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System Identification has been developed, by and large, following the classical parametric approach. In this tutorial we shall discuss how Bayesian statistics and regularization theory can be employed to tackle the system identification problem from a nonparametric (or semi-parametric) point of view. The present paper provides an introduction to the use of Bayesian techniques for smoothness and sparseness,...
This paper deals with fast input-free partial state estimation for discrete-time LPV systems through functional observers. The system inputs are assumed to be unknown and then the observers reduce to functions of finite sequences of the output only. The existence of the observer is proved by resorting to both the notion of inverse system and the concept of maximal A-invariant subspaces. A constructive...
An algorithmic method is proposed to design stabilizing control laws for a class of nonlinear systems that comprises single-input feedback-linearizable systems and a particular set of single-input non feedback-linearizable systems. The method proceeds iteratively and consists of two stages; it converts the system into cascade form and reduces the dimension at every step by creating quotient manifold...
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