Chien Chern Cheah – 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

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 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

This book presents recent advances in robot control theory on task space sensory feedback control of robot manipulators. By using sensory feedback information, the robot control systems are robust to various uncertainties in modelling and calibration errors of the sensors. Several sensory task space control methods that do not require exact knowledge of either kinematics or dynamics of robots, are presented. Some useful methods such as approximate Jacobian control, adaptive Jacobian control, region control and multiple task space regional feedback are included. These formulations and methods give robots a high degree of flexibility in dealing with unforeseen changes and uncertainties in its kinematics and dynamics, which is similar to human reaching movements and tool manipulation. It also leads to the solution of several long-standing problems and open issues in robot control, such as force control with constraint uncertainty, control of multi-fingered robot hand with uncertain contact points, singularity issue of Jacobian matrix, global task-space control, which are also presented in this book. The target audience for this book includes scientists, engineers and practitioners involved in the field of robot control theory.

This book presents recent advances in robot control theory on task space sensory feedback control of robot manipulators. By using sensory feedback information, the robot control systems are robust to various uncertainties in modelling and calibration errors of the sensors. Several sensory task space control methods that do not require exact knowledge of either kinematics or dynamics of robots, are presented. Some useful methods such as approximate Jacobian control, adaptive Jacobian control, region control and multiple task space regional feedback are included. These formulations and methods give robots a high degree of flexibility in dealing with unforeseen changes and uncertainties in its kinematics and dynamics, which is similar to human reaching movements and tool manipulation. It also leads to the solution of several long-standing problems and open issues in robot control, such as force control with constraint uncertainty, control of multi-fingered robot hand with uncertain contact points, singularity issue of Jacobian matrix, global task-space control, which are also presented in this book. The target audience for this book includes scientists, engineers and practitioners involved in the field of robot control theory.