Adaptive Neural Fuzzy Observer Design for Flexible Robot Joint Control
Keywords:
Adaptive Neural Fuzzy Observer, Flexible Robot Joint Control, system identification
Abstract
Industrial robotic arms are one of the most important and practical areas of robotics that have the ability to operate in hazardous, unpredictable situations and in cases where humans themselves are unable to perform. However, the high nonlinearity of the robot arm descriptive equations has complicated the design of suitable controllers for different operating conditions. Therefore, their proper performance depends on the use of robust and flexible control methods. In this research, the modeling and control of a robotic arm is investigated. First, the dynamic modeling of the nonlinear robot system in the form of state space extracted using the Lagrange-Euler method and then a new neural-fuzzy control strategy is proposed for tracking the reference points and improving the robustness of the robot. The proposed controller is adaptive and has two separate fuzzy Neural Identifier and Identifier networks that are trained online and offline. Therefore, changing the working position and parameters of robot modeling are not affect the robust, optimal and adaptive performance of the controller. For comparative purposes, the results of applying the proposed control for the robotic arm are compared with the results of applying the PID control for the robot. The simulation results performed with the MATLAB software show well the effectiveness of the control method in damping the oscillations at angles of the robot joints as well as the movement of the robot tool in the Cartesian coordinates.
References
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21. Cong Wang, Yu Zhao, Yubei Chen, Masayoshi Tomizuka. Nonparametric statistical learning control of robot manipulators for trajectory or contour tracking. Robotics and Computer-Integrated Manufacturing, Volume 35, October 2015, Pages 96-103
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23. Fatima Zahra Baghli, Larbi El bakkali, Yassine Lakha Multi-input Multi-output Fuzzy Logic Controller for Complex System: Application on Two-links Manipulator .Procedia Technology, Volume 19, 2015, Pages 607-614.
24. Zoltán Forgó, Ferenc Tolvaly-Roşca. Analytical and Numerical Model of Low DOF Manipulators. Procedia Technology, Volume 19, 2015, Pages 40-47
25. Mirosław Galicki. An adaptive non-linear constraint control of mobile manipulators Mechanism and Machine Theory, Volume 88, June 2015, Pages 63-85
26. Guaraci Bastos Jr., Olivier Brüls. An integrated control-structure design for manipulators with flexible linksIFAC-PapersOnLine, Volume 48, Issue 11, 2015, Pages 156-161
27. Bin Xue, Linghui Yang, Jun Wu, Zhexu Liu, Zhi Xiong. Model study on the traceability of the accuracy of an industrial manipulator to an external measurement system based on the Banach fixed-point theorem Mechanism and Machine Theory, Volume 92, October 2015, Pages 51-63
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29. Wang, Dan, and Jie Huang. "Adaptive neural network control for a class of uncertain nonlinear systems in pure-feedback form." Automatica 38, no. 8 (2002): 1365-1372.
30. Lee, Tong Heng, and Christopher John Harris. Adaptive neural network control of robotic manipulators. Vol. 19. World Scientific, 1998.
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33. De Silva, Clarence W. Intelligent control: fuzzy logic applications. CRC press, 2018.
34. Rahimi, H. N., and M. Nazemizadeh. "Dynamic analysis and intelligent control techniques for flexible manipulators: a review." Advanced Robotics 28, no. 2 (2014): 63-76.
35. Cintula, Petr, Christian Fermüller, and Carles Noguera. "Handbook of mathematical fuzzy logic-volume 3." (2015).
2. Cong Wang, Yu Zhao, Yubei Chen, Masayoshi Tomizuka. Nonparametric statistical learning control of robot manipulators for trajectory or contour tracking. Robotics and Computer-Integrated Manufacturing, Volume 35, October 2015, Pages 96-103
3. Zoran Miljković, Marko Mitić, Mihailo Lazarević, Bojan Babić . Neural network Reinforcement Learning for visual control of robot manipulators.Expert Systems with Applications, Volume 40, Issue 5, April 2013, Pages 1721-1736.
4. Fatima Zahra Baghli, Larbi El bakkali, Yassine Lakha Multi-input Multi-output Fuzzy Logic Controller for Complex System: Application on Two-links Manipulator .Procedia Technology, Volume 19, 2015, Pages 607-614.
5. Zoltán Forgó, Ferenc Tolvaly-Roşca. Analytical and Numerical Model of Low DOF Manipulators. Procedia Technology, Volume 19, 2015, Pages 40-47
6. Mirosław Galicki. An adaptive non-linear constraint control of mobile manipulators Mechanism and Machine Theory, Volume 88, June 2015, Pages 63-85
7. Guaraci Bastos Jr., Olivier Brüls. An integrated control-structure design for manipulators with flexible linksIFAC-PapersOnLine, Volume 48, Issue 11, 2015, Pages 156-161
8. Bin Xue, Linghui Yang, Jun Wu, Zhexu Liu, Zhi Xiong. Model study on the traceability of the accuracy of an industrial manipulator to an external measurement system based on the Banach fixed-point theorem Mechanism and Machine Theory, Volume 92, October 2015, Pages 51-63
9. Liu, Xin, Chenguang Yang, Zhiguang Chen, Min Wang, and Chun-Yi Su. "Neuro-adaptive observer based control of flexible joint robot." Neurocomputing 275 (2018): 73-82.
10. Miranda-Colorado, R. and Moreno-Valenzuela, J., 2018. Experimental parameter identification of flexible joint robot manipulators. Robotica, 36(3), pp.313-332.
11. Siciliano, B., Sciavicco, L., Villani, L. and Oriolo, G., 2010. Robotics: modelling, planning and control. Springer Science & Business Media.
12. Tokhi, M. Osman, and Abul KM Azad, eds. Flexible robot manipulators: modelling, simulation and control. Vol. 68. Iet, 2008.
13. Murray, Richard M. A mathematical introduction to robotic manipulation. CRC press, 2017.
14. Bassily, D., C. Georgoulas, J. Guettler, T. Linner, and T. Bock. "Intuitive and adaptive robotic arm manipulation using the leap motion controller." In ISR/robotik 2014; 41st international symposium on robotics; proceedings of, pp. 1-7. VDE, 2014.
15. Mondal, Sanjoy, and Chitralekha Mahanta. "Adaptive second order terminal sliding mode controller for robotic manipulators." Journal of the Franklin Institute 351, no. 4 (2014): 2356-2377.
16. Murray, Richard M. A mathematical introduction to robotic manipulation. CRC press, 2017.
17. Ju, Zhangfeng, Chenguang Yang, and Hongbin Ma. "Kinematics modeling and experimental verification of baxter robot." In Control Conference (CCC), 2014 33rd Chinese, pp. 8518-8523. IEEE, 2014.
18. Siradjuddin, Indrazno, Laxmidhar Behera, T. Martin McGinnity, and Sonya Coleman. "Image-based visual servoing of a 7-DOF robot manipulator using an adaptive distributed fuzzy PD controller." IEEE/ASME Transactions On Mechatronics 19, no. 2 (2014): 512-523.
19. Marchese, Andrew D., Konrad Komorowski, Cagdas D. Onal, and Daniela Rus. "Design and control of a soft and continuously deformable 2d robotic manipulation system." (2014).
20. Sung-Hwan Kim, Young June Shin, Kyung-Soo Kim, Soohyun Kim. Design and control of robot manipulator with a distributed actuation mechanism. Mechatronics, Volume 24, Issue 8, December 2014, Pages 1223-1230
21. Cong Wang, Yu Zhao, Yubei Chen, Masayoshi Tomizuka. Nonparametric statistical learning control of robot manipulators for trajectory or contour tracking. Robotics and Computer-Integrated Manufacturing, Volume 35, October 2015, Pages 96-103
22. Zoran Miljković, Marko Mitić, Mihailo Lazarević, Bojan Babić . Neural network Reinforcement Learning for visual control of robot manipulators.Expert Systems with Applications, Volume 40, Issue 5, April 2013, Pages 1721-1736.
23. Fatima Zahra Baghli, Larbi El bakkali, Yassine Lakha Multi-input Multi-output Fuzzy Logic Controller for Complex System: Application on Two-links Manipulator .Procedia Technology, Volume 19, 2015, Pages 607-614.
24. Zoltán Forgó, Ferenc Tolvaly-Roşca. Analytical and Numerical Model of Low DOF Manipulators. Procedia Technology, Volume 19, 2015, Pages 40-47
25. Mirosław Galicki. An adaptive non-linear constraint control of mobile manipulators Mechanism and Machine Theory, Volume 88, June 2015, Pages 63-85
26. Guaraci Bastos Jr., Olivier Brüls. An integrated control-structure design for manipulators with flexible linksIFAC-PapersOnLine, Volume 48, Issue 11, 2015, Pages 156-161
27. Bin Xue, Linghui Yang, Jun Wu, Zhexu Liu, Zhi Xiong. Model study on the traceability of the accuracy of an industrial manipulator to an external measurement system based on the Banach fixed-point theorem Mechanism and Machine Theory, Volume 92, October 2015, Pages 51-63
28. Ge, Shuzhi Sam, Chang C. Hang, Tong H. Lee, and Tao Zhang. Stable adaptive neural network control. Vol. 13. Springer Science & Business Media, 2013.
29. Wang, Dan, and Jie Huang. "Adaptive neural network control for a class of uncertain nonlinear systems in pure-feedback form." Automatica 38, no. 8 (2002): 1365-1372.
30. Lee, Tong Heng, and Christopher John Harris. Adaptive neural network control of robotic manipulators. Vol. 19. World Scientific, 1998.
31. Dai, Shi-Lu, Cong Wang, and Min Wang. "Dynamic learning from adaptive neural network control of a class of nonaffine nonlinear systems." IEEE transactions on neural networks and learning systems 25, no. 1 (2014): 111-123.
32. Zhu, Quanmin, and Ahmad Taher Azar, eds. Complex system modelling and control through intelligent soft computations. Germany: Springer, 2015.
33. De Silva, Clarence W. Intelligent control: fuzzy logic applications. CRC press, 2018.
34. Rahimi, H. N., and M. Nazemizadeh. "Dynamic analysis and intelligent control techniques for flexible manipulators: a review." Advanced Robotics 28, no. 2 (2014): 63-76.
35. Cintula, Petr, Christian Fermüller, and Carles Noguera. "Handbook of mathematical fuzzy logic-volume 3." (2015).
Published
2019-06-01
How to Cite
Rahimi Loraki, A., & Ashourian, M. (2019). Adaptive Neural Fuzzy Observer Design for Flexible Robot Joint Control. Majlesi Journal of Mechatronic Systems, 8(2), 39-54. Retrieved from https://ms.majlesi.info/index.php/ms/article/view/432
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