Dynamic state feedback neurocontroller for compensatory control
Cs. Szepesvári, Sz. Cimmer and A. Lõrincz
Neural Networks
in press (1997)
Abstract
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
the 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 dynamics 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.
