In this article, we investigate a new way of dealing with fault detection and isolation (FDI) for a class of delayed non‐linear systems with an event‐triggered mechanism (ETM). This article is based on 1) To estimate the dynamics of the nonlinear plant, Sliding Mode Observers (SMO's) is manipulated with Luenberger. 2) Under the ETM, sampled data is released only when the plant's measurement (sampled) violate the specific threshold of the event condition. 3) First, transform into two subsystems. In subsystem‐A contains uncertainties without sensor fault and vice‐versa in subsystem‐B. To handle the uncertainties that appeared in subsystem‐A, a new SMO's implemented while traditional Luenberger is employed to detect the sensor fault. Then, by using a new Lyapunov‐Krasovskii function approach performance index is proposed. Based on stability criteria, sufficient conditions are provided in the account of Linear Matrix Inequalities (LMIs). The simulation example is given to demonstrate the effectiveness of our design method.

This paper investigates the H∞ filtering problem for nonhomogeneous Markovian jump repeated scalar nonlinear systems with multiplicative noises and partially mode-dependent (PM) characterization. A new PM H∞ filter is proposed, which guarantees the stochastic stability of the filtering error systems. The transition probabilities (TPs) of the nonhomogeneous Markovian process are assumed to be polytopic and the probability for successful transmission of mode information is characterized by a Bernoulli distributed sequence. By constructing the Lyapunov functional method, the existence conditions of filter are presented. Finally, the efficiency of the obtained results for PM H∞ filter are demonstrated by an economic system example.

This paper deals with the iterative learning control issue for multi-input multi-output singular distributed parameter systems (SDPSs) with parabolic and hyperbolic type, which described by coupled partial differential equations with singular matrix coefficients. Initially, applying the singular value decomposition theory to SDPSs, an equivalent dynamic decomposition form is derived. Then, the estimation of the relationship between the learning system substates and output tracking error are constructed in the light of P-type update learning scheme under some assumptions. Moreover, two sufficient conditions are presented to ensure that the tracking error is convergent in the sense of L₂ norm by employing the contracting mapping principle as well as some basic differential inequalities. Finally, two numerical examples are shown to demonstrate the validity of the developed theoretical results.

In this paper, an iterative learning control problem is addressed for a class of parabolic partial difference systems. Several discrete D-type iterative learning control algorithms with initial state learning are proposed for the systems which have no direct channel between the input and output as well as the initial state value being unfixed in the learning process. Based on fundamental mathematical analysis tools and the discrete Gronwall inequality, sufficient conditions for tracking error convergence in the iterative domain for open-loop, closed-loop and open-closed-loop iterative learning control are established and proven respectively. Numerical simulations verify the effectiveness of the theoretical results.