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    This study introduces a decentralized event-triggered tracking control (ETTC) strategy for complex nonlinear large-scale interconnected systems (LSIS). The method enhances control efficiency by optimizing communication and computation, ensuring system stability.

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    Area of Science:

    • Control Systems Engineering
    • Nonlinear System Dynamics
    • Artificial Intelligence in Control

    Background:

    • Large-scale interconnected systems (LSIS) present significant control challenges due to increasing complexity.
    • Existing control strategies often struggle with unmatched interconnected terms and asymmetric input constraints.
    • Efficient resource utilization (communication bandwidth, computation) is critical for practical LSIS control.

    Purpose of the Study:

    • To develop a novel decentralized event-triggered tracking control (ETTC) strategy for continuous-time nonlinear LSIS.
    • To address challenges posed by unmatched interconnected terms and asymmetric input constraints.
    • To reduce communication and computational load through event-triggered mechanisms.

    Main Methods:

    • Auxiliary subsystems were designed to handle unmatched cross-linking terms.
    • A nominal augmented subsystem was constructed by combining tracking error and exosystem dynamics.
    • Nonquadratic performance functions and adaptive dynamic programming (ADP) with critic-only networks were employed.
    • Particle swarm optimization algorithm (PSOA) was integrated to enhance neural network training.
    • Lyapunov extension theorem was used to prove system stability and bounded tracking errors.

    Main Results:

    • The proposed ETTC strategy effectively manages unmatched interconnected terms and asymmetric input constraints.
    • Event-triggered conditions significantly conserve communication bandwidth and computational resources.
    • The adaptive dynamic programming approach successfully solved the associated Hamilton-Jacobi-Bellman equation.
    • Uniformly ultimately bounded (UUB) tracking errors and neural network weights were mathematically proven.

    Conclusions:

    • The novel decentralized ETTC strategy provides a robust and efficient solution for complex nonlinear LSIS.
    • The integration of event-triggering and ADP with PSOA optimization offers a promising approach for resource-constrained control.
    • Simulation results validate the effectiveness of the proposed method for unmatched interconnected systems.