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Neural Network-Based Sliding Mode Control for Semi-Markov Jumping Systems With Singular Perturbation.

Jun Cheng, Jiangming Xu, Huaicheng Yan

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    Summary
    This summary is machine-generated.

    This study introduces a dynamic event-triggered protocol for semi-Markov jumping systems, enhancing control performance and reducing triggers. The sliding mode control ensures system stability and reachability.

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    Area of Science:

    • Control Systems Engineering
    • Stochastic Systems Analysis

    Background:

    • Semi-Markov jumping systems (SMSPSs) exhibit complex mode-switching dynamics.
    • Event-triggered protocols (ETPs) aim to optimize control resource utilization.

    Purpose of the Study:

    • To develop a novel dynamic ETP for SMSPSs with singular perturbation.
    • To ensure system stability and performance while minimizing control signal transmissions.

    Main Methods:

    • A parameter-based dynamic ETP incorporating radial basis function neural network (RBFNN) weight estimation and internal dynamic variables.
    • Lyapunov's theory to establish stability criteria.
    • Sliding mode control (SMC) design with a convergence factor for reachability.

    Main Results:

    • Sufficient criteria for mean-square exponential stability of the closed-loop system were derived.
    • The proposed dynamic ETP effectively reduces the triggering frequency.
    • The SMC scheme guarantees system reachability.

    Conclusions:

    • The developed control methodology is effective and applicable for semi-Markov singularly perturbed systems.
    • The integration of dynamic ETP and SMC offers a robust control solution.
    • The approach balances performance with reduced communication load.