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.
- 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 the state-of-the-art methodologies
- Provides open-access simulation environment to access and reproduce the presented results
- Presents real-world experimental data
