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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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Event-Triggered Resilient Filtering With the Interval Type Uncertainty for Markov Jump Systems.

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    This study introduces event-triggered resilient filtering for Markov jump systems, enhancing stability and performance while reducing network resource consumption through an innovative asynchronous filter design. The method improves accuracy and computational efficiency for uncertain systems.

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    Area of Science:

    • Control Systems Engineering
    • Stochastic Systems Analysis
    • Robotics

    Background:

    • Investigates event-triggered resilient filtering for Markov jump systems with asynchronous constraints.
    • Employs a hidden Markov model to represent filter-system asynchronicity.
    • Addresses gain uncertainties using interval type, offering higher accuracy than norm-bounded types.

    Purpose of the Study:

    • To develop an event-triggered resilient filter for Markov jump systems.
    • To reduce network resource consumption and computational complexity.
    • To ensure stochastic stability and extended dissipation performance of the filtering error system.

    Main Methods:

    • Utilizes a hidden Markov model for asynchronous constraints.
    • Models filter gain uncertainties as interval types and separates vertices to simplify linear matrix inequalities.
    • Applies an event-triggered scheme with a diagonal matrix threshold parameter.
    • Employs convex optimization to derive sufficient conditions.

    Main Results:

    • Significantly reduces the number of linear matrix inequality constraints, decreasing calculation difficulty and time.
    • Successfully balances network bandwidth reduction with improved system performance.
    • Guarantees stochastic stability and extended dissipation performance for the filtering error systems.

    Conclusions:

    • The proposed event-triggered resilient filtering method is effective for Markov jump systems.
    • The approach offers advantages in terms of accuracy, computational efficiency, and resource management.
    • Validated through a single-link robot arm system simulation.