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A nonlinear robust control method for the trajectory tracking of the underwater vehicle and manipulator system that operates in the presence of external current disturbances is proposed using double closed-loop integral sliding mode control. The designed controller uses a double closed-loop control structure to track the desired trajectory in the joint space of the underwater vehicle and manipulator system, and its inner and outer loop systems use integral sliding surface to enhance the robustness of the whole system. Then, the continuous switching mode based on hyperbolic tangent function is used instead of the traditional discontinuous switching mode to reduce the chattering of the control input of the underwater vehicle and manipulator system. In addition, the control method proposed in this article does not need to estimate the uncertainties of the underwater vehicle and manipulator system control system through online identification, but also can ensure the robustness of the underwater vehicle and manipulator system motion control in underwater environment. Therefore, it is easier to be implemented on the embedded platform of the underwater vehicle and manipulator system and applied to the actual marine operation tasks. At last, the stability of the control system is proved by the Lyapunov theory, and its effectiveness and feasibility are verified by the simulation experiments in MATLAB software.

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A signal peptide is a short peptide chain that directs the transport of a protein and has become the crucial vehicle in finding new drugs or reprogramming cells for gene therapy. As the avalanche of new protein sequences generated in the postgenomic era, the challenge of identifying new signal sequences has become even more urgent and critical in biomedical engineering. In this paper, we propose a novel predictor called Signal-BNF to predict the N-terminal signal peptide as well as its cleavage site based on Bayesian reasoning network. Signal-BNF is formed by fusing the results of different Bayesian classifiers which used different feature datasets as its input through weighted voting system. Experiment results show that Signal-BNF is superior to the popular online predictors such as Signal-3L and PrediSi. Signal-BNF is featured by high prediction accuracy that may serve as a useful tool for further investigating many unclear details regarding the molecular mechanism of the zip code protein-sorting system in cells.

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