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Self-Learning Optimal Control of Nonlinear Systems : Adaptive Dynamic Program...

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Item specifics

Condition
Like New: A book that looks new but has been read. Cover has no visible wear, and the dust jacket ...
Book Title
Self-Learning Optimal Control of Nonlinear Systems : Adaptive Dyn
ISBN
9789811040795
Subject Area
Mathematics, Computers, Technology & Engineering, Science
Publication Name
Self-Learning Optimal Control of Nonlinear Systems : Adaptive Dynamic Programming Approach
Publisher
Springer
Item Length
9.3 in
Subject
Engineering (General), Mechanics / Dynamics, Intelligence (Ai) & Semantics, Electrical, Optimization
Publication Year
2017
Series
Studies in Systems, Decision and Control Ser.
Type
Textbook
Format
Hardcover
Language
English
Author
Benkai Li, Xiaofeng Lin, Qinglai Wei, Ruizhuo Song
Item Weight
22.6 Oz
Item Width
6.1 in
Number of Pages
Xviii, 230 Pages

About this product

Product Identifiers

Publisher
Springer
ISBN-10
9811040796
ISBN-13
9789811040795
eBay Product ID (ePID)
234221880

Product Key Features

Number of Pages
Xviii, 230 Pages
Language
English
Publication Name
Self-Learning Optimal Control of Nonlinear Systems : Adaptive Dynamic Programming Approach
Publication Year
2017
Subject
Engineering (General), Mechanics / Dynamics, Intelligence (Ai) & Semantics, Electrical, Optimization
Type
Textbook
Subject Area
Mathematics, Computers, Technology & Engineering, Science
Author
Benkai Li, Xiaofeng Lin, Qinglai Wei, Ruizhuo Song
Series
Studies in Systems, Decision and Control Ser.
Format
Hardcover

Dimensions

Item Weight
22.6 Oz
Item Length
9.3 in
Item Width
6.1 in

Additional Product Features

Dewey Edition
23
Reviews
"Book contains various real-world examples to illustrate the developed mathematical analysis. Thus, it is a valuable and important guide for engineers, researchers, and students in systems, decision and control science." (Savin Treanta, zbMATH 1403.49002, 2019)
Series Volume Number
103
Number of Volumes
1 vol.
Illustrated
Yes
Dewey Decimal
629.836
Table Of Content
Chapter 1. Principle of Adaptive Dynamic Programming.- Chapter 2. An Iterative -Optimal Control Scheme for a Class of Discrete-Time Nonlinear Systems With UnFixed Initial State.- Chapter 3. Discrete-Time Optimal Control of Nonlinear Systems Via Value Iteration-Based Q-Learning.- Chapter 4. A Novel Policy Iteration Based Deterministic Q-Learning for Discrete-Time Nonlinear Systems.- Chapter 5. Nonlinear Neuro-Optimal Tracking Control Via Stable Iterative Q-Learning Algorithm.- Chapter 6. Model-Free Multiobjective Adaptive Dynamic Programming for Discrete-Time Nonlinear Systems with General Performance Index Functions.- Chapter 7. Multi-Objective Optimal Control for a Class of Unknown Nonlinear Systems Based on Finite-Approximation-Error ADP Algorithm.- Chapter 8. A New Approach for a Class of Continuous-Time Chaotic Systems Optimal Control by Online ADP Algorithm.- Chapter 9. O-Policy IRL Optimal Tracking Control for Continuous-Time Chaotic Systems.- Chapter 10. ADP-Based Optimal Sensor Scheduling for Target Tracking in Energy Harvesting Wireless Sensor Networks.
Synopsis
Chapter 1. Principle of Adaptive Dynamic Programming.- Chapter 2. An Iterative -Optimal Control Scheme for a Class of Discrete-Time Nonlinear Systems With UnFixed Initial State.- Chapter 3. Discrete-Time Optimal Control of Nonlinear Systems Via Value Iteration-Based Q-Learning.- Chapter 4. A Novel Policy Iteration Based Deterministic Q-Learning for Discrete-Time Nonlinear Systems.- Chapter 5. Nonlinear Neuro-Optimal Tracking Control Via Stable Iterative Q-Learning Algorithm.- Chapter 6. Model-Free Multiobjective Adaptive Dynamic Programming for Discrete-Time Nonlinear Systems with General Performance Index Functions.- Chapter 7. Multi-Objective Optimal Control for a Class of Unknown Nonlinear Systems Based on Finite-Approximation-Error ADP Algorithm.- Chapter 8. A New Approach for a Class of Continuous-Time Chaotic Systems Optimal Control by Online ADP Algorithm.- Chapter 9. O-Policy IRL Optimal Tracking Control for Continuous-Time Chaotic Systems.- Chapter 10. ADP-Based Optimal Sensor Scheduling for Target Tracking in Energy Harvesting Wireless Sensor Networks., This book presents a class of novel, self-learning, optimal control schemes based on adaptive dynamic programming techniques, which quantitatively obtain the optimal control schemes of the systems. It analyzes the properties identified by the programming methods, including the convergence of the iterative value functions and the stability of the system under iterative control laws, helping to guarantee the effectiveness of the methods developed. When the system model is known, self-learning optimal control is designed on the basis of the system model; when the system model is not known, adaptive dynamic programming is implemented according to the system data, effectively making the performance of the system converge to the optimum. With various real-world examples to complement and substantiate the mathematical analysis, the book is a valuable guide for engineers, researchers, and students in control science and engineering.
LC Classification Number
TJ212-225

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