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Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
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    This study addresses Pareto optimal control for nonlinear systems with input saturation using adaptive dynamic programming and a dynamic event-triggered mechanism (DETM). The method ensures system safety and conserves resources by avoiding Zeno phenomena.

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    Area of Science:

    • Control Systems Engineering
    • Game Theory
    • Optimization

    Background:

    • Nonlinear game systems with asymmetric input saturation present control challenges.
    • Ensuring safety and optimizing resource usage are critical in complex control systems.

    Purpose of the Study:

    • To develop a method for achieving Pareto optimal solutions in nonlinear game systems with input saturation.
    • To integrate a dynamic event-triggered mechanism (DETM) for resource efficiency.
    • To guarantee system safety and signal boundedness.

    Main Methods:

    • Utilizing barrier functions to transform systems with safety constraints.
    • Constructing a united cost function with nonquadratic utility.
    • Applying adaptive dynamic programming with concurrent learning for strategy approximation.
    • Integrating DETM to minimize computational and communication load and avoid Zeno phenomena.

    Main Results:

    • The proposed method effectively approximates Pareto optimal strategies.
    • The dynamic event-triggered mechanism reduces resource consumption while preventing Zeno behavior.
    • All signals in the closed-loop system are proven to be uniformly ultimately bounded.

    Conclusions:

    • The study presents an effective approach for Pareto optimal control in challenging nonlinear systems.
    • The integration of DETM offers significant advantages in resource management.
    • Simulation results validate the proposed method's effectiveness and robustness.