Feedback Control and Adaptive Learning in Optical-Tweezer Robotics
- 1st Edition - October 1, 2025
- Imprint: Academic Press
- Authors: Xiang Li, Shu Miao, Chien Chern Cheah
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 3 1 5 4 8 - 0
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 3 1 5 4 9 - 7
Feedback Control and Adaptive Learning in Optical-Tweezer Robotics is a comprehensive guide to merging robotic feedback control with optical trapping techniques in cell manipu… Read more
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Request a sales quoteFeedback Control and Adaptive Learning in Optical-Tweezer Robotics is a comprehensive guide to merging robotic feedback control with optical trapping techniques in cell manipulation. It begins by providing foundational knowledge in dynamic modeling and control theory, essential for understanding optical-tweezer robotics' complexities. The book explores optical trapping principles, discussing traditional approaches' constraints and challenges. It then introduces a unified control methodology designed for dynamic adaptation to cell movement and escape scenarios, highlighting the importance of closed-loop control strategies in navigating the interaction between optical forces and robotic manipulation.
Additionally, the book delves into adaptive learning algorithms for real-time adjustments in unknown trapping stiffness, addressing challenges like limited field of view, stochastic disturbances, and handling multiple cells. It integrates open-access simulations and real-world experiments to reinforce theoretical concepts, offering practical examples. This unified approach's potential impact on biomedicine, biotechnology, and microscale robotics is emphasized, making it an invaluable resource for readers in these fields.
Additionally, the book delves into adaptive learning algorithms for real-time adjustments in unknown trapping stiffness, addressing challenges like limited field of view, stochastic disturbances, and handling multiple cells. It integrates open-access simulations and real-world experiments to reinforce theoretical concepts, offering practical examples. This unified approach's potential impact on biomedicine, biotechnology, and microscale robotics is emphasized, making it an invaluable resource for readers in these fields.
- Integrates dynamic modeling, feedback control, and adaptive learning with optical tweezer robotics
- Offers practical insights and solutions to micro-world challenges, including unknown trapping stiffness, stochastic disturbances, limited field of view, and simultaneous manipulation of multiple cells
- Explores advanced theories and control techniques, presenting state-of-the-art methodologies
- Provides an open-access simulation environment to access and reproduce the presented results
- Presents real-world experimental data
Researchres and Professional in the fields of robotics, control systems, biotechnology, biomedical engineering, and microscale technology
1. Introduction to Optical-Tweezer Robotics
1.1. Principle of Optical Traps
1.2. Overall Structure of Optical Tweezers
1.3. Dynamic Model
2. Dynamic Trapping and Manipulation
2.1. Backstepping Approach
2.2. Singular Perturbation Approach
3. State Estimation and Parameter Adaptation
3.1. Design of Velocity Observer
3.2. Adaptation of Unknown Trapping Stiffness
3.4. Stochastic Optical Manipulation
4. Region Control and Regional Feedback Control
4.1. Simple PD Control
4.2. Limited Field of View Problem
5. Coordination of Multiple Optical Traps
5.1. Sequential Trapping of Multiple Cells
5.2. Independent Control of Multiple Traps
5.3. Case Studies
6. Emerging Techniques in Optical Manipulation
6.1. Reinforcement-Learning-Based In-Hand Tasks
6.2. Deep-Learning-Based Visual Detection and Tracking
6.3. Future Directions and Research Opportunities
1.1. Principle of Optical Traps
1.2. Overall Structure of Optical Tweezers
1.3. Dynamic Model
2. Dynamic Trapping and Manipulation
2.1. Backstepping Approach
2.2. Singular Perturbation Approach
3. State Estimation and Parameter Adaptation
3.1. Design of Velocity Observer
3.2. Adaptation of Unknown Trapping Stiffness
3.4. Stochastic Optical Manipulation
4. Region Control and Regional Feedback Control
4.1. Simple PD Control
4.2. Limited Field of View Problem
5. Coordination of Multiple Optical Traps
5.1. Sequential Trapping of Multiple Cells
5.2. Independent Control of Multiple Traps
5.3. Case Studies
6. Emerging Techniques in Optical Manipulation
6.1. Reinforcement-Learning-Based In-Hand Tasks
6.2. Deep-Learning-Based Visual Detection and Tracking
6.3. Future Directions and Research Opportunities
- Edition: 1
- Published: October 1, 2025
- Imprint: Academic Press
- Language: English
- Paperback ISBN: 9780443315480
- eBook ISBN: 9780443315497
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Xiang Li
Xiang LI is an Associate Professor with the Department of Automation, Tsinghua University. He has been the Associate Editor of IEEE Robotics and Automation Letters since 2022 and the Associate Editor of IEEE Transactions on Automation Science and Engineering since 2023. He was the Associate Editor of IEEE Robotics & Automation Magazine from 2019 to 2021 and the Associate Editor of ICRA in 2019, 2020, 2021, and 2023. He received the Highly Commended Paper Award in 2013 IFToMM, the Best Paper in Robotic Control in 2017 ICAR, the Best Application Paper Finalists in 2017 IROS, the T. J. Tarn Best Paper in Robotics in 2018 IEEE ROBIO, and the Best Paper Award in 2023 ICRA DOM Workshop. He is the Program Chair of the 2023 IEEE International Conference on Real-time Computing and Robotics. He is a Senior Member of IEEE.
Affiliations and expertise
Tsinghua University, ChinaSM
Shu Miao
Shu Miao is a Postdoctoral Fellow with the Department of Automation, Tsinghua University. His research interests include robotic cell manipulation and medical robotics. He also serves as a reviewer for several top journals in the field of robotics.
Affiliations and expertise
Tsinghua University, ChinaCC
Chien Chern Cheah
Chien Chern Cheah was born in Singapore. He received the B.Eng. degree in electrical engineering from the National University of Singapore, in 1990, and the M.Eng. and Ph.D. degrees in electrical engineering from Nanyang Technological University, Singapore, in 1993 and 1996, respectively. From 1990 to 1991, he was a Design Engineer with Chartered Electronics Industries, Singapore. He was a Research Fellow with the Department of Robotics, Ritsumeikan University, Japan, from 1996 to 1998. He is currently an Associate Professor with Nanyang Technological University. He served as an Associate Editor for IEEE Transactions on Robotics, from 2010 to 2013, the Program Co-Chair for the IEEE International Conference on Robotics and Automation, in 2017, the Award Chair for the IEEE/RSJ International Conference on Intelligent Robots and Systems, in 2019, and the Lead Guest Editor for IEEE Transactions on Mechatronics, in 2021. He serves as an Associate Editor for Automatica.
Affiliations and expertise
Nanyang Technological University, Singapore