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.