The paper studies how on-line inferring stability of a potential control-loop consisting of an uncertain plant interconnected in feedback with a candidate controller using plant I/O pairs recorded while the plant is possibly driven by a different controller. In such a context, a convenient tool to work with is to resort to the conceptual entity of a virtual reference (VR). The adopted approach consists of embedding, in the so-called unfalsified adaptive switching control schemes based on VR, a family of nominal models pairwise associated with the given candidate controllers. The result is that the supervised switching mechanism can moderate the chance that destabilizing controllers be switched-on and, hence, reduce both the magnitude and time durations of “learning” transients after start-up, while, in contrast with pre-existing multi-model based methods, stability in-the-large is guaranteed under the minimal conceivable assumption that a stabilizing candidate controller exist.
Multi-model unfalsified adaptive switching supervisory control / S. Baldi; G. Battistelli; E. Mosca; P.Tesi. - In: AUTOMATICA. - ISSN 0005-1098. - STAMPA. - 46:(2010), pp. 249-259. [10.1016/j.automatica.2009.10.034]
Multi-model unfalsified adaptive switching supervisory control
BALDI, SIMONE;BATTISTELLI, GIORGIO;MOSCA, EDOARDO;TESI, PIETRO
2010
Abstract
The paper studies how on-line inferring stability of a potential control-loop consisting of an uncertain plant interconnected in feedback with a candidate controller using plant I/O pairs recorded while the plant is possibly driven by a different controller. In such a context, a convenient tool to work with is to resort to the conceptual entity of a virtual reference (VR). The adopted approach consists of embedding, in the so-called unfalsified adaptive switching control schemes based on VR, a family of nominal models pairwise associated with the given candidate controllers. The result is that the supervised switching mechanism can moderate the chance that destabilizing controllers be switched-on and, hence, reduce both the magnitude and time durations of “learning” transients after start-up, while, in contrast with pre-existing multi-model based methods, stability in-the-large is guaranteed under the minimal conceivable assumption that a stabilizing candidate controller exist.File | Dimensione | Formato | |
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