Adaptive Neural Dynamic Surface Control With Prespecified Tracking Accuracy of Uncertain Stochastic Nonstrict-Feedback Systems.

This article addresses the adaptive neural tracking control problem for a class of uncertain stochastic nonlinear systems with nonstrict-feedback form and prespecified tracking accuracy. Some radial basis function neural networks (RBF NNs) are used to approximate the unknown continuous functions online, and the desired controller is designed via the adaptive dynamic surface control (DSC) method and the gain suppressing inequality technique. Different from the reported works on uncertain stochastic systems, by combining some non-negative switching functions and dynamic surface method with the nonlinear filter, the design difficulty is overcome, and the control performance is analyzed by employing stochastic Barbalat's lemma. Under the constructed controller, the tracking error converges to the accuracy defined a priori in probability. The simulation results are shown to verify the availability of the presented control scheme.

Constructing Three-Dimensional Honeycombed Graphene/Silicon Skeletons for High-Performance Li-Ion Batteries.

Silicon has been considered to be an attractive high-capacity anode material for next-generation lithium-ion batteries (LIBs). Currently, the commercial application of Si-based anodes is still restricted by its limited cycle life and rate capacity, which could be ascribed to the colossal volumetric change during the cycling process and poor electronic conductivity. We report the design of a unique Si-based nanocomposite of three-dimensional (3D) honeycombed graphene aerogel and the reduced graphene oxide sheets preprotected silicon secondary particles (SiNPs@rGO1). Through simple electrostatic self-assembly and hydrothermal processes, SiNPs are able to be wrapped with rGO1 to form reunited SiNPs@rGO1, and embedded into the backbone of 3D graphene honeycomb (rGO2). Such an intriguing design (namely, SiNPs@rGO1/rGO2) not only provides a conductive skeleton to improve the electrical conductivity, but also possesses abundant void spaces to accommodate the dramatic volume changes of SiNPs. Meanwhile, the outer rGO1 coats protect the inner SiNPs away from the electrolyte and prevent the destruction of the solid electrolyte interphase (SEI) film. As a result, the 3D honeycombed architecture achieves a high cyclability and excellent rate capability.