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

Area of Science:

• Control Systems Engineering
• Stochastic Systems Analysis
• Estimation Theory

Background:

• Hidden semi-Markov jump systems present challenges in state estimation due to unobservable modes.
• Conventional event-triggering mechanisms often lead to excessive data transmission and have limitations on triggering intervals.
• Accurate state estimation is crucial for effective control and monitoring in dynamic systems.

Purpose of the Study:

• To develop a generalized dissipative state estimation method for hidden semi-Markov jump systems.
• To introduce an observed mode-dependent periodic event-triggering mechanism to reduce data transmission.
• To enhance the applicability of semi-Markov jump systems in practical scenarios.

Main Methods:

• A dual-stage periodic event-triggering mechanism is proposed, involving periodic measurement acquisition and condition checking.
• A generalized dissipative filter is designed to guarantee performance criteria within a unified framework.
• The method addresses systems where system modes are inferred from observed modes.

Main Results:

• The proposed event-triggering mechanism significantly reduces data transmission compared to conventional methods.
• The generalized dissipative filter ensures a wide range of estimation performance criteria are met.
• The effectiveness is validated through a numerical example and a continuously stirred tank reactor case study.

Conclusions:

• The developed method provides an efficient and robust approach for state estimation in hidden semi-Markov jump systems.
• The periodic event-triggering mechanism overcomes limitations of traditional approaches, enabling practical applications.
• This work contributes to the advancement of estimation theory for complex stochastic systems.