Learning Control
Applications in Robotics and Complex Dynamical Systems
- 1st Edition - December 5, 2020
- Imprint: Elsevier
- Editors: Dan Zhang, Bin Wei
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 2 2 3 1 4 - 7
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 2 2 3 1 5 - 4
Learning Control: Applications in Robotics and Complex Dynamical Systems provides a foundational understanding of control theory while also introducing exciting cutting-edge techno… Read more
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Request a sales quoteLearning 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.
- Provides foundational control theory concepts, along with advanced techniques and the latest advances in adaptive control and robotics
- Introduces state-of-the-art learning-based control technologies and their applications in robotics and other complex dynamical systems
- Demonstrates computational techniques for control systems
- Covers iterative learning impedance control in both human-robot interaction and collaborative robots
Researchers and academics in robotics, mechanical engineering, and mechatronics; engineers working in the same fields; grad students
- Cover image
- Title page
- Table of Contents
- Copyright
- List of contributors
- Chapter 1: A high-level design process for neural-network controls through a framework of human personalities
- Abstract
- 1.1. Introduction
- 1.2. Background
- 1.3. Proposed methods
- 1.4. Results
- 1.5. Conclusions
- Appendix 1.A.
- References
- Chapter 2: Cognitive load estimation for adaptive human–machine system automation
- Abstract
- 2.1. Introduction
- 2.2. Noninvasive metrics of cognitive load
- 2.3. Details of open-loop experiments
- 2.4. Conclusions and discussions
- 2.5. List of abbreviations
- References
- Chapter 3: Comprehensive error analysis beyond system innovations in Kalman filtering
- Abstract
- 3.1. Introduction
- 3.2. Standard formulation of Kalman filter after minimum variance principle
- 3.3. Alternate formulations of Kalman filter after least squares principle
- 3.4. Redundancy contribution in Kalman filtering
- 3.5. Variance of unit weight and variance component estimation
- 3.6. Test statistics
- 3.7. Real data analysis with multi-sensor integrated kinematic positioning and navigation
- 3.8. Remarks
- References
- Chapter 4: Nonlinear control
- Abstract
- 4.1. System modeling
- 4.2. Nonlinear control
- 4.3. Summary
- References
- Chapter 5: Deep learning approaches in face analysis
- Abstract
- 5.1. Introduction
- 5.2. Face detection
- 5.3. Pre-processing steps
- 5.4. Facial attribute estimation
- 5.5. Facial expression recognition
- 5.6. Face recognition
- 5.7. Discussion and conclusion
- References
- Chapter 6: Finite multi-dimensional generalized Gamma Mixture Model Learning for feature selection
- Abstract
- 6.1. Introduction
- 6.2. The proposed model
- 6.3. Parameter estimation
- 6.4. Model selection using the minimum message length criterion
- 6.5. Experimental results
- 6.6. Conclusion
- References
- Chapter 7: Variational learning of finite shifted scaled Dirichlet mixture models
- Abstract
- 7.1. Introduction
- 7.2. Model specification
- 7.3. Variational Bayesian learning
- 7.4. Experimental result
- 7.5. Conclusion
- Appendix 7.A.
- Appendix 7.B.
- References
- Chapter 8: From traditional to deep learning: Fault diagnosis for autonomous vehicles
- Abstract
- 8.1. Introduction
- 8.2. Traditional fault diagnosis
- 8.3. Deep learning for fault diagnosis
- 8.4. An example using deep learning for fault detection
- 8.5. Conclusion
- References
- Chapter 9: Controlling satellites with reaction wheels
- Abstract
- 9.1. Introduction
- 9.2. Spacecraft attitude mathematical model
- 9.3. Attitude tracking
- 9.4. Actuator dynamics
- 9.5. Attitude control law
- 9.6. Performance analysis
- 9.7. Conclusions
- References
- Chapter 10: Vision dynamics-based learning control
- Abstract
- 10.1. Introduction
- 10.2. Problem definition
- 10.3. Experiments
- 10.4. Conclusions
- References
- Index
- Edition: 1
- Published: December 5, 2020
- Imprint: Elsevier
- No. of pages: 280
- Language: English
- Paperback ISBN: 9780128223147
- eBook ISBN: 9780128223154
DZ
Dan Zhang
Dan Zhang is a Kaneff Professor and Tier 1 York Research Chair in Advanced Robotics and Mechatronics in the Department of Mechanical Engineering of the Lassonde School of Engineering at York University, Toronoto, Canada. Previously he was Professor and Canada Research Chair in Advanced Robotics and Automation, and he was a founding Chair of the Department of Automotive, Mechanical, and Manufacturing Engineering with the Faculty of Engineering and Applied Science at University of Ontario Institute of Technology. He is editor-in-chief for International Journal of Robotics Applications and Technologies, Associate editor for the International Journal of Robotics and Automation (ACTA publisher), and guest editor on four other international journals. He is the editor of 6 books related to mechatronics and robotics.
Affiliations and expertise
Kaneff Professor and Tier 1 York Research Chair in Advanced Robotics and Mechatronics, Department of Mechanical Engineering of the Lassonde School of Engineering, York University, Toronto, CanadaBW
Bin Wei
Bin Wei is an Assistant Professor at Algoma University, Ontario, Canada. He received his Ph.D. in robotics from University of Ontario Institute of Technology, Canada, in 2016. He conducts research in the areas of robotics, control theory, and computational mechanics. He has co-edited 5 books on robotic mechanics.
Affiliations and expertise
Assistant Professor, Algoma University, Ontario, CanadaRead Learning Control on ScienceDirect