L., Kevin, W. and Howe, D., Robust and adaptive control with aerospace applications, England: Springer-Verlag London, 2013. A. Albattat, B. Gruenwald, and T. Yucelen, Design and analysis of adaptive control systems over wireless networks, J. Dyn. Syst. Meas. Contr. , 139 (2017) 074501. T. Yucelen, and W.M. Haddad, Low-frequency learning and fast adaptation in model reference adaptive control, IEEE Trans. Autom. Control, 58 (2012) 1080-1085. https://doi.org/10.1109/TAC.2012.2218667 Nguyen, N.T., Model-reference adaptive control, Springer, 2018. X. Lu, X. Zhang, G. Zhang, S. Jia, Design of adaptive sliding mode controller for four-Mecanum wheel mobile robot, 2018 37th Chinese Control Conference, 2018, 3983-3987. https://doi.org/10.23919/ChiCC.2018.8483388 Y.-H., Chen, Nonlinear Adaptive Optimal control design and implementation for trajectory tracking of four-wheeled mecanum mobile robots, Mathematics, 12 (2024) 4013. https://doi.org/10.3390/math12244013

Highlights

A multi-stage controller combining MRAC and PID was designed to control the trajectory tracking of a 4-mecanum wheel robot A mobile robot with unique holders was manufactured to transport logistics items MATLAB/Simulink was programmed and connected to Arduino to send control commands and receive feedback The robot's performance was evaluated using the designed control algorithm

DOI

10.30684/etj.2025.155232.1852

First Page

641

Last Page

658

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