Event-Triggered $$H_\infty $$ State Estimation for Coupled and Switched Genetic Regulatory Networks

This paper investigates the problem of event-triggered \(H_\infty \) state estimation for switched genetic regulatory networks with static coupling via a sojourn-probability-dependent approach. The measurements of the network are evaluated by the event-triggers which are only undertaken at the switching times. By employing a time-delay approach, the estimation can be achieved by determining the exponential mean-square stability of the switched system with time-varying delay and known sojourn probability, while the system prescribes an \(H_\infty \) performance level. A co-design approach for the event-triggered mechanism and the estimators is presented by means of a novel Lyapunov–Krasovskii functional combining with refined Jensen-based inequalities. Finally, a numerical example is given to demonstrate the effectiveness of the designed estimators.