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This paper investigates the fault detection and isolation (FDI) problem for a class of nonlinear systems with sensor outage faults. The considered nonlinear systems are described as affine fuzzy models, and the system outputs are chosen as the premise variables of fuzzy models. Different from the existing results, the influence of sensor faults on premise variables is considered. As a result, the well-known parallel distributed compensation scheme cannot be used for FDI filters design. By using the structural information encoded in the fuzzy rules, the affine fuzzy system is represented by multiple operating-regime-based models in fault-free case and faulty cases. In the multiple-model scheme, a bank of piecewise FDI filters are constructed, each of them is based on the affine fuzzy model that describes the system in the presence of a specified fault. The fault-dependent residual signals generated from the filters are used for detecting and isolating the specified fault. The FDI filter design conditions are obtained in the formulation of linear matrix inequalities. Finally, a numerical example is given to illustrate the effectiveness and merits of the proposed method. [ABSTRACT FROM PUBLISHER]

This paper is devoted to the control of Markov jump nonlinear systems with general transition probabilities (TPs) allowed to be known, uncertain, and unknown. With the help of the S-procedure to dispose the system nonlinearities and the TP property to eliminate the coupling between unknown TP and Lyapunov variable, an extended bounded real lemma for the considered system to be stochastically stable with the prescribed \mathcal H{\infty } performance is established in the framework of linear matrix inequalities. To handle the nonlinearity incurred by uncertain TP for controller synthesis, a separated method is proposed to decouple the interconnection between Lyapunov variables and controller gains. A numerical example is given to show the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

A novel decentralized adaptive integral sliding mode control law is proposed for a class of nonlinear uncertain large-scale systems subject to quantization mismatch between quantizer sensitivity parameters. Firstly, by applying linear matrix inequality techniques, integral-type sliding surface functions are derived for ensuring the stability of the whole sliding mode dynamics and obtaining the prescribed bounded L2 gain performance. Secondly, the decentralized adaptive sliding mode control law is developed to ensure the reachability of the sliding manifolds in the presence of quantization mismatch, interconnected model uncertainties, and external disturbances. Finally, an example is shown to verify the validity of theoretical results. [ABSTRACT FROM AUTHOR]