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This study focuses on the memory state feedback control problem for a family of time delayed interval type-2 Takagi-Sugeno fuzzy systems with external disturbance and actuator faulty input. Notably, an actuator fault model is implemented in the control design. In this study, the membership functions, the premise variables of the controller and the considered plant model are chosen freely. By employing the Lyapunov–Krasovskii functional method, the linear matrix inequality approach and the Abel lemma-based finite sum inequality, a set of sufficient conditions that guarantees the stability of the considered system is obtained via the extended passivity performance index. The developed theoretical results are validated with an artificial example and an application-oriented example. [ABSTRACT FROM AUTHOR]

This paper is concerned with the finite-time mixedH∞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 mixedH∞and passive performance index is achieved. Moreover, the robust mixedH∞and passivity filter design gain matrices can be computed from the obtained LMIs. Furthermore, the developed results unifyH∞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. [ABSTRACT FROM PUBLISHER]

This paper investigates the reliable dissipative control problem for high-speed trains (HSTs) under probabilistic time-varying sampling with a known upper bound on the sampling intervals. In particular, random variables obeying the Bernoulli distribution are considered to account for the probabilistic time-varying delays. Based on Lyapunov–Krasovskii functional approach which considers full use of the available information about actual sampling pattern, a new set of sufficient condition is established to guarantee that the HST can well track the desired speed and the relative spring displacement between the two neighbouring carriages is asymptotically stable and the corresponding error system is strictly-dissipative. The existence condition of the dissipativity-based reliable sampled-data controller is obtained in terms of a set of linear matrix inequalities which are delay-distribution-dependent, i.e. the solvability of the condition depends on not only the variation range of the delay but also the probability distribution of it. Moreover, different control processes for the HST system can be obtained from the proposed design procedure and hence it can reduce the time and cost. Finally, the effectiveness and benefits of the proposed control law is demonstrated through a numerical example by taking the experimental values of Japan Shinkansen HST. [ABSTRACT FROM PUBLISHER]