Finite-time control in probability for time-varying systems with measurement censoring

Abstract

This paper considers a parameter-dependent controller design problem for a class of discrete-time uncertain systems subject to censored measurement. First, a set of mutually independent stochastic variables obeying uniform distribution is used to describe the system uncertainty. Then, an array of new bounded variables is introduced to characterize the boundedness of the censored measurement. In addition, a novel definition, named as finite-time boundedness in probability (FTBP), is presented to depict the dynamic behavior of addressed systems in the sense of probability. In this case, the norm of controlled system states cannot exceed a given boundary under a probability constraint. By means of the hyper-rectangle depending on the value range of stochastic variables, a sufficient condition is presented to ensure that the system is FTBP. Finally, the corresponding controller design problem is formulated as an algorithm based on the recursive linear matrix inequality. Two simulation examples are given to illustrate the effectiveness of the proposed methodology.