Learning Control: Applications in Robotics and Complex Dynamical Systems provides a foundational understanding of control theory while also introducing exciting cutting-edge technologies in the field of learning-based control. State-of-the-art techniques involving machine learning and artificial intelligence (AI) are covered, as are foundational control theories and more established techniques such as adaptive learning control, reinforcement learning control, impedance control, and deep reinforcement control. Each chapter includes case studies and real-world applications in robotics, AI, aircraft and other vehicles and complex dynamical systems. Computational methods for control systems, particularly those used for developing AI and other machine learning techniques, are also discussed at length.
1. Learning Control - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page i 4. Table of Contents - p. Page iii 5. Copyright - p. Page iii 6. Contents - p. Page v 7. List of contributors - p. Page v 8. List of Illustrations - p. Page viii 9. List of Tables - p. Page ix 10. Chapter 1 : A high-level design process for neural-network controls through a framework of human personalities - p. Page ix 11. Chapter 2 : Cognitive load estimation for adaptive human–machine system automation - p. Page 33 12. Chapter 3 : Comprehensive error analysis beyond system innovations in Kalman filtering - p. Page 55 13. Chapter 4 : Nonlinear control - p. Page 89 14. Chapter 5 : Deep learning approaches in face analysis - p. Page 102 15. Chapter 6 : Finite multi-dimensional generalized Gamma Mixture Model Learning for feature selection - p. Page 132 16. Chapter 7 : Variational learning of finite shifted scaled Dirichlet mixture models - p. Page 170 17. Chapter 8 : From traditional to deep learning: Fault diagnosis for autonomous vehicles - p. Page 202 18. Chapter 9 : Controlling satellites with reaction wheels - p. Page 218 19. Chapter 10 : Vision dynamics-based learning control - p. Page 242 20. Index - p. Page 256
Learning Control: Applications in Robotics and Complex Dynamical Systems provides a foundational understanding of control theory while also introducing exciting cutting-edge technologies in the field of learning-based control. State-of-the-art techniques involving machine learning and artificial intelligence (AI) are covered, as are foundational control theories and more established techniques such as adaptive learning control, reinforcement learning control, impedance control, and deep reinforcement control. Each chapter includes case studies and real-world applications in robotics, AI, aircraft and other vehicles and complex dynamical systems. Computational methods for control systems, particularly those used for developing AI and other machine learning techniques, are also discussed at length.
1. Learning Control - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page i 4. Table of Contents - p. Page iii 5. Copyright - p. Page iii 6. Contents - p. Page v 7. List of contributors - p. Page v 8. List of Illustrations - p. Page viii 9. List of Tables - p. Page ix 10. Chapter 1 : A high-level design process for neural-network controls through a framework of human personalities - p. Page ix 11. Chapter 2 : Cognitive load estimation for adaptive human–machine system automation - p. Page 33 12. Chapter 3 : Comprehensive error analysis beyond system innovations in Kalman filtering - p. Page 55 13. Chapter 4 : Nonlinear control - p. Page 89 14. Chapter 5 : Deep learning approaches in face analysis - p. Page 102 15. Chapter 6 : Finite multi-dimensional generalized Gamma Mixture Model Learning for feature selection - p. Page 132 16. Chapter 7 : Variational learning of finite shifted scaled Dirichlet mixture models - p. Page 170 17. Chapter 8 : From traditional to deep learning: Fault diagnosis for autonomous vehicles - p. Page 202 18. Chapter 9 : Controlling satellites with reaction wheels - p. Page 218 19. Chapter 10 : Vision dynamics-based learning control - p. Page 242 20. Index - p. Page 256
