Controller selection in switching supervisory control under a coarse candidate model distribution

Studies the problem of inferring the behaviour of a linear feedback loop made up by an uncertain plant and a given candidate controller by using data from the actual operating loop. In such a context, it is shown that a convenient tool to work with is a quantity called normalized divergence. This is a discrepancy measure between the loop made up by the unknown plant in feedback with the candidate controller and the nominal “tuned-loop” related to the same candidate controller. It is shown that divergence can be in principle obtained by resorting to the concept of a virtual reference, and conveniently computed in real-time by suitably filtering an output prediction error. The latter result is of relevant practical value for online implementation and of paramount importance in switching supervisory control of uncertain plants, particularly in the case of a coarse candidate model distribution