Neurocontroller using dynamic state feedback for compensatory control

A common t.echnique in neurocontrol is that. of controlling a plant by static state feedback using the plant's inverse dynamics , which is approximated t.hrough a learning proce:flfl. It is ,"vdl knu\vn that in t.his control mode: e:ve:n small approximation errors or, which is the same, small perturbations of the plant may lead to insta­ bility. 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 Sta, te feedback control. This scheme is termed SDS Feedback Control. It is shown that as long as the error of the inverse dynamics model is "sign proper" 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, a.s arc qucstions related to noise sensitivity and robust control of robotic manipulators. Simulations of a simplified sensorimotor loop serve to illustrate the approach. KeJ)WOTd,�: Neural net.work cont.rol, compensating perturbations� stability, feedback control, feedfonvard control� inverse dynamics� on-line learning, Liapunov's second Preprint slllnnitted to Elsevier Science 24 March 1997