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Design of a fuzzy trajectory tracking controller for a mobile manipulator system

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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.

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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

    Article  Google Scholar 

  • 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

    Article  CAS  PubMed  Google Scholar 

  • Chang CW, Tao CW (2019) A simplified implementation of hierarchical fuzzy systems. Soft Comput 23(12):4471–4481

    Article  Google Scholar 

  • Chang CW, Yang CY, Tao CW (2017) Interval fuzzy sliding-mode formation controller design. Soft Comput 21(14):4045–4054

    Article  Google Scholar 

  • Colucci G, Tagliavini L, Botta A et al (2023) Decoupled motion planning of a mobile manipulator for precision agriculture. Robotica 41(6):1872–1887

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Hamner B, Koterba S, Shi J et al (2010) An autonomous mobile manipulator for assembly tasks. Auton Robots 28(1):131–149

    Article  Google Scholar 

  • Haviland J, Sünderhauf N, Corke P (2022) A holistic approach to reactive mobile manipulation. IEEE Robot Autom Lett 7(2):3122–3129

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  ADS  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  PubMed  Google Scholar 

  • Raja K (2023) Python-based fuzzy logic in automatic washer control system. Soft Comput 27(10):6159–6185

    Article  Google Scholar 

  • Ruspini EH, Bezdek JC, Keller JM (2019) Fuzzy clustering: a historical perspective. IEEE Comput Intell Mag 14(1):45–55

    Article  Google Scholar 

  • Seo IS, Han SI (2018) Dual closed-loop sliding mode control for a decoupled three-link wheeled mobile manipulator. ISA Trans 80:322–335

    Article  PubMed  Google Scholar 

  • Wang L, Langari R (1996) Complex systems modeling via fuzzy logic. IEEE Trans Syst Man Cybern B (Cybern) 26(1):100–106

    Article  CAS  PubMed  Google Scholar 

  • Yamamoto Y, Yun X (1994) Coordinating locomotion and manipulation of a mobile manipulator. IEEE Trans Autom Control 39(6):1326–1332

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

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Funding

This research was supported by the Ministry of Science and Technology (MOST) of Taiwan under the contract MOST 110-2221-E-130-012.

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Correspondence to Chin-Wang Tao.

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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

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