Advertisement

Simulation and Experimental Studies of Hybrid Learning Control with Acceleration Feedback for Flexible Manipulators

  • M. Z. Md Zain
  • M. S. Alam
  • M. O. Tokhi
  • Z. Mohamed
Conference paper

Abstract

This paper presents investigations at developing a hybrid iterative learning control scheme with acceleration feedback (PDILCAF) for flexible robot manipulators. An experimental flexible manipulator rig and corresponding simulation environment are used to demonstrate the effectiveness of the proposed control strategy. In this work the dynamic model of the flexible manipulator is derived using the finite element (FE) method. A collocated proportional-derivative (PD) controller utilizing hub-angle and hub-velocity feedback is developed for control of rigid-body motion of the system. This is then extended to incorporate iterative learning control with acceleration feedback and genetic algorithms (GAs) for optimization of the learning parameters for control of vibration (flexible motion) of the system. The system performance with the controllers is presented and analysed in the time and frequency domains. The performance of the hybrid learning control scheme without and with acceleration feedback is assessed in terms of input tracking, level of vibration reduction at resonance modes and robustness with various.

Keywords

Acceleration feedback flexible manipulator genetic algorithms iterative learning control 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Yurkovich S. Flexibility effects on performance and control. Robot Control 1992; Part 8:321–323.Google Scholar
  2. 2.
    Arimoto S., Kawamura S., and Miyazaki F. Bettering operation of robots by learning. Journal of Robotic Systems, 1984;1(2):123–140CrossRefGoogle Scholar
  3. 3.
    Panzieri S. and Ulivi G. Disturbance rejection of iterative learning control applied to trajectory tracking for a flexible manipulator. In Proceedings of 3 rd European Control Conference, ECC, September 1995, pages 2374–2379.Google Scholar
  4. 4.
    Amann N., Owens D. H., and Rogers E. Iterative learning control for discrete time systems with exponential rate of convergence. Technical Report 95/14, Centre for Systems and Control Engineering, University of Exeter, 1995.Google Scholar
  5. 5.
    Tokhi M. O., Mohamed Z. and Shaheed M. H. Dynamic characterisation of a flexible manipulator system. Robotica, 2001; 19(5): 571–580.CrossRefGoogle Scholar
  6. 6.
    Azad K. M. Analysis and design of control mechanisms for flexible manipulator systems. PhD thesis, Department of Automatic Control and Systems Engineering, The University of Sheffield, 1994.Google Scholar
  7. 7.
    Chipperfield, A.J., Flemming P.J., & Fonscea, C.M. ‘Genetic algorithms for control system engineering’, Proceeding Adaptive Computer in Engineering Design and Control, September 1994: pp.128–133.Google Scholar
  8. 8.
    Linkens, D.A., & Nyongesa, H.O., ‘Genetic algorithms for fuzzy control’, IEE Proceeding Control Theory Application, Vol. 142(3): pp. 161–185.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • M. Z. Md Zain
    • 1
  • M. S. Alam
    • 1
  • M. O. Tokhi
    • 1
  • Z. Mohamed
    • 2
  1. 1.Department of Automatic Control and Systems EngineeringThe University of SheffieldUK
  2. 2.Faculty of Electrical Engineering, 81310 UTM SkudaiUniversiti Teknologi MalaysiaMalaysia

Personalised recommendations