Scholarlink

This Brief aims to discuss the application of data-driven methods to control systems described by recurrent neural network models, which are known to be universal approximators of dynamical systems. The unified hybrid approach outlined here fills a significant gap in the current literature by combining the strengths of both direct and indirect methodologies so as to ensure in a purely data-driven fashion, on the one hand, desired performances and, on the other hand, closed-loop stability.

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Data-Based Control Design for Recurrent Neural Network Models with Stability Guarantees

  • 章节

  • 出版模式：

    开放获取

  • 发表日期：

    2025年

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  • William D’Amico

摘要

This Brief aims to discuss the application of data-driven methods to control systems described by recurrent neural network models, which are known to be universal approximators of dynamical systems. The unified hybrid approach outlined here fills a significant gap in the current literature by combining the strengths of both direct and indirect methodologies so as to ensure in a purely data-driven fashion, on the one hand, desired performances and, on the other hand, closed-loop stability.