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He is also working as a Robotic Researcher with Robotmaster. His research interests include robotics and control, computer vision, nonlinear systems, visual servoing, system identification, and artificial intelligence.Wen-Fang Xie (SM'12) received the Master's degree in flight control from Beihang University, Beijing, China, and the Ph.D. degree in intelligent process control from Hong Kong Polytechnic University, Hung Hom, Hong Kong, in 1991 and 1999, respectively.

In terms of exact solutions of the time-dependent Schrodinger equation for an effective giant spin modeled from a coupled two-mode Bose-Einstein condensate (BEC) with adiabatic and cyclic time-varying Raman coupling between two hyperfine states of the BEC, we obtain analytic time-evolution formulas of the population imbalance and relative phase between two components with various initial states, especially the SU(2)coherent state. We find the Berry phase depending on the number parity of atoms, and particle number dependence of the collapse revival of population-imbalance oscillation. It is shown that self-trapping and phase locking can be achieved from initial SU(2) coherent states with proper parameters.Comment: 18 pages,5 figure

Smart actuators such as magnetorestrictive actuators, shape memory alloy (SMA) actuators, and piezoceramic actuators exhibit different hysteresis loops. In this paper, a generalized Prandtl-Ishlinskii model is utilized for modeling and compensation of hysteresis nonlinearities in smart actuators. In the formulated model, a generalized play operator together with a density is integrated to form the generalized Prandtl-Ishlinskii model. The capability of the formulated model to characterize hysteresis in smart actuators is demonstrated by comparing its outputs with experimental results obtained from different smart actuators. As an example, hysteresis nonlinearities of the magnetostrictive and SMA actuators are characterized by the generalized Prandtl-Ishlinskii model. Furthermore, an analytical inverse of the generalized Prandtl-Ishlinskii model is derived for compensations in different smart actuators. In other words, exact inverse of the generalized Prandtl-Ishlinskii model is achievable and it can be implemented as a feedforward compensator to migrate the effects of the hysteresis in different types of smart actuators. Such compensation is experimentally illustrated by piezoceramic actuator.

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This paper presents a novel image-based visual servoing (IBVS) controller based on quasi-min-max model predictive control (MPC). By transforming the image Jacobian matrix (i.e. interaction matrix) into a convex combination of linear time-invariant vertices form with the tensor-product (TP) model transformation method, the visual servoing system is represented as a polytopic linear parameter-varying (LPV) system. A robust controller is designed for the robotic visual servoing system subject to input and output constraints such as robot physical limitations and visibility constraints. The control signal is calculated online by carrying out the convex optimization involving linear matrix inequalities (LMIs) in model predictive control. The proposed visual servoing method avoids the inverse of the image Jacobian matrix and hence can solve the intractable problems for the classical IBVS controller, such as large displacements between the initial and the desired position of the camera. The ability of handling constraints can keep the image features in the boundary of the desired field of view (FOV). To verify the effectiveness of the proposed algorithm, the simulation results on a 6 degrees-of-freedom (DOF) robot manipulator with eye-in-hand configuration are presented and discussed. 402 416 Fig. 13. Simulation results by Quasi-min-max MPC-based IBVS with noise (Z = 0.06).