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This chapter introduced the general problem addressed in this book, that is, the design of robust and adaptive control strategies for nonlinear, deterministic systems of ordinary differential equations affected by uncertainties, which are assumed to lie in some native space. The proposed framework is unique not only for its ability to forecast the performance of the controller as an explicit function of key properties of the native space elected to capture the functional uncertainties. The proposed framework is unique for having merged in a unique manner dynamical systems theory, machine learning theory, and approximation theory, and having extracted essential tools from each of these three macro research areas. This chapter is closed by a brief description of the content of this book to allow readers to choose their strategy for addressing the proposed topics.

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Introduction

  • 章节

  • 发表日期：

    2025年

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  • Andrew J. Kurdila,
  • Andrea L’Afflitto,
  • John A. Burns

摘要

This chapter introduced the general problem addressed in this book, that is, the design of robust and adaptive control strategies for nonlinear, deterministic systems of ordinary differential equations affected by uncertainties, which are assumed to lie in some native space. The proposed framework is unique not only for its ability to forecast the performance of the controller as an explicit function of key properties of the native space elected to capture the functional uncertainties. The proposed framework is unique for having merged in a unique manner dynamical systems theory, machine learning theory, and approximation theory, and having extracted essential tools from each of these three macro research areas. This chapter is closed by a brief description of the content of this book to allow readers to choose their strategy for addressing the proposed topics.