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Maximum load of flexible joint manipulators using nonlinear controllers

Published online by Cambridge University Press:  27 February 2015

A. H. Korayem ,
M. Irani Rahagi ,
H. Babaee  and
M. H. Korayem
A. H. Korayem
Affiliation:
Robotic Research Laboratory, Center of Excellence in Experimental Solid Mechanics and Dynamics, School of Mechanical Engineering, Iran University of Science and Technology, P.O. Box 18846, Tehran, Iran
M. Irani Rahagi
Affiliation:
Department of Solid Mechanics, Faculty of Mechanical Engineering, University of Kashan, P.O. Box 87317-51167, Kashan, Iran
H. Babaee
Affiliation:
Robotic Research Laboratory, Center of Excellence in Experimental Solid Mechanics and Dynamics, School of Mechanical Engineering, Iran University of Science and Technology, P.O. Box 18846, Tehran, Iran
M. H. Korayem*
Affiliation:
Robotic Research Laboratory, Center of Excellence in Experimental Solid Mechanics and Dynamics, School of Mechanical Engineering, Iran University of Science and Technology, P.O. Box 18846, Tehran, Iran
*
*Corresponding author. E-mail: [email protected]
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Summary

The main innovation of this paper is determining the dynamic load carrying capacity (DLCC) of a flexible joint manipulator (FJM) using a closed form nonlinear optimal control approach. The proposed method is compared with two closed loop nonlinear methods that are usually applied to robotic systems. As a new idea, DLCC of the manipulator is considered as a criterion to compare how well controllers perform point to point mission for the FJMs. The proposed method is compared with feedback linearization (FL) and robust sliding mode control (SMC) methods to show better performance of proposed nonlinear optimal control approach. An optimal controller is designed by solving a nonlinear partial differential equation named the Hamilton–Jacobi–Bellman (HJB) equation. This equation is complicated to solve exactly for complex dynamics so it is solved numerically using an iterative approximation combined with the Galerkin method. In the FL method, angular position, velocity, acceleration and jerk of links are considered as new states to linearize the dynamic equations. In the case of SMC, the dynamic equations of manipulator are changed to the standard form then the Slotine method is used to design the sliding mode controller. Two simulations are performed for a planar and a spatial Puma manipulator and performances of controllers are compared. Finally an experimental test is done on 6R manipulator and the Stereo vision method is used to determine the position and orientation of the end-effector.

Keywords

Flexible joint manipulatorDLCCNonlinear controlOptimal controlPoint to point
Type
Articles
Information
Robotica , Volume 35 , Issue 1 , January 2017 , pp. 119 - 142
Copyright
Copyright © Cambridge University Press 2015 

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References

1. Ozgoli, S. and Taghirad, H. D., “A survey on the control of flexible joint robots,” Asian J. Control 8 (4), 1–15 (2006).Google Scholar
2. Chatlatanagulchai, W. and Meckl, P. H., “Model-independent control of a flexible-joint robot manipulator.” J. Dyn. Syst. Meas. Control 131, 19–29, (2009).Google Scholar
3. Moberg, S. and Hanssen, S., “On Feedback Linearization for Robust Tracking Control of Flexible Joint Robots,” Technical Report from Automatic Control at Linköpings Universitet, 17th IFAC World Congress, Seoul, Korea (2008).CrossRefGoogle Scholar
4. Yoo, S. J., Park, J. B. and Choi, Y. H., “Adaptive output feedback control of flexible-joint robots using neural networks: Dynamic surface design approach,” IEEE Trans. Neural Netw. 19 (10), 1712–1726 (2008).Google Scholar
5. Yoo, S. J., Park, J. B. and Choi, Y. H., “Output feedback dynamic surface control of flexible-joint robots,” Int. J. Control, Autom. Syst. 6 (2), 223–233 (2008).Google Scholar
6. Kwan, C. M. and Yeung, K. S., “Robust adaptive control of revolute flexible-joint manipulators using sliding technique,” Syst. Control Lett. 20, 279–288 (1993).Google Scholar
7. Chaoui, H., Gueaieb, W., Yagoub, M. and Sicard, P., “Hybrid Neural Fuzzy Sliding Mode Control of Flexible-Joint Manipulators with Unknown Dynamics,” IEEE Industrial Electronics IECON, Paris, 4082–4087 (2006).Google Scholar
8. Fateh, M. M., “Nonlinear control of electrical flexible-joint robots,” Nonlinear Dyn. 67 (4), 2549–2559 DOI: 10.1007/s11071-011-0167-3 (2011).CrossRefGoogle Scholar
9. Korayem, M. H., Irani, M. and Rafee Nekoo, S., “Load maximization of flexible joint mechanical manipulator using nonlinear optimal controller,” Acta Astronaut. 69, 458–469 (2011).Google Scholar
10. Beeler, S. C., Tran, H. T. and Banks, H. T., “Feedback control methodologies for nonlinear systems,” J. Optim. Theory Appl. 107 (1), 1–33 (2000).Google Scholar
11. Cimen, T., “State-Dependent Riccati Equation (SDRE) Control: A Survey,” Proceedings of the 17th World Congress the International Federation of Automatic Control, COEX, South Korea (2008) pp. 3762–3775.Google Scholar
12. Korayem, M. H., Irani, M. and Nekoo, S. Rafee, “Analysis of manipulators using SDRE: A closed loop nonlinear optimal control approach,” Sci. Iranica Trans. B 17 (6), 456–467 (2010).Google Scholar
13. Tang, G., Zhao, Y. and Zhang, B., “Optimal output tracking control for nonlinear systems via successive approximation approach,” Nonlinear Anal. 66, 1365–1377 (2007).CrossRefGoogle Scholar
14. Kirk, D., Optimal Control Theory (Prentice-Hall Inc., Englewood Cliffs, NJ, 2007).Google Scholar
15. Korayem, M. H. and Irani, M., “Maximum mynamic load determination of mobile manipulators via nonlinear optimal feedback,” Sci. Iranica Trans. B 17 (2), 121–135 (2010).Google Scholar
16. Wang, C. Y. E., Timoszyk, W. K. and Bobrow, J. E., “Payload maximization for open chained manipulators: Finding weightlifting motions for a puma 762 Robot,” IEEE Trans. Robot. Autom. 17 (2), 218–224 (2001).Google Scholar
17. Korayem, M. H. and Nikoobin, A., “Maximum payload for flexible joint manipulators in point-to-point task using optimal control approach,” Int. J. Adv. Manuf. Technol. 38, 1045–1060 (2008).Google Scholar
18. Korayem, M. H., Firouzy, S. and Heidari, A., “Dynamic Load Carrying Capacity of Mobile-Base Flexible- Link Manipulators: Feedback Linearization Control Approach,” IEEE International Conference on Robotics and Biomimetics, Sanya (2007) pp. 2172–2177.Google Scholar
19. Korayem, M. H., Haghighi, R., Nikoobin, A., Alamdari, A. and Korayem, A. H., “Determining maximum load carrying capacity of flexible link manipulators,” Sci. Iranica Trans. B: Mech. Eng. 16 (5), 440–450 (2009).Google Scholar
20. Korayem, M. H. and Pilechian, A., “Modification of Algorithms for Determination of Dynamic Load Carrying Capacity in Flexible Joint Robots,” International Conference on Robotics and Biomimetics, Kunming, China (2006) pp. 924–929.Google Scholar
21. Chen, W. H., Ballance, D. J. and Gawthrop, P. J., “Optimal control of nonlinear systems: A predictive control approach,” Automatica 39 (4), 633–641 (2003).Google Scholar
22. Beard, R. W. and Mclain, T. W., “Successive galerkin approximation algorithms for nonlinear optimal and robust control,” Int. J. Control 71 (5), 717–743 (1998).Google Scholar
23. Spong, M. W. and Vidyasagar, M., Robot Dynamics and Control, Wiley India Pvt, (1989).Google Scholar