Elsevier

Fuzzy Sets and Systems

Volume 91, Issue 3, 1 November 1997, Pages 285-304
Fuzzy Sets and Systems

Self-organizing rule-based control of multivariable nonlinear servomechanisms

https://doi.org/10.1016/S0165-0114(96)00149-2Get rights and content

Abstract

By employing a feedforward and feedback PD control structure, this paper presents a simple approach to the problem of controlling multivariable nonlinear servomechanisms. The feedforward control action is deduced by a rule-based system employing a simplified fuzzy reasoning algorithm. Instead of relying on human experts the required rule-base is constructed automatically via a self-organizing counterpropagation network in cooperation with an on-line learning mechanism providing the required teacher signals. The convergence property of the learning mechanism is analyzed in some detail. Particular attention is paid to the problem of generalization, that is, the problem of how the learned knowledge can be used to handle novel situations without need for relearning. In the paper, it is suggested that local generalization may be achieved by nonlinear interpolation of fuzzy reasoning algorithm whereas linear generalization can be obtained by the appropriate utilization of the linear factor. A particular system under consideration is a multivariatle nonlinear passive line-of-sight (LOS) stabilization system. Simulation results on the LOS system have shown that the proposed control structure yields better performances than PD control alone, the rule-base can be constructed relatively fast in terms of requiring only a few learning cycles, and the suggested schemes for achieving generalization are useful and effective.

References (20)

  • T.H. Lee et al.

    Real-time parallel adaptive neural network control for nonlinear servomechanisms

    Mechatronics

    (1993)
  • R. Ortega et al.

    Adaptive motion control of rigid robots: a tutorial

    Automatica

    (1989)
  • S. Arimoto

    Learning control theory for robotic motion

    Int. J. Adaptive Control Signal Process.

    (1990)
  • C.G. Atkeson et al.

    Using associative content-addressable memories to control robots

  • J.J. Craig

    Introduction to Robotics: Mechanics and Control

    (1989)
  • J.J. Craig et al.

    Adaptive control of mechanical manipulators

    Int. J. Robotics Res.

    (1987)
  • R. Hecht-Nielsen

    Counterpropagation network

    Appl. Opt.

    (1987)
  • M. Kawato et al.

    A hierarchical neural-network model for control and learning of voluntary movement

    Biological Cybern.

    (1987)
  • T.Y. Kuc et al.

    An iterative control of robot manipulators

    IEEE Trans. Robotics Automation

    (1991)
  • T.H. Lee et al.

    Stable adaptive control of multivariable servomechanisms, with real-time application to a passive line-of-sight stabilization system

There are more references available in the full text version of this article.

Cited by (8)

  • Composite compensation control scheme for airborne opto-electronic platform

    2012, Guangxue Jingmi Gongcheng/Optics and Precision Engineering
  • Multivariable fuzzy logic/self-organizing for anesthesia control

    2010, Journal of Medical and Biological Engineering
  • Separately excited DC motor drive with fuzzy self-organizing

    2007, ICCAS 2007 - International Conference on Control, Automation and Systems
  • Some key issues in the design of self-organizing fuzzy control systems

    2006, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
View all citing articles on Scopus
View full text