Output feedback reinforcement learning control for linear systems
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...
| Main Author: | Rizvi, Syed Ali Asad. |
|---|---|
| Other Authors: | Lin, Zongli, 1964-, SpringerLink (Online service) |
| Format: | eBook |
| Language: | English |
| Published: |
Cham :
Birkhäuser,
2023.
Cham : 2023. |
| Physical Description: |
1 online resource (304 pages). |
| Series: |
Control engineering (Birkhäuser)
|
| Subjects: |
| LEADER | 07110cam a2200817 a 4500 | ||
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| 100 | 1 | |a Rizvi, Syed Ali Asad. | |
| 245 | 1 | 0 | |a Output feedback reinforcement learning control for linear systems / |c Syed Ali Asad Rizvi, Zongli Lin. |
| 260 | |a Cham : |b Birkhäuser, |c 2023. | ||
| 264 | 1 | |a Cham : |b Birkhäuser, |c 2023. | |
| 300 | |a 1 online resource (304 pages). | ||
| 336 | |a text |b txt |2 rdacontent. | ||
| 337 | |a computer |b c |2 rdamedia. | ||
| 338 | |a online resource |b cr |2 rdacarrier. | ||
| 490 | 1 | |a Control Engineering. | |
| 505 | 0 | |a Intro -- Preface -- Contents -- Notation and Acronyms -- 1 Introduction to Optimal Control and Reinforcement Learning -- 1.1 Introduction -- 1.2 Optimal Control of Dynamic Systems -- 1.2.1 Dynamic Programming Method -- 1.2.2 The Linear Quadratic Regulation Problem -- 1.2.3 Iterative Numerical Methods -- 1.3 Reinforcement Learning Based Optimal Control -- 1.3.1 Principles of Reinforcement Learning -- 1.3.2 Reinforcement Learning for Automatic Control -- 1.3.3 Advantages of Reinforcement Learning Control -- Optimality and Adaptivity -- Model-Free Control -- Large Spectrum of Applications. | |
| 505 | 8 | |a 1.3.4 Limitations of Reinforcement Learning Control -- 1.3.5 Reinforcement Learning Algorithms -- 1.4 Recent Developments and Challenges in Reinforcement Learning Control -- 1.4.1 State Feedback versus Output Feedback Designs -- 1.4.2 Exploration Signal/Noise and Estimation Bias -- 1.4.3 Discounted versus Undiscounted Cost Functions -- 1.4.4 Requirement of a Stabilizing Initial Policy -- 1.4.5 Optimal Tracking Problems -- 1.4.6 Reinforcement Learning in Continuous-Time -- 1.4.7 Disturbance Rejection -- 1.4.8 Distributed Reinforcement Learning -- 1.5 Notes and References. | |
| 505 | 8 | |a 2 Model-Free Design of Linear Quadratic Regulator -- 2.1 Introduction -- 2.2 Literature Review -- 2.3 Discrete-Time LQR Problem -- 2.3.1 Iterative Schemes Based on State Feedback -- 2.3.2 Model-Free Output Feedback Solution -- 2.3.3 State Parameterization of Discrete-Time Linear Systems -- 2.3.4 Output Feedback Q-function for LQR -- 2.3.5 Output Feedback Based Q-learning for the LQR Problem -- 2.3.6 Numerical Examples -- 2.4 Continuous-Time LQR Problem -- 2.4.1 Model-Based Iterative Schemes for the LQR Problem -- 2.4.2 Model-Free Schemes Based on State Feedback. | |
| 505 | 8 | |a 2.4.3 Model-Free Output Feedback Solution -- 2.4.4 State Parameterization -- 2.4.5 Learning Algorithms for Continuous-Time Output Feedback LQR Control -- 2.4.6 Exploration Bias Immunity of the Output Feedback Learning Algorithms -- 2.4.7 Numerical Examples -- 2.5 Summary -- 2.6 Notes and References -- 3 Model-Free H{u221E} Disturbance Rejection and Linear Quadratic Zero-Sum Games -- 3.1 Introduction -- 3.2 Literature Review -- 3.3 Discrete-Time Zero-Sum Game and H{u221E} Control Problem -- 3.3.1 Model-Based Iterative Algorithms. | |
| 505 | 8 | |a 3.3.2 State Parameterization of Discrete-Time Linear Systems Subject to Disturbances -- 3.3.3 Output Feedback Q-function for Zero-Sum Game -- 3.3.4 Output Feedback Based Q-learning for Zero-Sum Game and H{u221E} Control Problem -- 3.3.5 A Numerical Example -- 3.4 Continuous-Time Zero-Sum Game and H{u221E} Control Problem -- 3.4.1 Model-Based Iterative Schemes for Zero-Sum Game and H{u221E} Control Problem -- 3.4.2 Model-Free Schemes Based on State Feedback -- 3.4.3 State Parameterization -- 3.4.4 Learning Algorithms for Output Feedback Differential Zero-Sum Game and H{u221E} Control Problem. | |
| 500 | |a 3.4.5 Exploration Bias Immunity of the Output Feedback Learning Algorithms. | ||
| 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. | ||
| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Online resource; title from PDF title page (SpringerLink, viewed December 12, 2022). | |
| 650 | 0 | |a Feedback control systems. | |
| 650 | 0 | |a Control theory. | |
| 650 | 0 | |a Reinforcement learning. | |
| 650 | 6 | |a Systèmes à réaction. | |
| 650 | 6 | |a Théorie de la commande. | |
| 650 | 6 | |a Apprentissage par renforcement (Intelligence artificielle) | |
| 650 | 7 | |a Control theory. |2 fast. | |
| 650 | 7 | |a Feedback control systems. |2 fast. | |
| 650 | 7 | |a Reinforcement learning. |2 fast. | |
| 700 | 1 | |a Lin, Zongli, |d 1964- |1 https://id.oclc.org/worldcat/entity/E39PBJpV9vPW8Cm4CtRX87w3cP. | |
| 710 | 2 | |a SpringerLink (Online service) | |
| 776 | 0 | 8 | |i Print version: |a Rizvi, Syed Ali Asad |t Output Feedback Reinforcement Learning Control for Linear Systems |d Cham : Springer International Publishing AG,c2022 |z 9783031158575. |
| 830 | 0 | |a Control engineering (Birkhäuser) | |
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