Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
The paper presents the issue position/force control of a manipulator in contact with the flexible environment. It consists of the realisation of manipulator end-effector motion on the environment surface with the simultaneous appliance of desired pressure on the surface. The paper considers the case of a flexible environment when its deformation occurs under the pressure, which has a significant influence on the control purpose realisation. The article presents the model of the controlled system and the problem of tracking control with the use of neural networks. The control algorithm includes contact surface flexibility in order to improve control quality. The article presents the results of numerical simulations, which indicate the correctness of the applied control law.
Słowa kluczowe
Czasopismo
Rocznik
Tom
Strony
16--22
Opis fizyczny
Bibliogr. 32 poz., rys., tab., wykr.
Twórcy
autor
- 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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- 3. Burghardt A., Kurc K., Szybicki D., Muszyńska M., Nawrocki J. (2017a), Software for the robot-operated inspection station for engine guide vanes taking into consideration the geometric variability of parts, Tehnicki Vjesnik-Technical Gazette, 24(2), 349–353.
- 4. Burghardt A., Szybicki D., Kurc K., Muszyńska M., Mucha J. (2017b), Experimental Study of Inconel 718 Surface Treatment by Edge Robotic Deburring with Force Control, Strength Mater, 49(4), 594–604.
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- 7. Denkena B., Bergmann B., Lepper T. (2017), Design and optimization of a machining robot, Procedia Manufacturing, 14, 89– 96.
- 8. Duan J., Gan Y., Chen M., Dai X. (2018), Adaptive variable impedance control for dynamic contact force tracking in uncertain environment, Robotics and Autonomous Systems, 102, 54–65.
- 9. Galushkin A. I. (2007). Neural networks theory, Springer Science & Business Media.
- 10. Gierlak P. (2012), Hybrid Position/Force Control of the SCORBOTER 4pc Manipulator with Neural Compensation of Nonlinearities, in: Rutkowski L., Korytkowski M., Scherer R., Tadeusiewicz R., Zadeh L.A., Zurada J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2012. Lecture Notes in Computer Science, 7268, 433–441, Springer, Berlin, Heidelberg.
- 11. Gierlak P. (2014), Hybrid position/force control in robotised machining, Solid State Phenomena, 210, 192–199.
- 12. Gierlak P. (2018), Combined strategy for control of interaction force between manipulator and flexible environment, Journal of Control Engineering and Applied Informatics, 20(2), 64–75.
- 13. Gierlak P., Szuster M. (2017), Adaptive position/force control for robot manipulator in contact with a flexible environment, Robotics and Autonomous Systems, 95, 80–101.
- 14. Gracia L., Solanes J.E., Muñoz-Benavent P., Miro J.V., PerezVidal C., Tornero J. (2018), Adaptive Sliding Mode Control for Robotic Surface Treatment Using Force Feedback, Mechatronics, 52, 102–118.
- 15. Hashemi S.M., Gürcüoğlu U., Werner H. (2013), Interaction control of an industrial manipulator using LPV techniques, Mechatronics, 23(6), 689–699.
- 16. Hendzel Z., Burghardt A., Gierlak P., Szuster M. (2014), Conventional and fuzzy force control in robotised machining, Solid State Phenomena, 210, 178–185.
- 17. Hertz J., Krogh A., Palmer R.G. (1991), Introduction to the theory of neural computation, Boston, Addison-Wesley Longman Publishing Co.
- 18. Iglesias I., Sebastián M.A., Are, J.E. (2015), Overview of the state of robotic machining: Current situation and future potential, Procedia engineering, 132, 911–917.
- 19. Jafari A., Ryu J.H. (2016), Independent force and position control for cooperating manipulators handling an unknown object and interacting with an unknown environment, Journal of the Franklin Institute, 353(4), 857–875.
- 20. Kumar N., Panwar V., Sukavanam N., Sharma S.P., Borm J.-H. (2011), Neural network based hybrid force/position control for robot manipulators, International Journal of Precision Engineering and Manufacturing, 12(3), 419–426.
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- 23. Mendes N., Neto P. (2015), Indirect adaptive fuzzy control for industrial robots: a solution for contact applications, Expert Systems with Applications, 4 (22), 8929–8935.
- 24. Narendra K., Annaswamy A.M. (1987), A new adaptive law for robust adaptation without persistent excitation, IEEE Transactions on Automatic Control, 32(2), 134–145.
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- 26. Pliego-Jiménez J., Arteaga-Pérez M.A. (2015), Adaptive position/force control for robot manipulators in contact with a rigid surface with uncertain parameters, European Journal of Control, 22, 1–12.
- 27. Polycarpou M.M., Ioannu P.A. (1991), Identification and control using neural network models: design and stability analysis, California, University of Southern California.
- 28. Ravandi A. K., Khanmirza E., Daneshjou K. (2018), Hybrid force/position control of robotic arms manipulating in uncertain environments based on adaptive fuzzy sliding mode control. Applied Soft Computing, 70, 864–874.
- 29. Tian F., Lv C., Li Z., Liu G. (2016), Modeling and control of robotic automatic polishing for curved surfaces, CIRP Journal of Manufacturing Science and Technology, 14, 55–64.
- 30. Vukobratovič M., Ekalo Y., Rodič A. (2002), How to Apply Hybrid Position/Force Control to Robots Interacting with Dynamic Environment, In: Bianchi G., Guinot J.-C., Rzymkowski C. (Eds.) Romansy, 14, 249–258, Vienna.
- 31. Zhu D., Luo S., Yang L., Chen W., Yan S., Ding H. (2015), On energetic assessment of cutting mechanisms in robot-assisted belt grinding of titanium alloys, Tribology International, 90, 55–59.
- 32. Żylski W., Gierlak P. (2010), Verification of Multilayer Neural-Net Controller in Manipulator Tracking Control, Solid State Phenomena, 164, 99–104.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-7d91b11f-1373-4f4a-b394-d4ce92a235ce