M. Zele et al., A PROBABILISTIC MEASURE FOR MODEL PURPOSIVENESS IN IDENTIFICATION FORCONTROL, International Journal of Systems Science, 29(6), 1998, pp. 653-662
Citations number
11
Categorie Soggetti
Computer Science Theory & Methods","Operatione Research & Management Science","Computer Science Theory & Methods","Operatione Research & Management Science","Robotics & Automatic Control
The question of whether the identified process model will lead to a st
able closed loop is of practical relevance during iterative identifica
tion and controller design. It is known that, in the case of overly de
manding closed-loop requirements, the model resulting from the iterati
ve procedure might conflict with prior knowledge about the process. Ne
vertheless, in many cases the violation of the plausibility of the ide
ntified models does not necessarily violate its purposiveness. Therefo
re, it is a matter of practical importance to have a confident indicat
ion as to whether the given model will result in a stable closed-loop
design or not. If not, the iterative identification and controller des
ign should be stopped, that is more appropriate model structures shoul
d be chosen. In this paper, a probabilistic measure is proposed which
relies on the estimated model error obtainable by the stochastic embed
ding technique. The idea behind it is to estimate the probability that
the critical point (-1,0) will not be encircled by the Nyquist curve
of the return ratio transfer function of the true system. The results
obtained from experiments on a motor-generator laboratory set-up show
that the proposed probabilistic measure provides a reliable indication
of the stability of the designed closed loop.