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    Area of Science:

    • Control Systems Engineering
    • Systems Theory
    • Robotics

    Background:

    • Multiagent systems are crucial for complex tasks.
    • Achieving consensus in systems with dynamic parameters (Markov jump) is challenging.
    • Leader-follower configurations require robust state estimation and control.

    Purpose of the Study:

    • To solve the mean-square leader-follower consensus problem for multiagent Markov jump linear systems.
    • To develop observers for estimating the leader's state.
    • To design controllers for achieving consensus.

    Main Methods:

    • Construction of common and distributed adaptive observers to estimate the leader's state.
    • Design of synchronous and asynchronous controllers based on estimated states and follower information.
    • Utilizing Markov jump linear system theory for parameter variations.

    Main Results:

    • Successful estimation of the leader's state using the proposed observers.
    • Achievement of mean-square leader-follower consensus through designed controllers.
    • Demonstration of the feasibility and effectiveness of the proposed methods via simulations.

    Conclusions:

    • The developed observers and controllers effectively address the leader-follower consensus problem in complex systems.
    • The approach is robust to parameter uncertainties modeled by Markov jumps.
    • Simulation results validate the practical applicability of the proposed consensus strategies.