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Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
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    A novel adaptive tracking control method using goal representation heuristic dynamic programming (GrHDP) enhances nonlinear system performance. This data-driven approach offers faster learning and superior control compared to existing heuristic dynamic programming (HDP) methods.

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    Area of Science:

    • Control Systems Engineering
    • Artificial Intelligence
    • Nonlinear Dynamics

    Background:

    • Continuous-time nonlinear systems present significant control challenges.
    • Existing heuristic dynamic programming (HDP) methods have limitations in tracking performance.
    • Adaptive control strategies are crucial for optimizing system responses.

    Purpose of the Study:

    • To propose a data-driven adaptive tracking control approach for continuous-time nonlinear systems.
    • To introduce a novel goal representation heuristic dynamic programming (GrHDP) architecture.
    • To analyze the stability and performance of the proposed multivariable tracking scheme.

    Main Methods:

    • Utilized a filter-based action network (FAN) to observe system functions and generate control actions.
    • Implemented a goal network for adaptive internal reward signal generation.
    • Employed a critic network to approximate the cost function using the internal reward signal.
    • Conducted stability analysis in a continuous-time fashion.

    Main Results:

    • The proposed GrHDP approach demonstrated improved tracking performance over the conventional HDP method.
    • Simulation results on two multivariable tracking control examples confirmed superior learning speed.
    • The scheme achieved better overall control performance under identical settings.

    Conclusions:

    • The developed GrHDP-based adaptive tracking control scheme is effective for continuous-time nonlinear systems.
    • The filter-based action network and adaptive reward mechanism contribute to enhanced control capabilities.
    • This approach offers a promising alternative for complex system control with improved efficiency and performance.