Abstract
This paper treats the mode-dependent output-feedback synthesis problem of jump linear systems. This kind of systems are subjected to random (Markovian) jumps in parameter values. When the transition probability matrix is constant and known, we provide necessary and sufficient conditions for mode-dependent output-feedback control laws which stabilize the system in the mean-square sens. In the case with a time-varying, unknown-but-bounded transition probability matrix, our conditions are only sufficient. Our formulation leads to a nonconvex optimization problem under LMIs constraints. This problem is approximating by a family of an LMI-based linearization algorithms.
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