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This paper addresses the design of robust multiple model adaptive controllers (RMMAC) for linear parameter varying (LPV) systems using bilinear matrix inequalities (BMI). We show a novel method that allows increasing the performance of the RMMAC when the uncertain parameters of the system dynamics are time-varying (TV). We assume that these TV parameters cannot be measured on-line and that the only...
This paper introduces the novel concept of using Set-Valued Observers (SVOs) in Fault Detection and Isolation (FDI), for discrete-time linear time-varying systems. The proposed method relies on SVO-based model invalidation to discard models that are not compatible with the input/output data. We argue that there are mainly three significant advantages of using SVOs for FDI, when compared to the most...
This paper proposes a new approach for the selection of a biophysical model describing the haemodynamic response function (HRF) measured in BOLD-fMRI data, based on model falsification techniques. Specifically, the novel method of Multiple Model Set-Valued Observers (MMSVOs) is introduced. The observers consider that the initial state lives in a set, the linear time-varying dynamic system obtained...
This paper proposes a strategy referred to as stability overlay (SO) for linear and nonlinear time-varying plants, that provides input/output stability guarantees for a wide set of adaptive control schemes. We use this methodology to endow multiple-model adaptive control (MMAC) architectures with robust stability properties when the plant to be controlled is uncertain and time-varying. We emphasize...
We demonstrate, using Monte-Carlo simulations, the robust performance of the adaptive control methodology denoted by RMMAC/XI introduced and discussed (Fekri and Athans, 2006 and 2005). The RMMAC/XI architecture can handle simultaneous time-variations in the plant uncertain parameters as well as the disturbance intensity statistics. We compare the RMMAC/XI performance vs that of the best possible...
We demonstrate, using Monte-Carlo simulations, the superior performance of the "Robust Multiple- Model Adaptive Control (RMMAC)" method for different time-varying uncertain parameter waveforms, performance bandwidths and constant disturbance intensities using the test example designed and studied in Refs. [1] and [2]. We further show that the RMMAC RMS performance is just about 10-16% worse...
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