Neurocontroller using dynamic state feedback for compensatory control

Csaba Szepesvári, Szabolcs Cimmer, András Lorincz

Research output: Contribution to journalArticle

10 Citations (Scopus)


A common technique in neurocontrol is that of controlling a plant by static state feedback using the plant's inverse dynamics, which is approximated through a learning process. It is well known that in this control mode even small approximation errors or, which is the same, small perturbations of he plant may lead to instability. Here, a novel approach is proposed to overcome the problem of instability by using the inverse dynamics both for the static and for the error-compensating dynamic state feedback control. This scheme is termed SDS feedback control. It is shown that as long as the error of the inverse dynamic model is 'signproper' the SDS feedback control is stable, i.e., the error of tracking may be kept small. The proof is based on a modification of Liapunov's second method. The problem of on- line learning of the inverse dynamics when using the controller simultaneously for both forward control and for dynamic feedback is dealt with, as are questions related to noise sensitivity and robust control of robotic manipulators. Simulations of a simplified sensorimotor loop serve to illustrate the approach.

Original languageEnglish
Pages (from-to)1691-1708
Number of pages18
JournalNeural Networks
Issue number9
Publication statusPublished - Dec 1 1997


  • Compensating perturbations
  • Feedback control
  • Feedforward control
  • Inverse dynamics
  • Liapunov's second method
  • Neural network control
  • On-line learning
  • Stability

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Artificial Intelligence

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