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Optimal sliding mode control design based on the state-dependent Riccati equation for cooperative manipulators to increase dynamic load carrying capacity

Published online by Cambridge University Press:  09 October 2018

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, Tehran 1684613114, Iran. E-mails: habibnejad1371@yahoo.com, saerafee@yahoo.com
S. R. Nekoo
Affiliation:
Robotic Research Laboratory, Center of Excellence in Experimental Solid Mechanics and Dynamics, School of Mechanical Engineering, Iran University of Science and Technology, Tehran 1684613114, Iran. E-mails: habibnejad1371@yahoo.com, saerafee@yahoo.com
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, Tehran 1684613114, Iran. E-mails: habibnejad1371@yahoo.com, saerafee@yahoo.com
*
*Corresponding author: E-mail: hkorayem@iust.ac.ir

Summary

Cooperative manipulators have uncertainties in their structure; therefore, an optimal sliding mode control method is derived from a combination of the sliding mode control (SMC) and the state-dependent Riccati equation (SDRE) technique. This proposed combination is applied to a class of non-linear closed-loop systems. One of the distinguished features of this control method is its robustness toward uncertainty. Due to the lack of optimality in the SMC method, in this paper, a robust and optimal method is presented by considering the SDRE in design of the sliding surface. Due to the fact that cooperative manipulators have been used for carrying loads, the percentage of load distributions between each manipulator has been derived to increase the dynamic load carrying capacity (DLCC). The proposed control structure is implemented on a Scout robot with two manipulators in cooperative mode, theoretically and practically using LabVIEW software; and the results were compared by considering the uncertainty in its structure. In comparison with the SDRE, the proposed method increased the DLCC almost 10% in the Scout case.

Type
Articles
Copyright
Copyright © Cambridge University Press 2018 

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References

1. Kokkinis, T., “Dynamic hybrid control of cooperating robots by nonlinear inversion,” Robotics and Autonomous Systems 5 (4), 359368 (1989).Google Scholar
2. Yun, X., Kumar, R. V., Sarkar, N. and Paljug, E., “Control of multiple arm systems with rolling constraints,” Technical Report, MS-CIS-91-79 (1991).Google Scholar
3. Wen, J. T. and Delgado, K. K., “Motion and force control of multiple robotic manipulators,” Automatica 28 (4), 729743 (1992).Google Scholar
4. Gao, W. B. and Xiao, D., “Tracking tasks of massive objects by multiple robot systems with non-firm grasping,” Mechatronics 3 (6), 727746 (1993).Google Scholar
5. Li, C. J., “Coordinated motion control of multi-arm robot systems with optimal load distribution,” Systems & Control Letters 15 (3), 237245 (1990).Google Scholar
6. Lin, S. T. and Tsai, H. C., “Impedance control with on-line neural network compensator for dual-arm robots,” Journal of Intelligent and Robotic Systems 18 (1), 87104 (1997).Google Scholar
7. Liu, J. S. and Chen, S. L., “Robust hybrid control of constrained robot manipulators via decomposed equations,” Journal of Intelligent and Robotic Systems 23 (1), 4570 (1998).Google Scholar
8. Zhao, J. and Bai, S. X., “Load distribution and joint trajectory planning of coordinated manipulation for two redundant robots,” Mechanism and Machine Theory 34 (8), 11551170 (1999).Google Scholar
9. Jing, Z. and Bai, S. X., “The study of coordinated manipulation of two redundant robots with elastic joints,” Mechanism and Machine Theory 35 (7), 895909 (2000).Google Scholar
10. Subbarao, K., Verma, A. and Junkins, J. L., “Model Reference Adaptive Control of Constrained Cooperative Manipulators,” Proceedings of the IEEE International Conference on Control Applications, Mexico City (Sep. 2001) pp. 553–558.Google Scholar
11. Li, Z., Ge, S. S. and Wang, Z., “Robust adaptive control of coordinated multiple mobile manipulators,” Mechatronics 18 (5), 239250 (2008).Google Scholar
12. Ghasemi, A. and Keshmiri, M., “Performance Assessment of a Decentralized Controller for Cooperative Manipulators; Numerical and Experimental Study,” Proceedings of the 6th International Symposium on Mechatronics and its Applications, Sharjah, UAE (Mar. 2009) pp. 1–6.Google Scholar
13. Yagiz, N., Hacioglu, Y. and Arslan, Y. Z., “Load transportation by dual arm robot using sliding mode control,” Journal of Mechanical Science and Technology 24 (5), 11771184 (2010).Google Scholar
14. Korayem, M. H., Jalali, M. and Tourajizadeh, H., “Dynamic load carrying capacity of spatial cable suspended robot: Sliding mode control approach,” International Journal of Advanced Design and Manufacturing Technology 5 (3), 7381 (2012).Google Scholar
15. Gonçalves, Chinelato Caio Igor and Martins-Filho, Luiz de Siqueira, “Control of cooperative mobile manipulators transporting a payload,” Proceedings of the Mechanical Engineering (COBEM), International Congress of. ABCM (2013).Google Scholar
16. Suzuki, S., Furuta, K. and Pan, Y., “State-dependent sliding-sector VS-control and application to swing-up control of pendulum,” Proceedings of the 42nd IEEE Conference on Decision and Control Maui, Hawaii USA (Dec. 2003).Google Scholar
17. Suzuki, S., Pan, Y., Furuta, K. and Hatakeyama, S., “Vs-control with time-varying sliding sector design and application to pendulum,” Asian Journal of Control 6 (3), 307316 (Sep., 2004).Google Scholar
18. Korayem, A. H., Nekoo, S. R. and Korayem, M. H., “Sliding mode control design based on the state-dependent Riccati equation: Theoretical and experimental implementation,” International Journal of Control 91, 0130 (2018).Google Scholar
19. Cimen, T., “Survey of state-dependent Riccati equation in nonlinear optimal feedback control synthesis,” Journal of Guidance, Control, and Dynamics 35 (4), 10251047 (2012).Google Scholar
20. Korayem, M. H. and Nekoo, S. R., “Finite-time state-dependent Riccati equation for time-varying nonaffine systems: Rigid and flexible joint manipulator control,” ISA Transactions 54, 125144 (2015).Google Scholar
21. Jean-Jacques, Slotine E. and Weiping, L., Applied Nonlinear Control, vol. 199, no. 1 (Englewood Cliffs, NJ: Prentice-Hall, 1991).Google Scholar
22. Korayem, M. H., Irani, M. and Nekoo, S. Rafee, “Load maximization of flexible joint mechanical manipulator using nonlinear optimal controller,” Acta Astronautica 69 (7), 458469 (2011).Google Scholar
23. Banks, H. T., Lewis, B. M. and Tran, H. T., “Nonlinear feedback controllers and compensators: A state-dependent Riccati equation approach,” Computational Optimization and Applications 37 (2), 177218 (2007).Google Scholar
24. Korayem, M. H., Irani, M. and Nekoo, S. R.. “Analysis of manipulators using SDRE: A closed loop nonlinear optimal control approach,” Scientia Iranica. Transaction B, Mechanical Engineering 17 (6), 456467 (2010).Google Scholar
25. Korayem, M. H. and Nekoo, S. R., “The SDRE control of mobile base cooperative manipulators: Collision free path planning and moving obstacle avoidance,” Robotics and Autonomous Systems 86, 86105 (2016).Google Scholar
26. Korayem, M. H. and Nekoo, S. R., “Controller design of cooperative manipulators using state-dependent Riccati equation,” Robotica 36, 132 (2017).Google Scholar