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The quadratic control problem for discrete-time singular Markov jump systems with parameter uncertainties is discussed. The weighting matrix in quadratic cost function is indefinite. For full and partial knowledge of transition probabilities cases, state feedback controllers are designed based on linear matrix inequalities (LMIs) methods which guarantee that the closed-loop discrete-time singular...
This paper is concerned with the output feedback guaranteed cost control problem for a class of uncertain stochastic large-scale systems governed by a random parameter. The uncertainties are assumed to satisfy integral quadratic constraints, and the random parameter is a Markov process. A sufficient condition is established for the design of decentralized output feedback guaranteed cost controllers...
Among of important results herein is the performance information analysis of forecasting higher-order characteristics of a general criterion of performance associated with a stochastic tracking system which is closely supervised by a reference command input and a desired trajectory. Both compactness from logic of state-space model description and quantitativity from probabilistic knowledge of stochastic...
This paper concerns the robust Hinfin filtering problem for uncertain discrete-time singular systems with lossy data due to unreliable networked transmission. The stochastic variable satisfying Bernoulli random binary distribution is introduced to model the missing measured outputs. A set of sufficient conditions for the existence of the desired filter is established, and a robust filter design method...
Control design for stochastic uncertain nonlinear systems is traditionally based on minimizing the expected value of a suitably chosen loss function. Moreover, most control methods usually assume the certainty equivalence principle to simplify the problem and make it computationally tractable. We offer an improved probabilistic framework which is not constrained by these previous assumptions, and...
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