Sequential Adaptive RBF-Fuzzy Variable Structure Control Applied to Robotics Systems

Автор: Mohammed Salem, Mohamed F. Khelfi

Журнал: International Journal of Intelligent Systems and Applications(IJISA) @ijisa

Статья в выпуске: 9 vol.6, 2014 года.

Бесплатный доступ

In this paper, we present a combination of sequential trained radial basis function networks and fuzzy techniques to enhance the variable structure controllers dedicated to robotics systems. In this aim, four RBFs networks were used to estimate the model based part parameters (Inertia, Centrifugal and Coriolis, Gravity and Friction matrices) of a variable structure controller so to respond to model variation and disturbances, a sequential online training algorithm based on Growing-Pruning "GAP" strategy and Kalman filter was implemented. To eliminate the chattering effect, the corrective control of the VS control was computed by a fuzzy controller. Simulations are carried out to control three degrees of freedom SCARA robot manipulator where the obtained results show good disturbance rejection and chattering elimination.

Еще

Radial Basis Function Networks, Sequential Training, Growing and Pruning, Fuzzy Control, Variable Structure Control, Robot Manipulator

Короткий адрес: https://sciup.org/15010600

IDR: 15010600

Список литературы Sequential Adaptive RBF-Fuzzy Variable Structure Control Applied to Robotics Systems

  • F.L. Lewis, C. T. Abdallah, and D. M. Dawson, Control of Robot Manipulators, Macmillan Publishing Company, Inc, USA, 1993.
  • J.J.E. Slotine and W. Li, Applied Nonlinear Control. Prentice-Hall, 1991.
  • S.E. Shaffei, “Sliding mode control of robot manipulators via intelligent approaches”, Advances strategies for Robots manipulators. Sciyo, pp. 135-172., 2010.
  • A.G. Ak and G. Cansever, “Sliding mode controller with RBF networks for robotic manipulator trajectory tracking”, Lecture notes in control and information sciences (Intelligent Control and Automation), vol.144, pp.527-532, 2006.
  • S. Sefriti, J. Boumhidi, M. Benyakhlef and I. Boumhidi, “adaptive decentralized sliding mode neural network Control of a class of nonlinear interconnected systems”, International Journal of Innovative Computing, Information and Control, vol. 9-7, pp. 2941-2947, 2013.
  • M. R. Soltanpour, B. Zolfaghari, M. Soltani and M. H. Khooban, “Fuzzy sliding mode control design for a class of nonlinear systems with structured and unstructured uncertainties”, International Journal of Innovative Computing, Information and Control, Vol. 9-7, pp. 2713-2726,2013.
  • L. Mehri, M. Salimifar, M. Mansouri and M. Teshnelab, “Sliding Mode Trained Neural Control for Single and Coupled Inverted Pendulum System”, Researches and Applications in Mechanical Engineering, Vol. 2-4, 2013.
  • F. G. Rossomando, C. Soria and R. Carelli, “Sliding Mode Neuro Adaptive Control in Trajectory Tracking for Mobile Robots”, Journal of Intelligent & Robotic Systems, 2013.
  • Y. Li, N. Sundararajan and P. Saratchandran, “Analysis of minimal radial basis function network algorithm for real-time identification of nonlinear dynamic systems: Control theory and application”, IEEE Proceedings-control Theory and Applications vol. 147-4, pp.476-484, 2000.
  • S. Haykin, Neural Networks: A Comprehensive Foundation. Upper Saddle River, N.J.: Prentice Hall, 1999. 2nd Edition.
  • D. Broomhead and D. Lowe, “Multivariate function approximation and adaptive networks,” Complex Systems, vol. 2, pp. 321–355, 1988.
  • M.F. Khelfi and M. Salem, M., “RBF base sliding mode control of robot manipulator”, Proceedings of CCCA11, Hammamat, Tunisia, 2011.
  • H. Rong, N. Sundararajan, G. Huang and P. Saratchandran, “Sequential adaptive fuzzy inference system (SAFIS) for nonlinear system identification and prediction”, Fuzzy Sets and Systems, vol.157-9, pp.1260-1275, 2006.
  • T.P. Leung, C.-Y. Su and Q.-J. Zhou, "Sliding mode control of robot manipulators: a case study", Industrial Electronics Society, IECON '90, 1990.
  • T. Yoshimura, “Adaptive fuzzy sliding mode control for uncertain multi-input multi-output discrete-time systems using a set of noisy measurements”, International Journal of Systems Science, 2013.
  • Q. P. Ha Q. H. Nguyen, D. C. Rye and H. F. Durrant-Whyte, “Fuzzy Sliding-Mode Controllers with Applications”, IEEE transactions on industrial electronics, vol. 48-1, 2001.
  • V.Q. Leu, F. Mwasilu, H.H Choi, J. Lee and J.W. Jung, “Robust fuzzy neural network sliding mode control scheme for IPMSM drives”, International Journal of Electronics, 2013.
  • D. Xu, B. Jiang, M. Qian and J. Zhao, “Terminal Sliding Mode Control Using Adaptive Fuzzy- Neural Observer, Mathematical Problems in Engineering, Vol 2013, 2013.
  • G. Xia and H. Wu, “Network-based neural adaptive sliding mode controller for the ship steering problem”, Advances in Swarm Intelligence Lecture Notes in Computer Science, Vol. 7928, pp 497-505, 2013.
  • S. Bhuvaneswari and J. Sabarathinam, "Defect Analysis Using Artificial Neural Network", IJISA, vol.5, no.5, pp.33-38, 2013, DOI: 10.5815/ijisa.2013.05.05
  • S.Bangia, P.R Sharma and M. Garg, "Simulation of Fuzzy Logic Based Shunt Hybrid Active Filter for Power Quality Improvement", IJISA, vol.5, no.2, pp.96, 2013, DOI: 10.5815/ijisa.2013.02.12.
Еще
Статья научная