Finite‐time fault detection for discrete nonlinear systems with time‐varying delays under the dynamic event‐triggered mechanism

Abstract

This article is concerned with the finite‐time fault detection (FTFD) problem for discrete networked systems subject to nonlinearities and time‐varying delays under the dynamic event‐triggered mechanism. The dynamic event‐based communication scheme is adopted to improve the efficiency of the bandwidth utilization, reduce the transmission of redundant signals and decrease the transmission pressure of the network. Then, by constructing a new Lyapunov functional and utilizing the appropriate inequality techniques, some sufficient conditions are derived under which the addressed augmented system achieves the finite‐time stability and attains a satisfactory H∞$$ {H}_{\infty } $$ performance index. Additionally, the expression forms of the fault detection filter (FDF) parameters are presented based on the solutions to certain matrix inequalities. In the end, a numerical simulation is employed to demonstrate the effectiveness and feasibility of the proposed FTFD strategy.

Adaptive event‐triggered fault detection for Markov jump nonlinear systems with time delays and uncertain parameters

“…Therefore, it is said that adding delay‐product terms to the L–K functional can reduce the conservatism of the result. It is worth mentioning that the practice of reducing conservatism by delay‐product‐type functional has not been noticed in some similar FD studies 35‐37 …”

“…It is worth mentioning that the practice of reducing conservatism by delay-product-type functional has not been noticed in some similar FD studies. [35][36][37] Remark 5. After a long development, event-triggered mechanisms have evolved into the primary communication method of networked control systems.…”

Robust Control of Nonlinear Discrete Systems With Perturbations Based on Estimated State Feedback

“…Inequality ( 25) can be obtained according to Schur's complement Lemma and inequality (24). 26) is equivalent to inequality (17) in Theorem 2., To accelerate the robust stabilization rate of system (16), inequality ( 18) is used to quadratically constrain the gain matrix of the controller, and a better control effect is achieved by setting performance indicators.…”

“…In the study of nonlinear discrete systems, the design of system state observer and the method of system robust control are often the focus of the research. Although studies [22][23][24][25][26][27] have been conducted considering the aspects of observer design, filtering, self-triggered pulse control, adaptive control, and fuzzy control, relatively few have focused on state observer-based robust control and observerbased robust feedback control. Therefore, in this study, the effect of system state estimation and robust control are the focus because of the difficulty they present, necessitating that the effect of system state observer on state estimation and the control achieved by the designed robust controller be considered.…”