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Output Feedback Reinforcement Learning Control for Linear Systems

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  • © 2023

Overview

Authors:
  1. Syed Ali Asad Rizvi

    1. Electrical and Computer Engineering, Tennessee Technological University, Cookeville, USA

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    You can also search for this author in PubMed   Google Scholar

  2. Zongli Lin

    1. Electrical and Computer Engineering, University of Virginia, Charlottesville, USA

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    You can also search for this author in PubMed   Google Scholar

  • Demonstrates new methods for the design of control systems based on reinforcement learning
  • Presents new new approaches to dealing with disturbance rejections, control constraints, and time delays
  • Incorporates ideas from game theory to solve output feedback disturbance rejection problems

Part of the book series: Control Engineering (CONTRENGIN)

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Table of contents (6 chapters)

  1. Front Matter

    Pages i-xvi
    Download chapter PDF

  2. Introduction to Optimal Control and Reinforcement Learning

    • Syed Ali Asad Rizvi, Zongli Lin
    Pages 1-25

  3. Model-Free Design of Linear Quadratic Regulator

    • Syed Ali Asad Rizvi, Zongli Lin
    Pages 27-96

  4. Model-Free H∞ Disturbance Rejection and Linear Quadratic Zero-Sum Games

    • Syed Ali Asad Rizvi, Zongli Lin
    Pages 97-161

  5. Model-Free Stabilization in the Presence of Actuator Saturation

    • Syed Ali Asad Rizvi, Zongli Lin
    Pages 163-224

  6. Model-Free Control of Time Delay Systems

    • Syed Ali Asad Rizvi, Zongli Lin
    Pages 225-255

  7. Model-Free Optimal Tracking Control and Multi-Agent Synchronization

    • Syed Ali Asad Rizvi, Zongli Lin
    Pages 257-281

  8. Back Matter

    Pages 283-294
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Keywords

About this book

This monograph explores the analysis and design of model-free optimal control systems based on reinforcement learning (RL) theory, presenting new methods that overcome recent challenges faced by RL.  New developments in the design of sensor data efficient RL algorithms are demonstrated that not only reduce the requirement of sensors by means of output feedback, but also ensure optimality and stability guarantees.  A variety of practical challenges are considered, including disturbance rejection, control constraints, and communication delays.  Ideas from game theory are incorporated to solve output feedback disturbance rejection problems, and the concepts of low gain feedback control are employed to develop RL controllers that achieve global stability under control constraints.


Output Feedback Reinforcement Learning Control for Linear Systems will be a valuable reference for graduate students, control theorists working on optimal control systems, engineers, and applied mathematicians.

Authors and Affiliations

  • Electrical and Computer Engineering, Tennessee Technological University, Cookeville, USA

    Syed Ali Asad Rizvi

  • Electrical and Computer Engineering, University of Virginia, Charlottesville, USA

    Zongli Lin

Bibliographic Information

  • Book Title: Output Feedback Reinforcement Learning Control for Linear Systems

  • Authors: Syed Ali Asad Rizvi, Zongli Lin

  • Series Title: Control Engineering

  • DOI: https://doi.org/10.1007/978-3-031-15858-2

  • Publisher: Birkhäuser Cham

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: Springer Nature Switzerland AG 2023

  • Hardcover ISBN: 978-3-031-15857-5Published: 30 November 2022

  • Softcover ISBN: 978-3-031-15860-5Published: 30 November 2023

  • eBook ISBN: 978-3-031-15858-2Published: 29 November 2022

  • Series ISSN: 2373-7719

  • Series E-ISSN: 2373-7727

  • Edition Number: 1

  • Number of Pages: XVI, 294

  • Topics: Systems Theory, Control, Control and Systems Theory, Optimization

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