Stochastically exponential synchronization for Markov jump neural networks with time-varying delays via event-triggered control scheme

Abstract

This paper focuses on the stochastically exponential synchronization problem for one class of neural networks with time-varying delays (TDs) and Markov jump parameters (MJPs). To derive a tighter bound of reciprocally convex quadratic terms, we provide an improved reciprocally convex combination inequality (RCCI), which includes some existing ones as its particular cases. We construct an eligible stochastic Lyapunov–Krasovskii functional to capture more information about TDs, triggering signals, and MJPs. Based on a well-designed event-triggered control scheme, we derive several novel stability criteria for the underlying systems by employing the new RCCI and other analytical techniques. Finally, we present two numerical examples to show the validity of our methods.

Dynamic Event-Triggered Control for Delayed Nonlinear Markov Jump Systems under Randomly Occurring DoS Attack and Packet Loss

“…In recent decades, Markov Jump Systems (MJSs) have received widespread attention [1,2] due to their powerful ability to depict the mutation phenomenon that the parameter and structure of systems often encountered. It is worth mentioning that some undesirable dynamic behaviors, such as cyber attack [3,4], packet loss [5,6], time delay [7,8], non-linearity [9,10], often appear in the real systems, due to the openness of communication networks, limited bandwidth, external disturbance and signal propagation.…”

Observer‐based adaptive event‐triggered neural tracking control for nonlinear cyber‐physical systems with incomplete measurements

“…An adaptive event‐triggered tracking controller was designed for a class of pure‐feedback nonlinear systems with output constraints in References 26,27 considered an event‐triggered controller for continuous‐time nonlinear systems 28 and studied the problem of fuzzy adaptive event‐triggered control for a class of pure‐feedback nonlinear systems, which contain unknown smooth functions and unmeasured states. Meanwhile, References 29‐31 are based on well‐designed event‐triggered control schemes for neutral‐type semi‐Markovian jump (SMJ) neural networks with partial mode‐dependent additive time‐varying delays (ATDs), semi‐Markov jump uncertain (SMJU) neutral‐type neural networks with distributed and additive time‐varying delays (TDs), and one class of neural networks with time‐varying delays (TDs) and Markov jump parameters (MJPs).…”

H‐infinity fault‐tolerant control for Markov jump system with actuator time delay

“…Shu et al. [3] addressed the stochastically exponential synchronization problem for one class of neural networks with time‐varying delays and Markov jump parameters. Ding and Zhu [4] investigated the issues on extended dissipative anti‐disturbance control for switched singular semi‐Markovian jump systems with multiple disturbance and time‐delay via disturbance observer, and an optimization algorithm to constrain control gain was developed to reduce control cost in practice to some extent.…”

“…Huang et al [2] dealt with the problem of finite-time H-infinity synchronization for semi-Markov jump Lur'e systems with time-varying delay and external disturbance, and a design method for the required state-feedback controller was presented with the application of several decoupling techniques. Shu et al [3] addressed the stochastically exponential synchronization problem for one class of neural networks with time-varying delays and Markov jump parameters. Ding and Zhu [4] investigated the issues on extended dissipative anti-disturbance control for switched singular semi-Markovian jump systems with multiple disturbance and time-delay via disturbance observer, and an optimization algorithm to constrain control gain was developed to reduce control cost in practice to some extent.…”