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Main issues in visual servoing of manipulators mainly include rapid convergence of feature errors to zero and the safety of joints regarding joint physical limits. To address the two issues, in this paper, an image-based visual servoing scheme is proposed for manipulators with an eye-in-hand configuration. Compared with existing schemes, the proposed one does not require performing pseudoinversion for the image Jacobian matrix or inversion for the Jacobian matrix associated with the forward kinematics of the manipulators. Theoretical analysis shows that the proposed scheme not only guarantees the asymptotic convergence of feature errors to zero but also the compliance with joint angle and velocity limits of the manipulators. Besides, simulation results based on a PUMA560 manipulator with a camera mounted on the end effector verify the theoretical conclusions and the efficacy of the proposed scheme.

This paper presents a new monitoring method for multimode processes based on subspace decomposition. In the proposed method, the influence of quality variables and multimode information are considered in multimode processes modeling, which is crucially important to ensure industrial production safety and quality stabilization. Process data are decomposed into the global common subspace and the local specific subspace and monitoring is performed in each subspace to simplify the model structure. Two experiments: penicillin fermentation processes and practical foods industrial production processes, have been used to demonstrate the excellent performance of the proposed method.