In this paper, the finite-horizon H ∞ fault estimation problem is investigated for a class of uncertain nonlinear time-varying systems subject to multiple stochastic delays. The randomly occurring uncertainties (ROUs) enter into the system due to the random fluctuations of network conditions. The measured output is quantized by a logarithmic quantizer before being transmitted to the fault estimator. Also, successive packet dropouts (SPDs) happen when the quantized signals are transmitted through an unreliable network medium. Three mutually independent sets of Bernoullidistributed white sequences are introduced to govern the multiple stochastic delays, ROUs and SPDs. By employing the stochastic analysis approach, some sufficient conditions are established for the desired finite-horizon fault estimator to achieve the specified H ∞ performance. The time-varying parameters of the fault estimator are obtained by solving a set of recursive linear matrix inequalities (RLMIs). Finally, an illustrative numerical example is provided to show the effectiveness of the proposed fault estimation approach.
In this paper, the non-fragile ∞ control problem is investigated for a class of discrete-time systems with randomly occurring gain variations (ROGVs). ROGVs, which describe the new network-induced phenomenon of a controller gain appearing in a random way, are modeled by a Bernoulli distributed white sequence with a known conditional probability. We aim to analyze and design a non-fragile ∞ output-feedback controller such that the closed-loop control system is stochastically stable while the desired ∞ performance is guaranteed. Intensive stochastic analysis is carried out to obtain sufficient conditions for ensuring the stochastic stability as well as prescribed ∞ disturbance-rejection attenuation. The controller design issue is then casted into a convex optimization one solvable by the semidefinite programme method. The effectiveness of the proposed method is demonstrated in the numerical example.Index Terms-Non-fragile ∞ control; randomly occurring gain variations; networked control systems.
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