References
- 1. Barata J.C.A., Hussein M.S. (2012), The Moore–Penrose pseudoinverse: A tutorial review of the theory, Brazilian Journal of Physics, 42(1-2), 146–165.
- 2. Birglen L., Schlicht T. (2018), A statistical review of industrial robotic grippers, Robotics and Computer-Integrated Manufacturing, 49, 88–97.
- 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.
- 5. Canudas de Wit C.A., Siciliano B., Bastin G. (Eds.) (1996), Theory of robot control, New York, Springer.
- 6. Capisani L. M., Ferrara A. (2012), Trajectory planning and second-order sliding mode motion/interaction control for robot manipulators in unknown environments, IEEE Transactions on Industrial Electronics, 59(8), 3189–3198.
- 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 SCORBOT-ER 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., Perez-Vidal 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.
- 21. Lewis F.L., Liu K., Yesildirek A. (1995), Neural Net Robot Controller with Guaranteed Tracking Performance, IEEE Transactions on Neural Networks, 6(3), 701–715.
- 22. Lotz M., Bruhm H., Czinki A. (2014), An new force control strategy improving the force control capabilities of standard industrial robots, Journal of Mechanics Engineering and Automation, Vol. 4, 276–283.
- 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.
- 25. Pao Y.-H., Park G.-H., Sobajic D.J. (1994), Learning and generalization characteristics of the random vector functional-link net, Neurocomputing, 6(2), 163–180.
- 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.