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Feedback Control and Adaptive Learning in Optical-Tweezer Robotics
Feedback Control and Adaptive Learning in Optical-Tweezer Robotics is structured to provide a comprehensive understanding of the integration of robotic feedback control with optical trapping techniques in the field of cell manipulation. It begins by establishing foundational knowledge in dynamic modeling and control theory, laying the groundwork for readers to grasp the intricacies of optical-tweezer robotics. The text then delves into the specifics of optical trapping principles, elucidating the constraints and challenges associated with traditional approaches. It includes the design and implementation of a unified control methodology capable of dynamically adapting to cell movement and escape scenarios. The book emphasizes closed-loop control strategies, enabling readers to navigate the complex interplay between optical forces and robotic manipulation. Additionally, adaptive learning algorithms are explored, offering readers insights into real-time adjustments where the trapping stiffness is unknown. It further addresses open challenges, including overcoming limited field of view, rejecting stochastic disturbances, and efficiently handling the simultaneous trapping and manipulation of multiple cells. Throughout the book, open-access simulations and real-world experiments are integrated to reinforce theoretical concepts. This provides readers with tangible examples and a deeper appreciation for the potential impact of this unified approach in areas such as biomedicine, biotechnology, and microscale robotics.
Book Details
- Publisher: Academic Press
- Publish Date: Oct 1st, 2025
- Pages: 300
- Language: English
- Edition: undefined - undefined
- Dimensions: 0.00in - 0.00in - 0.00in - 0.00lb
- EAN: 9780443315480
- Categories: • Biotechnology• Biomedical
About the Author
Li, Xiang: - 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.
Miao, Shu: -
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.
Cheah, Chien Chern: - 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.