Advances in Youla-Kucera parametrization: A Review

International audienceYoula-Kucera (YK) parametrization was formulated decades ago for obtaining the set of controllers stabilizing a linear plant. This fundamental result of control theory has been used to develop theoretical tools solving many control problems ranging from stable controller switching, closed-loop identification, robust control, disturbance rejection, adaptive control to fault tolerant control.This paper collects the recent work and classifies the maccording to the use of YK parametrization, Dual YK parametrization or both, providing the latest advances with main applications indifferent control fields. A final discussion gives some insights on the future trends in the field

The analysis and design of multirate sampled-data feedback systems via a polynomial approach

This thesis describes the modelling, analysis and design of multirate sampled-data feed-back via the polynomial equations approach. The key theoretical contribution constitutes the embedding of the principles underpinning and algebra related to the switch and frequency decomposition procedures within a modern control framework, thereby warranting the use of available computer-aided control systems design software. A salient feature of the proposed approach consequently entails the designation of system models that possess dual time- and frequency-domain interpretations. Expositionally, the thesis initially addresses scalar systems excited by deterministic inputs, prior to introducing stochastic signals and culminates in an analysis of multivariable configurations. In all instances, overall system representations are formulated by amalgamating models of individual sub-systems. The polynomial system descriptions are shown subsequently to be compatible with the Linear Quadratic Gaussian and Generalised Predictive Control feedback system synthesis methods provide causality issues are dealt with appropriately. From a practical perspective, the polynomial equations approach proffers an alternative methodology to the state-variable techniques customarily utilised in this context and affords the insights and intuitive appeal associated with the use of transfer function models. Numerical examples are provided throughout the thesis to illustrate theoretical developments

Multiple Model Adaptive Control Using Dual Youla-Kucera Factorisation

We propose a multi-model adaptive control scheme for uncertain linear plants based on the concept of model unfalsification. The approach relies on examining the ability of a pre-computed set of plant-controller candidates and choosing the one that is best able to reproduce observed in- and output signal samples. The ability to reproduce observations is measured as an easily computable signal norm. Compared to other related approaches, our procedure is designed to be able to handle significant measurement noise and closed-loop correlations between output measurements and control signals.<br/