Modeling, simulation and fuzzy control of an anthropomorphic robot arm by using Dymola
Analysis and fuzzy control of an anthropomorphic robot arm on a special trajectory is the subject of this paper. These types of systems are used in cutting operations on materials, joining materials by welding, material handling in remote and dangerous environments, packing of foods, inspection/testing electronic parts or medical products. This robot arm realizes the handling motion on a special trajectory. In this study, the first three links of Mitsubishi RV-2AJ Industrial Robot, are like an anthropomorphic arm, have been modeled and simulated by using Dymola. Kinematic equations have been obtained and mathematical model of this system has been formed by using Lagrange’s Equations. Fuzzy logic controller for the joint angles for the motion trajectory has been designed and the simulation results have been presented at the end of the study.
KeywordsSystems modeling Control Fuzzy logic Anthropomorphic robot arm Simulation
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- Bonert, M., Shu, L. H., & Benhabib, B. (1999). Motion planning for multi-robot assembly systems. Proceedings of the 1999 ASME Design Engineering Technical Conferences, September 12–15, Las Vegas, Nevada, DETC99/DAC-8649.Google Scholar
- Dymola. (2007). Dynasim AB, http://www.dynasim.se.
- Efe, M. O., Dagci, O. H., & Kaynak, O. (1999). Fuzzy control of a 2-DOF direct drive robot arm by using a parameterized T-norm. The Eighth Turkish Symposium on Artificial Intelligence and Neural Networks (TAINN’99), June 23–25, Istanbul, Turkey (pp. 210-218).Google Scholar
- Erbatur, K., Kaynak, O. & Rudas, I. (1997a). Fuzzy identifier based inverse dynamics control for a 3-dof articulated manipulator. Proceedings of IEEE 23rd International Conference on Industrial Electronics, Control and Instrumentation; IECON’97, Nov. 10–14, New Orleans, USA (Vol. 3, pp. 1052–1056).Google Scholar
- Erbatur, K., Kaynak, O., & Rudas, I. (1997b). An inverse dynamics based robot control method using fuzzy identifiers. AIM’97, Conference on Advances in Intelligent Mechatronics, June 16–20, Tokyo, JapanGoogle Scholar
- Spong, M., & Vidyasagar, M. (1989). Robot dynamics and control. John Wiley and Sons.Google Scholar
- Godey Simoes, M. (2007). Introduction to fuzzy control. Tutorial— Colorado School of Mines, Colorado USA. http://egweb.mines.edu/faculty/msimoes/tutorials/Introduction_fuzzy_logic/Intro_Fuzzy_Logic.pdf.
- Granino, A. K. (1995). Neural networks and fuzzy-logic control on personal computers and workstations. London: The MIT Press, ISBN 0262112051.Google Scholar
- Haklidir, M., & Guler, M. (2003). Fuzzy-PD Control of a Two DOF Robot Manipulator (In Turkish), UMTS 2003, Gazi University, Ankara, 4–6 September.Google Scholar
- Haklidir, M., & Tasdelen, I. (2006). Modeling and simulation of an antropomorphic robot arm by using Dymola. 5th International Symposium on Intelligent Manufacturing Systems IMS 2006, Sakarya, Turkey, 29–31 May.Google Scholar
- Hirzenger, M., & Fischer, M. (1999). Advances in robotics. International Journal of Robotics Research.Google Scholar
- Horstkotte, E. (2007). Fuzzy logic overview. http://www.austinlinks.com/Fuzzy/expert-systems.html.
- Koivo, A. J. (1989). Fundamentals for control of robotic manipulators. John Wiley and Sons.Google Scholar
- Mitsubishi Electric Shell Sheet. (2000). RV-1A, RV-2AJ industrial robots new concepts for better solutions. USA.Google Scholar
- Nedungadi, A. (1992). A fuzzy robot controller-hardware implementation. IEEE International Conference on Fuzzy Systems pp. 1325–1331.Google Scholar
- Paul R. (1981) Robotic manipulators. MIT Press, USAGoogle Scholar
- Sciavicco, L., & Siciliano, B. (2000). Modelling and control of robot manipulators. Springer.Google Scholar
- Wang L.X. (1994) Adapt. fuzzy systems. and controller.-design and stability. analysis. PTR Prentice Hall, Englewood CliffsGoogle Scholar