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

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 ite...

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Detaylı Bibliyografya
Asıl Yazarlar: Wei, Qinglai (Yazar), Song, Ruizhuo (Yazar), Li, Benkai (Yazar), Lin, Xiaofeng (Yazar)
Müşterek Yazar: SpringerLink (Online service)
Materyal Türü: e-Kitap
Dil:İngilizce
Baskı/Yayın Bilgisi: Singapore : Springer Nature Singapore : 2018.
Imprint: Springer,
Edisyon:1st ed. 2018.
Seri Bilgileri:Studies in Systems, Decision and Control, 103
Konular:
Control engineering.
Computational intelligence.
Multibody systems.
Vibration.
Mechanics, Applied.
Control and Systems Theory.
Computational Intelligence.
Multibody Systems and Mechanical Vibrations.
Online Erişim:Full-text access
İçindekiler:
  • Chapter 1. Principle of Adaptive Dynamic Programming
  • Chapter 2. An Iterative ϵ-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.

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