Article,
Neurocontroller using Dynamic State Feedback for Compensatory Control
Neural Networks, (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.
