This paper is concerned with the finite-time mixed H_∞ and passivity performance analysis and filter design for a class of uncertain nonlinear discrete-time Markovian jump systems (MJSs) described by Takagi–Sugeno fuzzy model with nonhomogeneous jump processes. In this paper, the proposed MJSs fuzzy model is formulated with norm-bounded parameter uncertainties and time-varying jump transition probability matrices. In particular, the time-varying transition probability matrices are expressed in respect of a polytope. By constructing a suitable Lyapunov functional, a new set of sufficient conditions is derived in the form of linear matrix inequalities (LMIs) to ensure that the filtering error system is robustly stochastically finite-time bounded and a prescribed mixed H_∞ and passive performance index is achieved. Moreover, the robust mixed H_∞ and passivity filter design gain matrices can be computed from the obtained LMIs. Furthermore, the developed results unify H_∞ and passive filtering problems in a single framework. Finally, two numerical examples including an application-oriented example are provided to demonstrate the effectiveness of the proposed filter design technique.

The aim of this paper is to analyze the problem of fault estimation for mode-dependent interval type-2 (IT2) fuzzy systems with quantized output measurements. Different from the existing fault estimation methods requiring the observer matching condition, a new fault estimation technique is proposed, wherein a stochastically intermediate variable subject to the information on operating modes is considered under which a robust observer is constructed to simultaneously estimate the state and faults. Based on the strategy of linear matrix inequality, sufficient conditions are established to ensure that the states of resulting systems are bounded in probability sense. By offering three illustrative examples in which two of them are practical models, namely, tunnel diode circuit system and Rossler system, the availability and feasibility of the proposed design method are explained.

In this paper, we focus on the event-triggered observer-based fuzzy positioning control and $L_{2}$ -gain analysis of nonlinear unmanned marine vehicle (UMV) systems in network environments, which is subject to a hybrid attack consisting of DoS attack and deception attack. Firstly, based on the T-S fuzzy modeling method, a unified switched system model is established for the positioning control of nonlinear UMV systems with actuator saturation, multiplicative gain uncertainties, external disturbance and hybrid attacks. Then, by resorting to introducing the integral-type state error, a novel adaptive memory-based event-triggered mechanism (ETM) is proposed to lighten the communication load in UMV systems. Furthermore, a sufficient criterion is proposed to make the mean square exponential stability of UMV systems with a non-weighted $L_{2}$ -gain level be guaranteed, and gain parameters of the designed controller and observer are also solved efficiently. Finally, the simulation example demonstrates the effectiveness of the proposed nonfragile control strategy.