Some novel approaches on state estimation of delayed neural networks

Quantized dissipative control based on T-S fuzzy model for wind generation systems

In this paper, we address the extended dissipativity (ED) performance of delayed T-S fuzzy model (TSFM)-based wind generation systems (WGSs). First, a concept of coupled leakage time-varying delay (CLTVD) is proposed to construct a more widespread TSFM. Second, the relaxed term with time-delay-product function (TDPF) is introduced. Then, a befitting Lyapunov-Krasovskii functional (LKF) is constructed, which can handle the delay and its derivation. Third, by using valid integral inequalities, new stabilization criteria are established. Meanwhile, the desired fuzzy quantized control with CLTVD is designed. Finally, simulation results are given to show the validity and superiority of our derived results.

A New Approach to Stochastic Stability of Markovian Neural Networks With Generalized Transition Rates

This paper investigates the stability problem of Markovian neural networks (MNNs) with time delay. First, to reflect more realistic behaviors, more generalized transition rates are considered for MNNs, where all transition rates of some jumping modes are completely unknown. Second, a new approach, namely time-delay-dependent-matrix (TDDM) approach, is proposed for the first time. The TDDM approach is associated with both time delay and its time derivative. Thus, the TDDM approach can fully capture the information of time delay and would play a key role in deriving less conservative results. Third, based on the TDDM approach and applying Wirtinger's inequality and improved reciprocally convex inequality, stability criteria are derived. In comparison with some existing results, our results are not only less conservative but also involve lower calculation complexity. Finally, numerical examples are provided to show the effectiveness and advantages of the proposed results.

Robust H∞ state-feedback control for linear systems

This paper investigates the problem of robust H∞ control for linear systems. First, the state-feedback closed-loop control algorithm is designed. Second, by employing the geometric progression theory, a modified augmented Lyapunov-Krasovskii functional (LKF) with the geometric integral interval is established. Then, parameter uncertainties and the derivative of the delay are flexibly described by introducing the convex combination skill. This technique can eliminate the unnecessary enlargement of the LKF derivative estimation, which gives less conservatism. In addition, the designed controller can ensure that the linear systems are globally asymptotically stable with a guaranteed H∞ performance in the presence of a disturbance input and parameter uncertainties. A liquid monopropellant rocket motor with a pressure feeding system is evaluated in a simulation example. It shows that this proposed state-feedback control approach achieves the expected results for linear systems in the sense of the prescribed H∞ performance.