A Youla-Kucera Parametrization for Data-Driven Controllers Tuning

Abstract

Abstract The Youla-Kucera parametrization is a fundamental result in system theory, very useful when designing model-based controllers. In this paper, this parametrization is employed to solve the controller design from data problem, without requiring a process model. It is shown that employing the proposed controller structure it is possible to achieve more stringent closed-loop performances than previous works in literature, maintaining a criterion to estimate the closed-loop stability. The developed design methodology does not imply a plant identification step and the solution can be obtained by least-squares algorithms in the case of stochastic additive noise. The designed solution is evaluated through Monte Carlo simulations for the regulation problem of an under-damped system.