Event-Triggered Control for Discrete-Time Semi-Markovian Jump Neural Networks Considering Probabilistic Actuator Faults
Xiaoqing Li, Kun She, Shouming Zhong · 2020
This paper addresses the reliable control synthesis problem for semi-Markovian jumping discrete-time neural networks (SMJDNNs) with delays and random faults by adopting event-triggered mechanism (ETM). The distortion probability for each actuator is governed by multiple Bernoulli probabilistic distribution delineate on the interval $[0,\mathbb{T}](\mathbb{T} \ge 1)$. Additionally, a more practical probabilistic actuator fault model for networked SMJDNNs is characterized. Under this framework, the distortion degree and failure rate of each actuator can be calculated by virtue of mathematical variance and expectation. By resorting to the Lyapunov functional method and combining with both the stochastic analysis technique and matrix inequality decomposition technique, several sufficient conditions on ensuring stabilization for co-designing the both the controller and trigger parameter matrices are formulated in the shape of linear matrix inequalities (LMIs). Eventually, a numerical example is exploited to illustrate the effectiveness and applicability of the proposed control design methodology.