Description 
1 online resource (xvi, 230 pages) 
Series 
Studies in systems, decision and control ; v. 103 

Studies in systems, decision and control ; v. 103.

Contents 
Chapter 1. Principle of Adaptive Dynamic Programming  Chapter 2. An Iterative ?Optimal Control Scheme for a Class of DiscreteTime Nonlinear Systems With Unfixed Initial State  Chapter 3. DiscreteTime Optimal Control of Nonlinear Systems Via Value IterationBased QLearning  Chapter 4. A Novel Policy Iteration Based Deterministic QLearning for DiscreteTime Nonlinear Systems  Chapter 5. Nonlinear NeuroOptimal Tracking Control Via Stable Iterative QLearning Algorithm  Chapter 6. ModelFree Multiobjective Adaptive Dynamic Programming for DiscreteTime Nonlinear Systems with General Performance Index Functions  Chapter 7. MultiObjective Optimal Control for a Class of Unknown Nonlinear Systems Based on FiniteApproximationError ADP Algorithm  Chapter 8. A New Approach for a Class of ContinuousTime Chaotic Systems Optimal Control by Online ADP Algorithm  Chapter 9. OffPolicy IRL Optimal Tracking Control for ContinuousTime Chaotic Systems  Chapter 10. ADPBased Optimal Sensor Scheduling for Target Tracking in Energy Harvesting Wireless Sensor Networks 

Chapter 1. Principle of Adaptive Dynamic Programming  Chapter 2. An Iterative ϵOptimal Control Scheme for a Class of DiscreteTime Nonlinear Systems With Unfixed Initial State  Chapter 3. DiscreteTime Optimal Control of Nonlinear Systems Via Value IterationBased QLearning  Chapter 4. A Novel Policy Iteration Based Deterministic QLearning for DiscreteTime Nonlinear Systems  Chapter 5. Nonlinear NeuroOptimal Tracking Control Via Stable Iterative QLearning Algorithm  Chapter 6. ModelFree Multiobjective Adaptive Dynamic Programming for DiscreteTime Nonlinear Systems with General Performance Index Functions  Chapter 7. MultiObjective Optimal Control for a Class of Unknown Nonlinear Systems Based on FiniteApproximationError ADP Algorithm  Chapter 8. A New Approach for a Class of ContinuousTime Chaotic Systems Optimal Control by Online ADP Algorithm  Chapter 9. OffPolicy IRL Optimal Tracking Control for ContinuousTime Chaotic Systems  Chapter 10. ADPBased Optimal Sensor Scheduling for Target Tracking in Energy Harvesting Wireless Sensor Networks 
Summary 
This book presents a class of novel, selflearning, 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, selflearning 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 realworld examples to complement and substantiate the mathematical analysis, the book is a valuable guide for engineers, researchers, and students in control science and engineering 
Bibliography 
Includes bibliographical references and index 
Notes 
Vendorsupplied metadata 
Subject 
Dynamic programming.


Nonlinear systems.


Artificial intelligence.


Dynamics & vibration.


Automatic control engineering.


MATHEMATICS  Applied.


MATHEMATICS  Probability & Statistics  General.


Dynamic programming.


Nonlinear systems.

Form 
Electronic book

Author 
Song, Ruizhuo, author


Li, Benkai, author


Lin, Xiaofeng, author.

ISBN 
9789811040801 

981104080X 
