Robust multiple model adaptive control: Modified using ν-gap metric

A new robust adaptive control method is proposed, which removes the deficiencies of the classic robust multiple model adaptive control (RMMAC) using benefits of the nu-gap metric. First, the classic RMMAC design procedure cannot be used for systematic design for unstable plants because it uses the Baram Proximity Measure, which cannot be calculated for open-loop unstable plants. Next, the %FNARC method which is used as a systematic approach for subdividing the uncertainty set makes the RMMAC structure being always companion with the mu-synthesis design method. Then in case of two or more uncertain parameters, the model set definition in the classic RMMAC is based on cumbersome ad hoc methods. Several methods based on nu-gap metric for working out the mentioned problems are presented in this paper. To demonstrate the benefits of the proposed RMMAC method, two benchmark problems subject to unmodeled dynamics, stochastic disturbance input and sensor noise are considered as case studies. The first case-study is a non-minimum-phase (NMP) system, which has an uncertain NMP zero; the second case-study is a mass-spring-dashpot system that has three uncertain real parameters. In the first case-study, five robust controller design methods (H-2, H-infinity, QFT, H-infinity loop-shaping and mu-synthesis) are implemented and it is shown via extensive simulations that RMMAC/nu/QFT method improves disturbance-rejection, when compared with the classic RMMAC. In the second case-study, two robust controller design methods (QFT and mixed mu-synthesis) are applied and it is shown that the RMMAC/nu/QFT method improves disturbance-rejection, when compared with RMMAC/nu/mixed-mu. Copyright (C) 2010 John Wiley & Sons, Ltd.