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Tytuł artykułu

Neural control of a robotic manipulator in contact with a flexible and uncertain environment

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Języki publikacji
EN
Abstrakty
EN
This article presents the synthesis of a neural motion control system of a robot caused by disturbances of constraints limiting the movement, which are the result of flexibility and disturbances of the contact surface. A synthesis of the control law is presented, in which the knowledge of the robot's dynamics and the parameters of a susceptible environment is not required. Moreover, the stability of the system is guaranteed in the case of an inaccurately known surface of the environment. This was achieved by introducing an additional module to the control law in directions normal to the surface of the environment. This additional term can be interpreted as the virtual viscotic resistance and spring force acting on the robot. This approach ensured the self-regulation of the robot’s interaction force with the compliant environment, limiting the impact of the geometrical inaccuracy of the environment.
Słowa kluczowe
Rocznik
Strony
435--444
Opis fizyczny
Bibliogr. 44 poz., rys., tab., wykr.
Twórcy
  • Faculty of Mechanical Engineering and Aeronautics, Department of Applied Mechanics and Robotics Rzeszow University of Technology, al. Powstańców Warszawy 12, 35-959 Rzeszów, Poland
Bibliografia
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  • 4. Gracia L, Solanes JE, Muñoz-Benavent P, Valls Miro J, Perez-Vidal C, Tornero J. Adaptive Sliding Mode Control for Robotic Surface Treatment Using Force Feedback. Mechatronics. 2018;52:102–18.
  • 5. Vukobratovič M, Ekalo Y, Rodič A. How to Apply Hybrid Position/Force Control to Robots Interacting with Dynamic Environment. W: Bianchi G, Guinot JC, Rzymkowski C, Eds. Romansy 14 [Internet]. Vienna: Springer Vienna; 2002 [cited 13 december 2022]. 249–58. Available from: http://link.springer.com/10.1007/978-3-7091-2552-6_27
  • 6. Gierlak P. Position/Force Control of Manipulator in Contact with Flexible Environment. Acta Mech Autom. 2019;13(1):16–22.
  • 7. Gierlak P. Adaptive Position/Force Control of a Robotic Manipulator in Contact with a Flexible and Uncertain Environment. Robotics. 12 2021;10(1):32.
  • 8. Application Manual. Force Control for Machining. Zürich: ABB Robotics; 2011.
  • 9. Burghardt A, Szybicki D, Kurc K, Muszyñska M, Mucha J. Experimental Study of Inconel 718 Surface Treatment by Edge Robotic Deburring with Force Control. Strength Mater. 2017;49(4):594–604.
  • 10. Gierlak P, Szuster M. Adaptive position/force control for robot manipulator in contact with a flexible environment. Robot Auton Syst. 2017;95:80–101.
  • 11. Duan J, Gan Y, Chen M, Dai X. Adaptive variable impedance control for dynamic contact force tracking in uncertain environment. Robot Auton Syst. 2018;102:54–65.
  • 12. Ravandi KA, Khanmirza E, Daneshjou K. Hybrid force/position control of robotic arms manipulating in uncertain environments based on adaptive fuzzy sliding mode control. Appl Soft Comput. 2018;70:864–74.
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  • 14. Wang W, Guo Q, Yang Z, Jiang Y, Xu J. A state-of-the-art review on robotic milling of complex parts with high efficiency and precision. Robot Comput-Integr Manuf. 2023;79:102436.
  • 15. Chen SC, Tung PC. Trajectory planning for automated robotic deburring on an unknown contour. Int J Mach Tools Manuf. 2000;40(7):957–78.
  • 16. Robotic Grinding Process of Turboprop Engine Compressor Blades with Active Selection of Contact Force. Teh Vjesn - Tech Gaz [Internet]. 2022 Feb 15 [cited 2022 Dec 7];29(1). Available from: https://hrcak.srce.hr/269299.
  • 17. Wang Z, Zou L, Luo G, Lv C, Huang Y. A novel selected force controlling method for improving robotic grinding accuracy of complex curved blade. ISA Trans. 2022;129:642–58.
  • 18. Ke X, Yu Y, Li K, Wang T, Zhong B, Wang Z, et al. Review on robot-assisted polishing: Status and future trends. Robot Comput-Integr Manuf. 2023;80:102482.
  • 19. Gierlak P. Hybrid Position/Force Control of the SCORBOT-ER 4pc Manipulator with Neural Compensation of Nonlinearities. In: Rutkowski L, Korytkowski M, Scherer R, Tadeusiewicz R, Zadeh LA, Zurada JM, editors. Artificial Intelligence and Soft Computing [Internet]. Berlin, Heidelberg: Springer Berlin Heidelberg; 2012 [cited 2020 Nov 21]. p. 433–41. (Hutchison D, Kanade T, Kittler J, Kleinberg JM, Mattern F, Mitchell JC, et al., editors. Lecture Notes in Computer Science; vol. 7268). Available from: http://link.springer.com/10.1007/978-3-642-29350-4_52.
  • 20. Gierlak P. Hybrid Position/Force Control in Robotised Machining. Solid State Phenom. 2013;210:192–9.
  • 21. Dwivedy SK, Eberhard P. Dynamic analysis of flexible manipulators, a literature review. Mech Mach Theory. 2006;41(7):749–77.
  • 22. Do TT, Vu VH, Liu Z. Linearization of dynamic equations for vibration and modal analysis of flexible joint manipulators. Mech Mach Theory. 2022;167:104516.
  • 23. Cheng X, Zhang Y, Liu H, Wollherr D, Buss M. Adaptive neural backstepping control for flexible-joint robot manipulator with bounded torque inputs. Neurocomputing. 2021;458:70–86.
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  • 25. Thomsen DK, Søe-Knudsen R, Balling O, Zhang X. Vibration control of industrial robot arms by multi-mode time-varying input shaping. Mech Mach Theory. 2021;155:104072.
  • 26. Cheong J, Youm Y. System mode approach for analysis of horizontal vibration of 3-D two-link flexible manipulators. J Sound Vib. 2003;268(1):49–70.
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  • 28. Wei B. Adaptive Control Design and Stability Analysis of Robotic Manipulators. Actuators. 2018;7(4):89.
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  • 32. Szuster M, Gierlak P. Approximate Dynamic Programming in Tracking Control of a Robotic Manipulator. Int J Adv Robot Syst. 2016;13(1):16.
  • 33. Kumar N, Rani M. Neural network-based hybrid force/position control of constrained reconfigurable manipulators. Neurocomputing. 2021;420:1–14.
  • 34. Yang Z, Peng J, Liu Y. Adaptive neural network force tracking impedance control for uncertain robotic manipulator based on nonlinear velocity observer. Neurocomputing. 2019;331:263–80.
  • 35. de Campos Souza PV. Fuzzy neural networks and neuro-fuzzy networks: A review the main techniques and applications used in the literature. Appl Soft Comput. 2020;92:106275.
  • 36. Refoufi S, Benmahammed K. Control of a manipulator robot by neuro-fuzzy subsets form approach control optimized by the genetic algorithms. ISA Trans. 2018;77:133–45.
  • 37. Vijay M, Jena D. PSO based neuro fuzzy sliding mode control for a robot manipulator. J Electr Syst Inf Technol. 2017;4(1):243–56.
  • 38. Fanaei A, Farrokhi M. ADAPTIVE NEURO-FUZZY CONTROLLER FOR HYBRID POSITION/FORCE CONTROL OF ROBOTIC MANIPULATORS. IFAC Proc Vol. 2005;38(1):127–32.
  • 39. Wang Z, Zou L, Su X, Luo G, Li R, Huang Y. Hybrid force/position control in workspace of robotic manipulator in uncertain environments based on adaptive fuzzy control. Robot Auton Syst. 2021 Nov;145:103870.
  • 40. Garcia-Rodriguez R, Parra-Vega V. Normal and tangent force neuro-fuzzy control of a soft-tip robot with unknown kinematics. Eng Appl Artif Intell. 2017 Oct;65:43–50.
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  • 42. Kumar N, Panwar V, Sukavanam N, Sharma SP, Borm JH. Neural network based hybrid force/position control for robot manipulators. Int J Precis Eng Manuf. 2011;12(3):419–26.
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Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-7303c01a-161f-4add-8332-d3f7320d71ca
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