University of Science and Technology Beijing

About: University of Science and Technology Beijing is a education organization based out in Beijing, China. It is known for research contribution in the topics: Microstructure & Alloy. The organization has 41558 authors who have published 44473 publications receiving 623229 citations. The organization is also known as: Beijing Steel and Iron Institute.

Fixed-Time Leader–Follower Consensus of Networked Nonlinear Systems via Event/Self-Triggered Control

Abstract: This brief addresses the fixed-time event/self-triggered leader–follower consensus problems for networked multi-agent systems subject to nonlinear dynamics. First, we present an event-triggered control strategy to achieve the fixed-time consensus, and a new measurement error is designed to avoid Zeno behavior. Then, two new self-triggered control strategies are presented to avoid continuous triggering condition monitoring. Moreover, under the proposed self-triggered control strategies, a strictly positive minimal triggering interval of each follower is given to exclude Zeno behavior. Compared with the existing fixed-time event-triggered results, we propose two new self-triggered control strategies, and the nonlinear term is more general. Finally, the performances of the consensus tracking algorithms are illustrated by a simulation example.

Self-Powered Magnetic Sensor Based on a Triboelectric Nanogenerator

Abstract: Magnetic sensors are usually based on the Hall effect or a magnetoresistive sensing mechanism. Here we demonstrate that a nanogenerator can serve as a sensor for detecting the variation of the time-dependent magnetic field. The output voltage of the sensor was found to exponentially increase with increasing magnetic field. The detection sensitivities for the change and the changing rate of magnetic field are about 0.0363 ± 0.0004 ln(mV)/G and 0.0497 ± 0.0006 ln(mV)/(G/s), respectively. The response time and reset time of the sensor are about 0.13 and 0.34 s, respectively. The fabricated sensor has a detection resolution of about 3 G and can work under low frequencies (<0.4 Hz).

Building robust architectures of carbon and metal oxide nanocrystals toward high-performance anodes for lithium-ion batteries.

Abstract: Design and fabrication of effective electrode structure is essential but is still a challenge for current lithium-ion battery technology. Herein we report the design and fabrication of a class of high-performance robust nanocomposites based on iron oxide spheres and carbon nanotubes (CNTs). An efficient aerosol spray process combined with vacuum filtration was used to synthesize such composite architecture, where oxide nanocrystals were assembled into a continuous carbon skeleton and entangled in porous CNT networks. This material architecture offers many critical features that are required for high-performance anodes, including efficient ion transport, high conductivity, and structure durability, therefore enabling an electrode with outstanding lithium storage performance. For example, such an electrode with a thickness of ∼35 μm could deliver a specific capacity of 994 mA h g(-1) (based on total electrode weight) and high recharging rates. This effective strategy can be extended to construct many other composite electrodes for high-performance lithium-ion batteries.

Neural Network Control of a Two-Link Flexible Robotic Manipulator Using Assumed Mode Method

Abstract: In this paper, the n-dimensional discretized model of the two-link flexible manipulator is developed by the assumed mode method (AMM). Subsequently, based on the discretized dynamic model, both full-state feedback control and output feedback control are investigated to achieve the trajectory tracking and vibration suppression. In order to guarantee the stability strictly, uniform ultimate boundedness (UUB) of the closed-loop system is realized by the Lyapunov's stability. Furthermore, through appropriately choosing control parameters, the states of the system will converge to zero within a small neighborhood. Eventually, extensive simulations and experiments on the Quanser platform for a two-link robotic manipulator are carried out to demonstrate the feasibility of the proposed neural network controller.