Periodic event-triggered generalized dissipative state estimation for hidden semi-Markov jump systems

This paper investigates generalized dissipative state estimation for hidden semi-Markov jump systems under a periodic event-triggering mechanism. The proposed method extends the applicability of semi-Markov jump systems to scenarios where the system modes cannot be directly observed but must be inferred from the observed modes. To overcome the limitations of conventional event-triggering mechanisms, such as frequent sensor activation and the constraint of a minimum triggering interval of one, the proposed approach introduces a dual-stage process. In this mechanism, sensors periodically acquire measurements and then determine whether the triggering condition is met, significantly reducing data transmission. Building on this framework, we establish an observed mode-dependent periodic event-triggering mechanism and design a generalized dissipative filter to guarantee a wide range of estimation performance criteria within a unified framework. The effectiveness of the proposed method is demonstrated through a numerical example and a case study involving a continuously stirred tank reactor system.