Feedback Control and Adaptive Learning in Optical-Tweezer Robotics

Feedback 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 rei