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Optimal regulation of a cable robot in presence of obstacle using optimal adaptive feedback linearization approach

Published online by Cambridge University Press:  21 March 2014

M. H. Korayem ,
H. Tourajizadeh ,
A. Zehfroosh  and
A. H. Korayem
M. H. Korayem*
Affiliation:
Robotics Research Laboratory, Center of Excellence in Experimental Solid Mechanics and Dynamics, School of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran
H. Tourajizadeh
Affiliation:
Robotics Research Laboratory, Center of Excellence in Experimental Solid Mechanics and Dynamics, School of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran
A. Zehfroosh
Affiliation:
Robotics Research Laboratory, Center of Excellence in Experimental Solid Mechanics and Dynamics, School of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran
A. H. Korayem
Affiliation:
Robotics Research Laboratory, Center of Excellence in Experimental Solid Mechanics and Dynamics, School of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran
*
*Corresponding author. E-mail: hkorayem@iust.ac.ir
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Summary

Optimal path planning of a closed loop cable robot, between two predefined points in presence of obstacles is the goal of this paper. This target is met by proposing a new method of optimal regulation for non linear systems while Dynamic Load Carrying Capacity (DLCC) of the robot is supposed as the related cost function. Feedback linearization is used to linearize the system while Linear Quadratic Regulator (LQR) is employed to optimize the DLCC of the system based on torque and error constraints. Obstacle avoidance for both the end-effector and cables is also considered by the aid of designing an adaptive local obstacle avoidance controller. As a result of linearized nature of the proposed optimal regulation and obstacle avoidance, fast calculation for real time applications is possible. Therefore, formulation of the optimal feedback linearization, together with calculating the DLCC of the robot based on the presented constraints is derived. Finally, a simulation study is performed to study the optimal dynamics and also the maximum DLCC of the cable robot in presence of obstacles. Simulation results are eventually compared with experimental tests conducted on IUST Cable Suspended Robot (ICaSbot) to verify the validity and efficiency of the proposed optimal controllers.

Keywords

Cable suspended robotFeedback linearizationOptimal regulationDynamic load capacityAdaptive local obstacle avoidance
Type
Articles
Information
Robotica , Volume 33 , Issue 4 , May 2015 , pp. 933 - 952
Copyright
Copyright © Cambridge University Press 2014 

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References

1. Korayem, M. H., Najafi, Kh. and Bamdad, M., “Synthesis of cable driven robots' dynamic motion with maximum load carrying capacities: Iterative linear programming approach,” Trans. B: Mech. Eng. [Sharif University of Technology], 17 (3), 229–239 (Jun. 2010)Google Scholar
2. Korayem, M. H., Bamdad, M. and Bayat, S., “Optimal Trajectory Planning with Dynamic Load Carrying Capacity of Cable-suspended Manipulator,” IEEE International Symposium Mechatronics and its Applications (ISMA) (2009) pp 1–6.Google Scholar
3. Li, Y. and Chen, X., “Mobile Robot Navigation Using Particle Swarm Optimization and Adaptive NN,” Lecture Notes in Computer Science, 3612 (PART III) (2005) pp. 628–631.Google Scholar
4. Fang, Sh., Franitza, D., Torlo, M., Bekes, F. and Hiller, M., “Motion control of a tendon-based parallel manipulator using optimal tension distribution,” IEEE/ASME Trans. Mechatronics 9 (3), (Sept. 2004) pp. 561–568.Google Scholar
5. Gholami, P., Aref, M. and Taghirad, H. D., “On the Control of the KNTU CDRPM: A Cable Driven Redundant Parallel Manipulator,” IEEE/RSJ International Conference on Intelligent Robots and Systems, Acropolis Convention Center, Nice, France (Sep. 22–26, 2008).Google Scholar
6. Zhang, Y., Agrawal, S. K., Hemanshu, P. R. and Piovoso, M. J., “Optimal Control using State Dependent Riccati Equation (SDRE) for a Flexible Cable Transporter System with Arbitrarily Varying Lengths,” Proceedings of the 2005 IEEE Conference on Control Applications, Canada (Aug. 28–31, 2005).Google Scholar
7. Korayem, M. H., Azimi, V., Nikoobin, A. and Boroujeni, Z., “Maximum load-carrying capacity of autonomous mobile manipulator in an environment with obstacle considering tip over stability,” J. Adv. Manuf. Technol. 46 (5–8), 811–829 (2010).Google Scholar
8. Koren, Y. and Borenstein, J., “Potential Field Methods and Their Inherent Limitations for Mobile Robot Navigation,” Proceedings of the International Conference on Robotics and Automation (1999) pp. 1398–1404.Google Scholar
9. Kim, D. H. and Shin, S., “Local path planning using a new artificial potential function composition and its analytical design guidelines,” Adv. Robot. 20 (1), 115–135 (2006)Google Scholar
10. Yang, D. and Hong, S., “A roadmap construction algorithm for mobile robot path planning using skeleton maps,” Adv. Robot. 21 (1–2), 51–63 (2007).Google Scholar
11. Kubota, T., Hashimoto, H. and Harashima, F., “Path searching for a mobile robot by local planning,” Adv. Robot. 5 (4), 397–410 (1991).Google Scholar
12. Forest, C., Frakes, D. and Singhose, W., “Input-Shaped Control of Gantry Cranes: Simulation and Curriculum Development,” ASME DETC 18th Biennial Conference on Mechanical Vibration and Noise, Pittsburgh, PA (2001).Google Scholar
13. Alp, A. B., Cable Suspended Parallel Robots, MSc. Thesis (Mechanical Engineering Department, University of Delaware, 2001)Google Scholar
14. Korayem, M. H. and Tourajizadeh, H., “Maximum DLCC of spatial cable robot for a predefined trajectory within the workspace using closed loop optimal control approach,” J. Intell. Robot. Syst. 63 (1), 75–99 (2011).Google Scholar
15. Lin, F., Robust Control Design an Optimal Control Approach, (Wayne State University, USA and Tongji University, China, Published by John Wiley & Sons Ltd, England, 2007).Google Scholar
16. Korayem, M. H., Bamdad, M., Tourajizadeh, H., Shafiee, H., Zehtab, R. M. and Iranpour, A., “Development of ICaSbot a cable suspended robot with 6 DOFs,” Arab. J. Sci. Eng. 38, 1131–1149 (2013).Google Scholar