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Output feedback reinforcement learni...

Rizvi, Syed Ali Asad.

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Output feedback reinforcement learning control for linear systems

Record Type:	Electronic resources : Monograph/item
Title/Author:	Output feedback reinforcement learning control for linear systems/ by Syed Ali Asad Rizvi, Zongli Lin.
Author:	Rizvi, Syed Ali Asad.
other author:	Lin, Zongli.
Published:	Cham :Springer International Publishing : : 2023.,
Description:	xvi, 294 p. :ill., digital ;24 cm.
[NT 15003449]:	Preface -- Introduction to Optimal Control and Reinforcement Learning -- Model-Free Design of Linear Quadratic Regulator -- Model-Free H-infinity Disturbance Rejection and Linear Quadratic Zero-Sum Games -- Model-Free Stabilization in the Presence of Actuator Saturation -- Model-Free Control of Time Delay Systems -- Model-Free Optimal Tracking Control and Multi-Agent Synchronization -- Index.
Contained By:	Springer Nature eBook
Subject:	Feedback control systems. -
Online resource:	https://doi.org/10.1007/978-3-031-15858-2
ISBN:	9783031158582

Output feedback reinforcement learning control for linear systems
Rizvi, Syed Ali Asad.

Output feedback reinforcement learning control for linear systems[electronic resource] /by Syed Ali Asad Rizvi, Zongli Lin. - Cham :Springer International Publishing :2023. - xvi, 294 p. :ill., digital ;24 cm. - Control engineering,2373-7727. - Control engineering..

Preface -- Introduction to Optimal Control and Reinforcement Learning -- Model-Free Design of Linear Quadratic Regulator -- Model-Free H-infinity Disturbance Rejection and Linear Quadratic Zero-Sum Games -- Model-Free Stabilization in the Presence of Actuator Saturation -- Model-Free Control of Time Delay Systems -- Model-Free Optimal Tracking Control and Multi-Agent Synchronization -- Index.

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.

ISBN: 9783031158582

Standard No.: 10.1007/978-3-031-15858-2doiSubjects--Topical Terms:

531860
Feedback control systems.

LC Class. No.: TJ216

Dewey Class. No.: 629.83

Output feedback reinforcement learning control for linear systems
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245 1 0 $a Output feedback reinforcement learning control for linear systems $h [electronic resource] / $c by Syed Ali Asad Rizvi, Zongli Lin.
260 $a Cham : $b Springer International Publishing : $b Imprint: Birkhäuser, $c 2023.
300 $a xvi, 294 p. : $b ill., digital ; $c 24 cm.
490 1 $a Control engineering, $x 2373-7727
505 0 $a Preface -- Introduction to Optimal Control and Reinforcement Learning -- Model-Free Design of Linear Quadratic Regulator -- Model-Free H-infinity Disturbance Rejection and Linear Quadratic Zero-Sum Games -- Model-Free Stabilization in the Presence of Actuator Saturation -- Model-Free Control of Time Delay Systems -- Model-Free Optimal Tracking Control and Multi-Agent Synchronization -- Index.
520 $a 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.
650 0 $a Feedback control systems. $3 531860
650 0 $a Control theory. $3 535880
650 0 $a Reinforcement learning. $3 1006373
650 1 4 $a Systems Theory, Control. $3 893834
650 2 4 $a Control and Systems Theory. $3 3381515
650 2 4 $a Calculus of Variations and Optimization. $3 3538813
700 1 $a Lin, Zongli. $3 1070914
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773 0 $t Springer Nature eBook
830 0 $a Control engineering. $3 2054295
856 4 0 $u https://doi.org/10.1007/978-3-031-15858-2
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