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1511 results in Control systems and optimization

Page 9 of 76

  • 2 - Fundamentals of Mathematics

  • Bingen Yang, University of Southern California, Inna Abramova, University of Southern California
  • Book:
    Dynamic Systems
    Published online:
    28 April 2023
    Print publication:
    24 November 2022, pp 16-113

  • Summary

    This chapter provides the reader with a brief refresher course on the mathematical apparatus crucial for modeling of dynamic systems. Sections 2.1 and 2.2 present basic concepts and terminology of vector and matrix algebra. Definitions and basic operations on complex numbers are introduced in Section 2.3. Section 2.4 is devoted to one of the important methods for solving differential equations – the Laplace transform. Sections 2.5 and 2.6 discuss the types of differential equations widely encountered in modeling of common dynamic systems and develop methods for solving these equations. Section 2.7 introduces the mathematical foundation for deriving transfer functions and creating block diagrams of various linear time-invariant dynamic systems. Section 2.8 presents a brief overview of solving differential equations numerically, with MATLAB and Wolfram Mathematica.

  • 4 - Electrical Systems

  • Bingen Yang, University of Southern California, Inna Abramova, University of Southern California
  • Book:
    Dynamic Systems
    Published online:
    28 April 2023
    Print publication:
    24 November 2022, pp 222-333

  • Summary

    Practically every modern engineered dynamic system has electrical components such as motors, sensors, controllers, or, at the very least, power sources. Therefore, an understanding of the physical processes occurring in the typical electrical circuits, and the ability to model behavior of an electrical subsystem are essential for anyone interested in dynamic systems.

  • 8 - Introduction to Feedback Control Systems

  • Bingen Yang, University of Southern California, Inna Abramova, University of Southern California
  • Book:
    Dynamic Systems
    Published online:
    28 April 2023
    Print publication:
    24 November 2022, pp 589-665

  • Summary

    Controls in engineering are efforts to change, design, or modify the behaviors of dynamic systems. Automatic control is control that involves only machines and devices, and that has no human intervention. Examples of automatic control are diverse, including room-temperature control, cruise control of cars, missile guidance, trajectory control of robots, control of appliances such as washing machines and refrigerators, and control of industrial processes like papermaking and steelmaking. In this chapter, for simplicity, an automatic control system is called a control system. Because the focus of this text is on the modeling and analysis of dynamic systems, only basic concepts of feedback control are introduced. For theories and methods about feedback control systems, one may refer to standard textbooks for control courses, including those listed at the end of the chapter.

Identification and Classical Control of Linear Multivariable Systems
  • V. Dhanya Ram, M. Chidambaram
  • Published online:
    31 July 2022
    Print publication:
    05 January 2023
  • Most systems involved in a chemical process plant are interactive multivariable systems, to control which, the transfer function matrix model is required. This lucid book considers the identification and control of such systems. It discusses open loop and closed loop identification methods, as well as the design of multivariable controllers based on steady state gain matrix. Simple methods for designing controllers based on transfer function matrix model have been reviewed. The design of controllers for non-square systems, and closed loop identification of multivariable unstable systems by the optimization method are also covered. Several simulation examples and exercise problems at the end of each chapter further help the reader consolidate the knowledge gained. This book will be useful to any engineering student, researcher or practitioner who works with interactive, multivariable control systems.

Data-Driven Science and Engineering
  • Machine Learning, Dynamical Systems, and Control
  • 2nd edition
  • Steven L. Brunton, J. Nathan Kutz
  • Published online:
    10 June 2022
    Print publication:
    05 May 2022
  • Data-driven discovery is revolutionizing how we model, predict, and control complex systems. Now with Python and MATLAB®, this textbook trains mathematical scientists and engineers for the next generation of scientific discovery by offering a broad overview of the growing intersection of data-driven methods, machine learning, applied optimization, and classical fields of engineering mathematics and mathematical physics. With a focus on integrating dynamical systems modeling and control with modern methods in applied machine learning, this text includes methods that were chosen for their relevance, simplicity, and generality. Topics range from introductory to research-level material, making it accessible to advanced undergraduate and beginning graduate students from the engineering and physical sciences. The second edition features new chapters on reinforcement learning and physics-informed machine learning, significant new sections throughout, and chapter exercises. Online supplementary material – including lecture videos per section, homeworks, data, and code in MATLAB®, Python, Julia, and R – available on databookuw.com.

Page 9 of 76