Shu Miao – författare

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 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 roboticsOffers practical insights and solutions to micro-world challenges, including unknown trapping stiffness, stochastic disturbances, limited field of view, and simultaneous manipulation of multiple cellsExplores advanced theories and control techniques, presenting state-of-the-art methodologiesProvides an open-access simulation environment to access and reproduce the presented resultsPresents real-world experimental data