INFORMATION CHANGE THE WORLD

International Journal of Information Technology and Computer Science(IJITCS)

ISSN: 2074-9007 (Print), ISSN: 2074-9015 (Online)

Published By: MECS Press

IJITCS Vol.7, No.2, Jan. 2015

Intelligent Adaptive Gain Backstepping Technique

Full Text (PDF, 582KB), PP.60-67


Views:62   Downloads:1

Author(s)

Sara Heidari, Ali Shahcheraghi, Kamran Heidari, Samaneh Zahmatkesh, Farzin Piltan

Index Terms

Continuum Robot Manipulator, Robust Backstepping Controller, Fuzzy Logic System, Adaptive Methodology

Abstract

In this research, intelligent adaptive backstepping control is presented as robust control for continuum robot. The first objective in this research is design a Proportional-Derivative (PD) fuzzy system to compensate the system model uncertainties. The second objective is focused on the design tuning gain adaptive methodology according to high quality partly nonlinear methodology. Conventional backstepping controller is one of the important robust controllers especially to control of continuum robot manipulator. The fuzzy controller is used in this method to system compensation. In real time to increase the system robust fuzzy logic theory is applied to backstepping controller. To approximate a time-varying nonlinear dynamic system, a fuzzy system requires a large amount of fuzzy rule base. The adaptive laws in this algorithm are designed based on the Lyapunov stability theorem. This method is applied to continuum robot manipulator to have the best performance.

Cite This Paper

Sara Heidari, Ali Shahcheraghi, Kamran Heidari, Samaneh Zahmatkesh, Farzin Piltan,"Intelligent Adaptive Gain Backstepping Technique", IJITCS, vol.7, no.2, pp.60-67, 2015. DOI: 10.5815/ijitcs.2015.02.08

Reference

[1]T. R. Kurfess, Robotics and automation handbook: CRC, 2005.

[2]J. J. E. Slotine and W. Li, Applied nonlinear control vol. 461: Prentice hall Englewood Cliffs, NJ, 1991.

[3]L. Cheng, et al., "Multi-agent based adaptive consensus control for multiple manipulators with kinematic uncertainties," 2008, pp. 189-194.

[4]J. J. D'Azzo, et al., Linear control system analysis and design with MATLAB: CRC, 2003.

[5]B. Siciliano and O. Khatib, Springer handbook of robotics: Springer-Verlag New York Inc, 2008.

[6]I. Boiko, et al., "Analysis of chattering in systems with second-order sliding modes," IEEE Transactions on Automatic Control, vol. 52, pp. 2085-2102, 2007.

[7]J. Wang, et al., "Indirect adaptive fuzzy sliding mode control: Part I: fuzzy switching," Fuzzy Sets and Systems, vol. 122, pp. 21-30, 2001.

[8]F. Piltan, et al., "Artificial Control of Nonlinear Second Order Systems Based on AFGSMC," Australian Journal of Basic and Applied Sciences, 5(6), pp. 509-522, 2011.

[9]V. Utkin, "Variable structure systems with sliding modes," Automatic Control, IEEE Transactions on, vol. 22, pp. 212-222, 2002.

[10]R. A. DeCarlo, et al., "Variable structure control of nonlinear multivariable systems: a tutorial," Proceedings of the IEEE, vol. 76, pp. 212-232, 2002.

[11]K. D. Young, et al., "A control engineer's guide to sliding mode control," 2002, pp. 1-14.

[12]O. Kaynak, "Guest editorial special section on computationally intelligent methodologies and sliding-mode control," IEEE Transactions on Industrial Electronics, vol. 48, pp. 2-3, 2001.

[13]S. Zahmatkesh, Farzin Piltan, K. Heidari, M. Shamsodini, S. Heidari, “Artificial Error Tuning Based on Design a Novel SISO Fuzzy Backstepping Adaptive Variable Structure Control” International Journal of Intelligent Systems and Applications, vol.5, no.11, pp.34-46, 2013. DOI: 10.5815/ijisa.2013.11.04.

[14]Meysam Kazeminasab, Farzin Piltan, Zahra Esmaeili, Mahdi Mirshekaran, Alireza Salehi ,"Design Parallel Fuzzy Partly Inverse Dynamic Method plus Gravity Control for Highly Nonlinear Continuum Robot", IJISA, vol.6, no.1, pp.112-123, 2014. DOI: 10.5815/ijisa.2014.01.12.