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PETREL, a winged hybrid-driven underwater glider is a novel and practical marine survey platform which combines the features of legacy underwater glider and conventional AUV (autonomous underwater vehicle). It can be treated as a multi-rigid-body system with a floating base and a particular hydrodynamic profile. In this paper, theorems on linear and angular momentum are used to establish the dynamic equations of motion of each rigid body and the effect of translational and rotational motion of internal masses on the attitude control are taken into consideration. In addition, due to the unique external shape with fixed wings and deflectable rudders and the dual-drive operation in thrust and glide modes, the approaches of building dynamic model of conventional AUV and hydrodynamic model of submarine are introduced, and the tailored dynamic equations of the hybrid glider are formulated. Moreover, the behaviors of motion in glide and thrust operation are analyzed based on the simulation and the feasibility of the dynamic model is validated by data from lake field trials.

This article presents an intelligent algorithm based on extreme learning machine and sequential mutation genetic algorithm to determine the inverse kinematics solutions of a robotic manipulator with six degrees of freedom. This algorithm is developed to minimize the computational time without compromising the accuracy of the end effector. In the proposed algorithm, the preliminary inverse kinematics solution is first computed by extreme learning machine and the solution is then optimized by an improved genetic algorithm based on sequential mutation. Extreme learning machine randomly initializes the weights of the input layer and biases of the hidden layer, which greatly improves the training speed. Unlike classical genetic algorithms, sequential mutation genetic algorithm changes the order of the genetic codes from high to low, which reduces the randomness of mutation operation and improves the local search capability. Consequently, the convergence speed at the end of evolution is improved. The performance of the extreme learning machine and sequential mutation genetic algorithm is also compared with that of a hybrid intelligent algorithm, and the results showed that there is significant reduction in the training time and computational time while the solution accuracy is retained. Based on the experimental results, the proposed extreme learning machine and sequential mutation genetic algorithm can greatly improve the time efficiency while ensuring high accuracy of the end effector.

A lamellar membrane assembled by parallel restacking of two-dimensional nanosheets provides a novel platform for studying the electrostatic manipulation of ion mobility under angstrom-scale confinement. The membrane nanostructure in aqueous solution is unstable due to intercalation. However, because accurate measurements of structural changes under different charged conditions are difficult, their effect on the ion modulation was seldom fully addressed, and the electrostatic modulation mechanism was not good. Here, we report the efficient manipulation of ion diffusion in the confined channels through a Ti 3 C 2 T x membrane and gain insight into the mechanism by an in situ measurement of the lamellar nanostructure using an electrochemical quartz crystal microbalance with dissipation (EQCM-D). By precisely tuning the external potential directly applied to the membrane, the ion permeation rate can be effectively and reversibly increased by 6 times or decreased by 15 times. The EQCM-D measurement provides empirical evidence to demonstrate that the anomalous ionflow modulation is determined by both the channel diameter and an ion rearrangement in the electrical double layers (EDLs), particularly, in the Stern layers. Although the channel expanded at low negative voltage, the ion permeation rate decelerated due to the counterion distribution in the Stern layers.