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    Area of Science:

    • Control Engineering
    • Nonlinear Systems Theory
    • Optimization

    Background:

    • Distributed Model Predictive Control (DMPC) is crucial for managing complex systems.
    • Existing DMPC methods face challenges with perturbed nonlinear systems and constraints.
    • Event-triggered control aims to reduce communication load in distributed systems.

    Purpose of the Study:

    • To develop a novel compound event-triggered DMPC strategy for perturbed coupled nonlinear systems.
    • To address state and control input constraint satisfaction under disturbances and system coupling.
    • To reduce the frequency of control signal transmissions and triggering instants.

    Main Methods:

    • A compound event-triggered DMPC strategy with a novel triggering condition.
    • A new constraint tightening approach to ensure state constraint satisfaction.
    • An open-loop prediction scheme for states within the terminal set to avoid periodic transmissions.
    • Parallel checking of two stability-related conditions, relaxing Lyapunov function decrease requirements.

    Main Results:

    • Significant reduction in the number of triggering and transmission instants.
    • Effective satisfaction of state constraints despite external disturbances and dynamical coupling.
    • Demonstrated stability and performance improvements through simulations.

    Conclusions:

    • The proposed event-triggered DMPC strategy effectively controls perturbed coupled nonlinear systems.
    • The novel approach enhances system performance by reducing communication overhead and ensuring constraint satisfaction.
    • The method offers a promising solution for real-world applications requiring efficient and robust control.