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Learning and Guaranteed Cost Control With Event-Based Adaptive Critic Implementation.

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    This study introduces an event-triggered control for nonlinear systems, balancing performance and communication limits using self-learning. It ensures system stability with reduced data transmission.

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    Area of Science:

    • Control Theory
    • Artificial Intelligence
    • Nonlinear Systems

    Background:

    • Traditional control systems often require continuous communication, straining resources.
    • Guaranteed cost control aims to minimize performance degradation under uncertainties.
    • Event-triggered control reduces communication load by transmitting data only when necessary.

    Purpose of the Study:

    • To design an event-triggered guaranteed cost control for nonlinear systems with matched uncertainties.
    • To balance control performance with communication efficiency.
    • To develop a self-learning approach for implementing the control strategy.

    Main Methods:

    • Formulating the control design as an optimal control problem with an event-based mechanism.
    • Utilizing a critic neural network for implementing the event-based optimal control.
    • Analyzing the guaranteed cost performance against time-based control.

    Main Results:

    • An event-based guaranteed cost control strategy for nonlinear systems was successfully developed.
    • The approach effectively balances guaranteed cost performance and communication resource limitations.
    • Stability of the nonlinear system is guaranteed through the critic neural network implementation.

    Conclusions:

    • The proposed event-triggered control offers an efficient solution for nonlinear systems with uncertainties.
    • Self-learning techniques, specifically critic neural networks, are effective for implementing such control strategies.
    • This method provides a practical approach for systems with constrained communication resources.