Have a personal or library account? Click to login

Neural Control of a Robotic Manipulator in Contact with a Flexible and Uncertain Environment

Open Access
|Jul 2023

References

  1. Iglesias I, Sebastián MA, Ares JE. Overview of the State of Robotic Machining: Current Situation and Future Potential. Procedia Eng. 2015;132:911–7.
  2. Tian F, Lv C, Li Z, Liu G. Modeling and control of robotic automatic polishing for curved surfaces. CIRP J Manuf Sci Technol. 2016;14:55–64.
  3. Denkena B, Bergmann B, Lepper T. Design and optimization of a machining robot. Procedia Manuf. 2017;14:89–96.
  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.
  13. Guo K, Zhang Y, Sun J. Towards stable milling: Principle and application of active contact robotic milling. Int J Mach Tools Manuf. 2022;182:103952.
  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.
  24. Endo T, Kawasaki H. Bending moment-based force control of flexible arm under gravity. Mech Mach Theory. 2014;79:217–29.
  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.
  27. Lewis FL, Jagannathan S, Yeşildirek A. Neural network control of robot manipulators and nonlinear systems. London: Taylor & Francis; 1999. 442 p. (The Taylor & Francis systems and control book series).
  28. Wei B. Adaptive Control Design and Stability Analysis of Robotic Manipulators. Actuators. 2018;7(4):89.
  29. Gupta P, Sinha NK. Intelligent control of robotic manipulators: experimental study using neural networks. Mechatronics. 2000;10(1–2):289–305.
  30. Yin X, Pan L, Cai S. Robust adaptive fuzzy sliding mode trajectory tracking control for serial robotic manipulators. Robot Comput-Integr Manuf. 2021;72:101884.
  31. Pham DT, Fahmy AA. NEURO-FUZZY MODELLING AND CONTROL OF ROBOT MANIPULATORS FOR TRAJECTORY TRACKING. IFAC Proc Vol. 2005;38(1):170–5.
  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.
  41. Pao YH, Park GH, Sobajic DJ. Learning and generalization characteristics of the random vector functional-link net. Neurocomputing. 1994;6(2):163–80.
  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.
  43. Lewis FL, Liu K, Yesildirek A. Neural net robot controller with guaranteed tracking performance. IEEE Trans Neural Netw. 1995;6(3):703–15.
  44. Obal P, Gierlak P. EGM Toolbox—Interface for Controlling ABB Robots in Simulink. Sensors. 2021;21(22):7463.
DOI: https://doi.org/10.2478/ama-2023-0050 | Journal eISSN: 2300-5319 | Journal ISSN: 1898-4088
Language: English
Page range: 435 - 444
Submitted on: Dec 13, 2022
Accepted on: Mar 27, 2023
Published on: Jul 15, 2023
Published by: Bialystok University of Technology
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2023 Piotr Gierlak, published by Bialystok University of Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.