Robust stabilization of stochastic discrete-time LPV systems

This research presents a series of robust stabilization conditions for stochastic discrete-time linear parameter-varying (SDTLPV) systems characterized by linear fractional representation (LFR). The approach employs slack variables and decision matrices within the LFR framework to support the development of innovative controllers. The analysis focuses on the use of parameter-dependent Lyapunov (PDL) functions and full-block multipliers to derive synthesis conditions aimed at reducing conservatism in the study of SDTLPV systems. These design conditions are formulated as linear matrix inequalities (LMIs), enabling the synthesis of robust state-feedback and output-feedback controllers. The efficacy of the proposed approach is illustrated through a numerical example.