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The Resource Self-Learning Optimal Control of Nonlinear Systems : Adaptive Dynamic Programming Approach

Self-Learning Optimal Control of Nonlinear Systems : Adaptive Dynamic Programming Approach

Label
Self-Learning Optimal Control of Nonlinear Systems : Adaptive Dynamic Programming Approach
Title
Self-Learning Optimal Control of Nonlinear Systems
Title remainder
Adaptive Dynamic Programming Approach
Creator
Contributor
Subject
Language
eng
Member of
Cataloging source
MiAaPQ
Literary form
non fiction
Nature of contents
dictionaries
Series statement
Studies in Systems, Decision and Control Ser.
Series volume
v.103
Self-Learning Optimal Control of Nonlinear Systems : Adaptive Dynamic Programming Approach
Label
Self-Learning Optimal Control of Nonlinear Systems : Adaptive Dynamic Programming Approach
Link
http://libproxy.rpi.edu/login?url=https://ebookcentral.proquest.com/lib/rpi/detail.action?docID=4877517
Publication
Copyright
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Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • Preface -- Background of this Book -- The Content of this Book -- Acknowledgements -- Contents -- List of Symbols -- 1 Principle of Adaptive Dynamic Programming -- 1.1 Dynamic Programming -- 1.1.1 Discrete-Time Systems -- 1.1.2 Continuous-Time Systems -- 1.2 Original Forms of Adaptive Dynamic Programming -- 1.2.1 Principle of Adaptive Dynamic Programming -- 1.3 Iterative Forms of Adaptive Dynamic Programming -- 1.3.1 Value Iteration -- 1.3.2 Policy Iteration -- 1.4 About This Book -- References -- 2 An Iterative f-Optimal Control Scheme for a Class of Discrete-Time Nonlinear Systems with Unfixed Initial State -- 2.1 Introduction -- 2.2 Problem Statement -- 2.3 Properties of the Iterative Adaptive Dynamic Programming Algorithm -- 2.3.1 Derivation of the Iterative ADP Algorithm -- 2.3.2 Properties of the Iterative ADP Algorithm -- 2.4 The f-Optimal Control Algorithm -- 2.4.1 The Derivation of the f-Optimal Control Algorithm -- 2.4.2 Properties of the f-Optimal Control Algorithm -- 2.4.3 The f-Optimal Control Algorithm for Unfixed Initial State -- 2.4.4 The Expressions of the f-Optimal Control Algorithm -- 2.5 Neural Network Implementation for the f-Optimal Control Scheme -- 2.5.1 The Critic Network -- 2.5.2 The Action Network -- 2.6 Simulation Study -- 2.7 Conclusions -- References -- 3 Discrete-Time Optimal Control of Nonlinear Systems via Value Iteration-Based Q-Learning -- 3.1 Introduction -- 3.2 Preliminaries and Assumptions -- 3.2.1 Problem Formulations -- 3.2.2 Derivation of the Discrete-Time Q-Learning Algorithm -- 3.3 Properties of the Discrete-Time Q-Learning Algorithm -- 3.3.1 Non-Discount Case -- 3.3.2 Discount Case -- 3.4 Neural Network Implementation for the Discrete-Time Q-Learning Algorithm -- 3.4.1 The Action Network -- 3.4.2 The Critic Network -- 3.4.3 Training Phase -- 3.5 Simulation Study -- 3.5.1 Example 1 -- 3.5.2 Example 2
  • 3.6 Conclusion -- References -- 4 A Novel Policy Iteration-Based Deterministic Q-Learning for Discrete-Time Nonlinear Systems -- 4.1 Introduction -- 4.2 Problem Formulation -- 4.3 Policy Iteration-Based Deterministic Q-Learning Algorithm {u0083} -- 4.3.1 Derivation of the Policy Iteration-Based Deterministic Q-Learning Algorithm -- 4.3.2 Properties of the Policy Iteration-Based Deterministic Q-Learning Algorithm -- 4.4 Neural Network Implementation for the Policy Iteration-Based Deterministic Q-Learning Algorithm -- 4.4.1 The Critic Network -- 4.4.2 The Action Network -- 4.4.3 Summary of the Policy Iteration-Based Deterministic Q-Learning Algorithm -- 4.5 Simulation Study -- 4.5.1 Example 1 -- 4.5.2 Example 2 -- 4.6 Conclusion -- References -- 5 Nonlinear Neuro-Optimal Tracking Control via Stable Iterative Q-Learning Algorithm -- 5.1 Introduction -- 5.2 Problem Statement -- 5.3 Policy Iteration Q-Learning Algorithm for Optimal Tracking Control -- 5.4 Properties of the Policy Iteration Q-Learning Algorithm -- 5.5 Neural Network Implementation for the Policy Iteration Q-Learning Algorithm -- 5.5.1 The Critic Network -- 5.5.2 The Action Network -- 5.6 Simulation Study -- 5.6.1 Example 1 -- 5.6.2 Example 2 -- 5.7 Conclusions -- References -- 6 Model-Free Multiobjective Adaptive Dynamic Programming for Discrete-Time Nonlinear Systems with General Performance Index Functions -- 6.1 Introduction -- 6.2 Preliminaries -- 6.3 Multiobjective Adaptive Dynamic Programming Method -- 6.4 Model-Free Incremental Q-Learning Method -- 6.4.1 Derivation of the Incremental Q-Learning Method -- 6.5 Neural Network Implementation for the Incremental Q-Learning Method -- 6.5.1 The Critic Network -- 6.5.2 The Action Network -- 6.5.3 The Procedure of the Model-Free Incremental Q-learning Method -- 6.6 Convergence Proof -- 6.7 Simulation Study -- 6.7.1 Example 1 -- 6.7.2 Example 2
  • 6.8 Conclusion -- References -- 7 Multiobjective Optimal Control for a Class of Unknown Nonlinear Systems Based on Finite-Approximation-Error ADP Algorithm -- 7.1 Introduction -- 7.2 General Formulation -- 7.3 Optimal Solution Based on Finite-Approximation-Error ADP -- 7.3.1 Data-Based Identifier of Unknown System Dynamics -- 7.3.2 Derivation of the ADP Algorithm with Finite Approximation Errors -- 7.3.3 Convergence Analysis of the Iterative ADP Algorithm -- 7.4 Implementation of the Iterative ADP Algorithm -- 7.4.1 Critic Network -- 7.4.2 The Action Network -- 7.4.3 The Procedure of the ADP Algorithm -- 7.5 Simulation Study -- 7.5.1 Example 1 -- 7.5.2 Example 2 -- 7.6 Conclusions -- References -- 8 A New Approach for a Class of Continuous-Time Chaotic Systems Optimal Control by Online ADP Algorithm -- 8.1 Introduction -- 8.2 Problem Statement -- 8.3 Optimal Control Based on Online ADP Algorithm -- 8.3.1 Design Method of the Critic Network and the Action Network -- 8.3.2 Stability Analysis -- 8.3.3 Online ADP Algorithm Implementation -- 8.4 Simulation Examples -- 8.4.1 Example 1 -- 8.4.2 Example 2 -- 8.5 Conclusions -- References -- 9 Off-Policy IRL Optimal Tracking Control for Continuous-Time Chaotic Systems -- 9.1 Introduction -- 9.2 System Description and Problem Statement -- 9.3 Off-Policy IRL ADP Algorithm -- 9.3.1 Convergence Analysis of IRL ADP Algorithm -- 9.3.2 Off-Policy IRL Method -- 9.3.3 Methods for Updating Weights -- 9.4 Simulation Study -- 9.4.1 Example 1 -- 9.4.2 Example 2 -- 9.5 Conclusion -- References -- 10 ADP-Based Optimal Sensor Scheduling for Target Tracking in Energy Harvesting Wireless Sensor Networks -- 10.1 Introduction -- 10.2 Problem Formulation -- 10.2.1 NN Model Description of Solar Energy Harvesting -- 10.2.2 Sensor Energy Consumption -- 10.2.3 KF Technology
  • 10.3 ADP-Based Sensor Scheduling for Maximum WSNs Residual Energy and Minimum Measuring Accuracy -- 10.3.1 Optimization Problem of the Sensor Scheduling -- 10.3.2 ADP-Based Sensor Scheduling with Convergence Analysis -- 10.3.3 Critic Network -- 10.3.4 Implementation Process -- 10.4 Simulation Study -- 10.5 Conclusion -- References -- Index
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1 online resource (240 pages)
Form of item
online
Isbn
9789811040801
Media category
computer
Media MARC source
rdamedia
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c
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