NEUROCONTROLLER USING DYNAMIC STATE-FEEDBACK FOR COMPENSATORY CONTROL

A common technique in neurocontrol is that of controlling a plant by s tatic state feedback using the plant's inverse dynamics, which is appr oximated through a learning process. It is well known that in this con trol mode even small approximation errors or, which is the same, small perturbations of the plant may lead to instability. Here, a novel app roach is proposed to overcome the problem of instability by using the inverse dynamics both for the static and for the error-compensating dy namic state feedback control. This scheme is termed SDS feedback contr ol. It is shown that as long as the error of the inverse dynamics mode l is ''signproper'' the SDS feedback control is stable, i.e., the erro r 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 in verse dynamics when using the controller simultaneously for both forwa rd control and for dynamic feedback is dealt with, as are questions re lated to noise sensitivity and robust control of robotic manipulators. Simulations of a simplified sensorimotor loop serve to illustrate the approach. (C) 1997 Elsevier Science Ltd. All rights reserved.