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    A novel double hidden layer recurrent neural network (DHLRNN) controller enhances dynamic system performance. This adaptive global sliding-mode controller offers improved accuracy, faster training, and superior dynamic capabilities for active power filters.

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    Area of Science:

    • Control Systems Engineering
    • Artificial Intelligence
    • Signal Processing

    Background:

    • Traditional neural networks (NNs) often have limitations in accuracy and training speed.
    • Recurrent neural networks (RNNs) offer potential for dynamic system control but can be complex.
    • Sliding-mode control provides robustness but can be sensitive to parameter variations.

    Purpose of the Study:

    • To design a novel double hidden layer recurrent neural network (DHLRNN) structure.
    • To develop an adaptive global sliding-mode controller utilizing the DHLRNN for dynamic systems.
    • To enhance the accuracy, generalization, and training speed of neural network controllers.

    Main Methods:

    • Designed a full-regulated neural network with a double hidden layer recurrent neural network (DHLRNN) structure.
    • Developed an adaptive global sliding-mode controller based on the DHLRNN.
    • Implemented theoretical guidance and adaptive adjustment mechanisms for Gaussian function parameters within the DHLRNN.
    • Utilized an output feedback neural structure for associative memory and rapid convergence.
    • Performed simulations and experiments on an active power filter.

    Main Results:

    • The DHLRNN demonstrated improved accuracy and generalization ability compared to single hidden layer NNs.
    • Network training speed was accelerated due to reduced network weights and enhanced fitting capabilities.
    • The proposed controller exhibited excellent static and dynamic performances in active power filter applications.
    • The DHLRNN-based controller showed superior approximation performance and a more stable internal state than other schemes.

    Conclusions:

    • The designed DHLRNN structure and adaptive global sliding-mode controller are effective for dynamic systems.
    • The proposed approach offers significant improvements in performance metrics like accuracy, speed, and stability.
    • The controller's capabilities were validated through simulations and experimental results on an active power filter.