Abstract
Mobile manipulator robots that combine mobile platforms and robotic arms have been attracting considerable attention in recent years. In this paper, the motion control problem of the mobile manipulator system is considered with the following proposed control strategy. Firstly, a decoupled dynamic model is created to increase operation safety and to reduce complexity such that the independent controllers can be designed for the mobile system and the manipulator system. Subsequently, an effective reference trajectory generator is proposed to guide the mobile manipulator systems to the proper position for the manipulation being able to grip the target. Thereafter, the fuzzy controllers are designed for the mobile system to eliminate the tracking error and for the manipulator system to accomplish the gripping mission. The stability of the mobile manipulator control system can be guaranteed by the Lyapunov theory. Finally, the numerical simulations are given to demonstrate the effectiveness of the proposed approach. From the simulation results, it can be seen that this paper as well as other compared methods have good control response in case of small controller gain, in which the convergence time are about 4–5 s. However, by increasing the controller gain to improve the control response, the other methods will make the system unstable or the controller output will produce a large amount of chattering. The proposed controller in this paper can not only decrease the convergence time, from 5 to 3 s, but also provide a smooth control response.
Similar content being viewed by others
Availability of data and materials
Enquiries about data availability should be directed to the authors.
References
Ahmad S, Zhang H, Liu G (2013) Multiple working mode control of door-opening with a mobile modular and reconfigurable robot. IEEE/ASME Trans Mechatron 18(3):833–844
Baraldi A, Blonda P (1999) A survey of fuzzy clustering algorithms for pattern recognition. I. IEEE Trans Syst Man Cybern B (Cybern) 29(6):778–785
Chang CW, Tao CW (2019) A simplified implementation of hierarchical fuzzy systems. Soft Comput 23(12):4471–4481
Chang CW, Yang CY, Tao CW (2017) Interval fuzzy sliding-mode formation controller design. Soft Comput 21(14):4045–4054
Colucci G, Tagliavini L, Botta A et al (2023) Decoupled motion planning of a mobile manipulator for precision agriculture. Robotica 41(6):1872–1887
Fierro R, Lewis F (1995) Control of a nonholonomic mobile robot: backstepping kinematics into dynamics. In: Proceedings of 1995 34th IEEE conference on decision and control, vol 4, pp 3805–3810
Galicki M (2015) An adaptive non-linear constraint control of mobile manipulators. Mech Mach Theory 88:63–85
Garud KS, Jayaraj S, Lee MY (2021) A review on modeling of solar photovoltaic systems using artificial neural networks, fuzzy logic, genetic algorithm and hybrid models. Int J Energy Res 45(1):6–35
Ghavidel HF (2020) A modeling error-based adaptive fuzzy observer approach with input saturation analysis for robust control of affine and non-affine systems. Soft Comput 24(3):1717–1735
Hamner B, Koterba S, Shi J et al (2010) An autonomous mobile manipulator for assembly tasks. Auton Robots 28(1):131–149
Haviland J, Sünderhauf N, Corke P (2022) A holistic approach to reactive mobile manipulation. IEEE Robot Autom Lett 7(2):3122–3129
Holmberg R, Khatib O (2000) Development and control of a holonomic mobile robot for mobile manipulation tasks. Int J Robot Res 19(11):1066–1074
Jiao J, Cao Z, Gu N et al (2017) Transportation by multiple mobile manipulators in unknown environments with obstacles. IEEE Syst J 11(4):2894–2904
Kazeminezhad M, Etemad-Shahidi A, Mousavi S (2005) Application of fuzzy inference system in the prediction of wave parameters. Ocean Eng 32(14):1709–1725
Khan MA (2020) Design and control of a robotic system based on mobile robots and manipulator arms for picking in logistics warehouses. PhD thesis, Normandie Université
Lata S, Mehfuz S, Urooj S, et al. (2020) Fuzzy clustering algorithm for enhancing reliability and network lifetime of wireless sensor networks. IEEE Access 8:66,013–66,024
Lehner P, Brunner S, Dömel A et al (2018) Mobile manipulation for planetary exploration. In: 2018 IEEE aerospace conference, pp 1–11
Mezui JMLN, Moungomo JBM, Kibouka GR et al (2023) SolidWorks/Simscape multibody co-simulation of the dynamic model of a mobile manipulator system. Int J Res Eng Sci Manag 6(1):79–87
Mishra S, Londhe P, Mohan S et al (2018) Robust task-space motion control of a mobile manipulator using a nonlinear control with an uncertainty estimator. Comput Electr Eng 67:729–740
Mishra S, Mohan S, Vishvakarma SK (2021) Performance investigations of an improved backstepping operational-space position tracking control of a mobile manipulator. Def Sci J 71(4):436–447
Naranjo R, Santos M, Garmendia L (2021) A convolution-based distance measure for fuzzy singletons and its application in a pattern recognition problem. Soft Comput 28:51–63
Peng J, Yang Z, Wang Y et al (2019) Robust adaptive motion/force control scheme for crawler-type mobile manipulator with nonholonomic constraint based on sliding mode control approach. ISA Trans 92:166–179
Raja K (2023) Python-based fuzzy logic in automatic washer control system. Soft Comput 27(10):6159–6185
Ruspini EH, Bezdek JC, Keller JM (2019) Fuzzy clustering: a historical perspective. IEEE Comput Intell Mag 14(1):45–55
Seo IS, Han SI (2018) Dual closed-loop sliding mode control for a decoupled three-link wheeled mobile manipulator. ISA Trans 80:322–335
Wang L, Langari R (1996) Complex systems modeling via fuzzy logic. IEEE Trans Syst Man Cybern B (Cybern) 26(1):100–106
Yamamoto Y, Yun X (1994) Coordinating locomotion and manipulation of a mobile manipulator. IEEE Trans Autom Control 39(6):1326–1332
Yi G, Mao J, Wang Y et al (2018) Adaptive tracking control of nonholonomic mobile manipulators using recurrent neural networks. Int J Control Autom Syst 16(3):1390–1403
Funding
This research was supported by the Ministry of Science and Technology (MOST) of Taiwan under the contract MOST 110-2221-E-130-012.
Author information
Authors and Affiliations
Contributions
All authors have contributed equally to this manuscript.
Corresponding author
Ethics declarations
Competing Interests
The authors have no relevant financial or non-financial interests to disclose.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Chang, CW., Tao, CW. Design of a fuzzy trajectory tracking controller for a mobile manipulator system. Soft Comput 28, 5197–5211 (2024). https://doi.org/10.1007/s00500-023-09298-z
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-023-09298-z