Hostname: page-component-8448b6f56d-sxzjt Total loading time: 0 Render date: 2024-04-20T02:51:07.529Z Has data issue: false hasContentIssue false

A novel self-adaptive modified bat fuzzy sliding mode control of robot manipulator in presence of uncertainties in task space

Published online by Cambridge University Press:  22 May 2014

Mohammad Veysi
Affiliation:
Department of Electrical Engineering, Shahid Sattari Aeronautical University of Science and Technology, Tehran, Iran
Mohammad Reza Soltanpour
Affiliation:
Department of Electrical Engineering, Shahid Sattari Aeronautical University of Science and Technology, Tehran, Iran
Mohammad Hassan Khooban*
Affiliation:
Young Researchers Club, Garmsar Branch, Islamic Azad University, Garmsar, Iran
*
*Corresponding author. E-mail: dr.mohammadhassan.khooban@ieee.org

Summary

In this paper, an optimal fuzzy sliding mode controller has been designed for controlling the end-effector position in the task space. In the proposed control, feedback linearization method, sliding mode control, first-order fuzzy TSK system and optimization algorithm are utilized. In the proposed controller, a novel heuristic algorithm namely self-adaptive modified bat algorithm (SAMBA) is employed. To achieve an optimal performance, the parameters of the proposed controller as well as the input membership functions are optimized by SAMBA simultaneously. In this method, the bounds of structural and non-structural uncertainties are reduced by using feedback linearization method, and to overcome the remaining uncertainties, sliding mode control is employed. Mathematical proof demonstrates that the closed loop system with the proposed control has global asymptotic stability. The presence of sliding mode control gives rise to the adverse phenomenon of chattering in the end-effector position tracking in the task space. Subsequently, to prevent the occurrence of chattering in control input, a first-order TSK fuzzy approximator is utilized. Finally, to determine the fuzzy sliding mode controller coefficients, the optimization algorithm of Self-Adaptive Modified Bat is employed. To investigate the performance of the proposed control, a two-degree-of-freedom manipulator is used as a case study. The simulation results indicate the favorable performance of the proposed method.

Type
Articles
Copyright
Copyright © Cambridge University Press 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Yu, W. and Moreno-Armendariz, M. A., “Robust visual servoing of robot manipulators with neuro compensation,” J. Franklin Inst. 342, 824838 (2005).Google Scholar
2. Liu, C., Cheah, C. C. and Slotine, J.-J. E., “Adaptive Jacobian tracking control of rigid-link electrically driven robots based on visual taskspace information,” J. Autom. 42, 14911501 (2006).Google Scholar
3. Valenzuela, J. M. and Hernández, L. G., “Operational space trajectory tracking control of robot manipulators endowed with a primary controller of synthetic joint velocity,” ISA Trans. 50 (1), 131140 (2011).Google Scholar
4. Guzman, S. P., Valenzuela, J. M. and Santibáñez, V.: “Adaptive neural network motion control of manipulators with experimental evaluations,” Sci. World J. Vo. 2014, 113 (2014).Google Scholar
5. Qu, Z. and Dawson, D. M., Robust Tracking Control of Robot Manipulators (IEEE Press, Inc., New York, USA 1996).Google Scholar
6. Sage, H. G., De Mathelin, M. F. and Ostertag, E., “Robust control of robot manipulators: A survey,” Int. J. Control 72 (6), 14981522 (1999).Google Scholar
7. Dixon, W. E., “Adaptive Regulation of Amplitude Limited Robot Manipulators with Uncertain Kinematics and Dynamics,” Proceedings of American Control Conference, Boston, MA (2004) pp: 38443939.Google Scholar
8. Cheah, C. C., Hirano, M., Kawamura, S. and Arimoto, S.. : “Approximate Jacobian control with task-space damping for robot manipulators,” J. IEEE Trans. Autom. Control 49 (5), 752757 (2004).Google Scholar
9. Cheah, C. C., Hirano, M., Kawamura, S. and Arimoto, S., “Approximate Jacobian control for robots with uncertain kinematics and dynamics,” J. IEEE Trans. Robot. Autom. 19 (4), 692702 (2003).Google Scholar
10. Soltanpour, M. R., Fateh, M. M. and Ahmadifard, A. R., “Nonlinear tracking control on a robot manipulator in the task space with uncertain dynamics,” J. Appl. Sci. Asian Netw. Sci. Inf. 8 (23), 43974403 (2008).Google Scholar
11. Soltanpour, M. R. and Siahi, M., “Robust control of robot manipulator in task space,” Int. J. Appl. Comput. Math. 8 (2), 227238 (2009).Google Scholar
12. Fateh, M. M. and Soltanpour, M. R., “Robust task-space control of robot manipulators under imperfect transformation of control space,” Int. J. Innovative Comput. Inf. Control 5 (11)(A), 39493960 (2009).Google Scholar
13. Soltanpour, M. R. and Shafiei, S. E., “Robust backstepping control of robot manipulator in task space with uncertainties in kinematics and dynamics,” Electron. Electr. Eng. J. Autom. Robot. 96 (8), 7580 (2009).Google Scholar
14. Soltanpour, M. R. and Fateh, M. M., “Adaptive robust control of robot manipulators in the task space under uncertainties,” Aust. J. Basic Appl. Sci. 1 (3), 308322 (2009).Google Scholar
15. Soltanpour, M. R. and Shafiei, S. E., “Robust adaptive control of manipulators in the task space by dynamical partitioning approach,” Int. J. Electron. Electr. Eng. J. Autom. Robot. 101 (5), 7378 (2010).Google Scholar
16. Soltanpour, M. R., Zolfaghari, B., Soltani, M. and Khooban, M. H., “Fuzzy sliding mode control design for a class of nonlinear systems with structured and unstructured uncertainties,” Int. J. Innovative Comput. Inf. Control 9 (7), 27132726 (2013).Google Scholar
17. Shafiei, S. E. and Soltanpour, M. R., “Robust neural network control of electrically driven robot manipulator using backstepping approach,” Int. J. Adv. Robot. Syst. 6 (4), 285292 (2009).Google Scholar
18. Veysi, M. and Soltanpour, M. R., “Eliminating chattering phenomenon in sliding mode control of robot manipulators in the joint space using fuzzy logic,” J. Solid Fluid Mech. Shahrood University of Technology 2 (3), 4554 (2012).Google Scholar
19. Craig, J. J., Introduction to Robotics: Mechanics and Control (Addision-Wesley, 2005).Google Scholar
20. Khatib, O., Dynamic Control of Manipulators in Operational Space. Sixth IFTOMM Congress on Theory of Machines and Mechanisms, New Dehli (1983).Google Scholar
21. Gilbert, Strang.. : Linear algebra and its application, Massachusetts Institute of Technology, saunders College Publishing (1986).Google Scholar
22. Slotine, J. J. E. and Li, W., Applied Nonlinear Control (Prentice-Hall 1991).Google Scholar
23. X.-S. Yang, Gandomi, A. H. and Algorithm, B.: “A novel approach for global engineering optimization,” Eng. Comput. 29 (5), 464483 (2012).Google Scholar
24. Baziar, A., Kavoosi-Fard, A. and Zare, J.. “A novel self adaptive modification approach based on bat algorithm for optimal management of renewable MG,” J. Intell. Learn. Syst. Appl. 5 (1) (2013).Google Scholar
25. Niknam, T., Kavousifard, A. and Seifi, A., “Distribution feeder reconfiguration considering fuel cell/wind/photovoltaic power Plants,” Renew. Energy 37 (1), 213225 (2011).Google Scholar
26. Khooban, M. H., Soltanpour, M. R., Nazari, D. and Esfahani, Z., “Optimal intelligent control for HVAC systems,” J. Power Technol. 92 (3), 192200 (2012).Google Scholar
27. Khooban, M. H., Alfi, A. and Abadi, D. N. M., “Teaching–learning-based optimal interval type-2 fuzzy PID controller design: a nonholonomic wheeled mobile robots,” Robotica 31, 10591071. doi:10.1017/S0263574713000283, (2013).Google Scholar
28. Khooban, M. H., Alfi, A. and Abadi, D. N. M., “Control of a class of nonlinear uncertain chaotic systems via an optimal type-2 fuzzy PID controller,” IET Sci. Meas. Technol. 7 (1), 5058 (2013).Google Scholar
29. Soltanpour, M. R., Khooban, M. H. and Soltani, M.. “Robust fuzzy sliding mode control for tracking the robot manipulator in joint space and in presence of uncertainties,” Robotica, available on CJO2013. doi:10.1017/S0263574713000702, (2013).Google Scholar
30. Khooban, M. H., “Design an intelligent proportional-derivative (PD) feedback linearization control for nonholonomic-wheeled mobile robot,” J. Intell. Fuzzy Syst. 26 (4), 18331843. Doi: 10.3233/IFS-130863, (2014).Google Scholar
31. Niknam, T., Khooban, M. H., Kavousifard, A. and Soltanpour, M. R., “An optimal type II fuzzy sliding mode control design for a class of nonlinear systems,” Nonlinear Dyn. 75 (1–2), 7383 (2014).Google Scholar