Self-learning optimal control of nonlinear systems : adaptive dynamic programming approach 1st ed.

Author

• Wei, Qinglai

Additional Author(s)

• Song, Ruizhuo
• Li, Benkai
• Lin, Xiaofeng

Publisher

Beach Road: Springer Nature Singapore Pte Ltd., 2018

Language

English

ISBN

9789811040801

Series

Studies in Systems, Decision and Control 103

Subject(s)

• COMPUTATIONAL INTELLIGENCE
• DYNAMICS
• VIBRATION

Notes

. .

Abstract

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.

Physical Dimension

Number of Page(s)

1 online resource (xviii, 230 p.)

Dimension

-

Other Desc.

ill. (in col.)

Summary / Review / 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. Off-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. Chapter 1. Principle of Adaptive Dynamic Programming --
Chapter 2. An Iterative {u03F5}-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. Off-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.

Exemplar(s)

#	Accession No.	Call Number	Location	Status
1.	00514/20	519.703 Wei S	Online !	Available