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    Area of Science:

    • Control Systems Engineering
    • Artificial Intelligence
    • Nonlinear Dynamics

    Background:

    • Switched systems present unique control challenges due to their hybrid nature.
    • Data-based control design is crucial for systems where models are uncertain or complex.
    • Approximate dynamic programming (ADP) offers a powerful framework for solving complex control problems.

    Purpose of the Study:

    • To develop a data-based optimal switching and control codesign method for discrete-time nonlinear switched systems.
    • To ensure the admissibility of the designed hybrid control policy for practical implementation.
    • To provide a rigorous convergence proof for the proposed algorithm.

    Main Methods:

    • A two-stage approximate dynamic programming (ADP) algorithm is employed, involving offline policy improvement and evaluation.
    • System input/output data is utilized to iteratively determine the optimal hybrid control policy.
    • An actor-critic neural network (NN) structure with multiple NNs approximates Q-functions and control policies for subsystems.

    Main Results:

    • The proposed two-stage ADP algorithm guarantees convergence for optimal hybrid control policy design.
    • An admissibility criterion is derived and applied to ensure practical viability of the control policy.
    • Numerical simulations demonstrate the effectiveness of the developed data-based control strategy.

    Conclusions:

    • The data-based optimal switching and control codesign via two-stage ADP is effective for nonlinear switched systems.
    • The developed method ensures an admissible hybrid control policy, crucial for real-world applications.
    • The actor-critic NN implementation facilitates practical realization of the proposed control strategy.