Event-based finite-time state estimation for Markovian jump systems with quantizations and randomly occurring...

Area of Science:

• Control Systems Engineering
• Stochastic Systems Analysis
• Networked Systems

Background:

• Markovian jump systems are crucial for modeling systems with abrupt changes.
• Event-triggered schemes and quantization are vital for efficient data transmission in networked systems.
• Randomly occurring nonlinearities pose significant challenges in system analysis and estimation.

Purpose of the Study:

• To design a finite-time state estimator for Markovian jump systems with quantization and random nonlinearities.
• To reduce data transmission load and network bandwidth requirements.
• To ensure robust state estimation under uncertain and dynamic conditions.

Main Methods:

• Stochastic analysis techniques are employed to handle the probabilistic nature of the system.
• Linear matrix inequality (LMI) methods are utilized for deriving stability and boundedness conditions.
• An event-triggered control scheme is integrated to optimize data transmission.

Main Results:

• Sufficient conditions for stochastic finite-time boundedness and H-infinity finite-time boundedness are established.
• The explicit expression for the estimator gain is derived using LMIs.
• The proposed method effectively handles quantization and random nonlinearities.

Conclusions:

• The developed finite-time state estimator provides a robust solution for the considered complex systems.
• The theoretical results are validated through a numerical example, demonstrating practical applicability.
• The study contributes to the advancement of state estimation techniques for networked control systems.