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    A new policy-adjustable Q-learning (PA-QL) algorithm enables adaptive optimal tracking control for nonlinear systems. This method allows dynamic weight adjustment post-training for enhanced flexibility in control applications.

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    Area of Science:

    • Control Systems Engineering
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Optimal tracking control (OTC) is crucial for nonlinear discrete-time (DT) systems.
    • Existing methods often lack flexibility in adapting control policies after training.

    Purpose of the Study:

    • To introduce a novel policy-adjustable Q-learning (PA-QL) algorithm for enhanced OTC in nonlinear DT systems.
    • To develop a flexible control strategy adaptable to changing operational conditions and objectives.

    Main Methods:

    • A new iteration scheme integrates control weights into the neural network (NN) input.
    • The learning process is reformulated to express the optimal policy as a function of adjustable weights.
    • The algorithm allows for dynamic adjustment of control weights, both offline and online.

    Main Results:

    • The proposed PA-QL algorithm enables control policies unconstrained by fixed weights.
    • Dynamic weight adjustments can be seamlessly performed online, increasing system adaptability.
    • Theoretical analysis and simulation studies confirm the algorithm's effectiveness.

    Conclusions:

    • The PA-QL algorithm offers a flexible and adaptive solution for OTC problems in nonlinear DT systems.
    • This approach significantly enhances adaptability to evolving system dynamics and control requirements.