Adaptive compensation of persistent actuator failures of nonlinear systems

In this article, adaptive compensation designs are developed for nonlinear systems with uncertainties from the system functions and persistent actuator failures of characterizations that (i) some unknown system inputs are stuck at some unknown fixed or varying values at unknown time instants and (ii) the failure pattern always switches from one to another and the switching does not stop. Such a controlled plant is described by an uncertain time-varying nonlinear system, and some robust adaptive feedback linearization based failure compensation results are studied for closed-loop system stabilization and bounded output tracking for some specific conditions. To improve the tracking performance in the presence of persistent actuator failures, a new adaptive control scheme is developed, using the failure indicator function which contains the failure pattern and failure time in the formulation. Detailed stability and tracking performance are shown. Simulation results are shown to verify the effectiveness of the proposed adaptive actuator failure compensation method.     

Adaptive finite-time tracking control for parameterized nonlinear systems with full state constraints

In this article, the issue of adaptive finite-time dynamic surface control (DSC) is discussed for a class of parameterized nonlinear systems with full state constraints. Using the property of logarithmic function, a one-to-one nonlinear mapping is constructed to transform a constrained system into an unconstrained system with the same structure. The nonlinear filter is constructed to replace the first-order linear filter in the traditional DSC, and the demand on the filter time constant is reduced. Based on finite-time stable theory and using modified DSC, the finite-time controller is designed via DSC. Theoretical analysis shows that all the signals in the closed-loop system are semiglobal practical finite-time stable. Furthermore, none of the states are outside the defined open set. In the end, simulation results are presented to demonstrate the effectiveness of the proposed control schemes with both linear filters and nonlinear filters.     

A machine learning‐based operational control framework for reducing energy consumption of an amine‐based gas sweetening process

This study proposes a data‐driven operational control framework using machine learning‐based predictive modeling with the aim of decreasing the energy consumption of a natural gas sweetening process. This multi‐stage framework is composed of the following steps: (a) a clustering algorithm based on Density‐Based Spatial Clustering of Applications with Noise methodology is implemented to characterize the sampling space of all possible states of the operation and to determine the operational modes of the gas sweetening unit, (b) the lowest steam consumption of each operational mode is selected as a reference for operational control of the gas sweetening process, and (c) a number of high‐accuracy regression models are developed using the Gradient Boosting Machines algorithm for predicting the controlled parameters and output variables. This framework presents an operational control strategy that provides actionable insights about the energy performance of the current operations of the unit and also suggests the potential of energy saving for gas treating plant operators. The ultimate goal is to leverage this data‐driven strategy in order to identify the achievable energy conservation opportunity in such plants. The dataset for this research study consists of 29 817 records that were sampled over the course of 3 years from a gas train in the South Pars Gas Complex. Furthermore, our offline analysis demonstrates that there is a potential of 8% energy saving, equivalent to 5 760 000 Nm³ of natural gas consumption reduction, which can be achieved by mapping the steam consumption states of the unit to the best energy performances predicted by the proposed framework.     

典型废玻璃回收厂区空气颗粒物及噪声分布特征研究

以某典型废玻璃回收厂区作为研究对象, 监测和分析了车间及厂区内部的噪声强度、空气颗粒物(PM_2.5、PM₁₀) 浓度等环境指标, 点位布设涵盖了车间入口、人工分拣、物料筛分、破碎、干法清洗等关键工艺环节; 其次, 解析了空气颗粒物的组分及形貌特征, 并对其在厂区及车间内部的时空分布特征进行了研究; 此外, 利用噪声控制模型模拟并分析了隔声罩对噪声的控制作用。结果表明, 生产车间中工作态的空气颗粒物浓度显著高于非工作态, 其中干法清洗区浓度最高, 其PM_2.5 浓度为3.725 mg/m³, PM₁₀浓度为4.055 mg/m³; 噪声监测结果显示生产车间内噪声强度较高, 达到99.5 dB, 而引入隔声罩后噪声强度可降至67 dB, 结果表明, 隔声罩可有效控制频率为125~1 000 Hz 的噪声。该研究可为废玻璃的绿色、高效回收处置提供理论基础和实践经验。     

斑衣蜡蝉在葡萄上的危害及防治措施

斑衣蜡蝉是一种危害多种林木和果树的重要刺吸性害虫,主要分布于中国、日本、越南、韩国、印度和美国等地。该虫在经济林木中最喜危害葡萄,目前在宁夏地区随着转换寄主,现已扩散到了部分葡萄园,成为贺兰山东麓酿酒葡萄的重要虫害之一。为全面了解其研究进展,本文概述了国内外关于斑衣蜡蝉的寄主植物、形态特征、危害状况、发生规律、生活习性、诱发因素及防治措施等研究现状,以期为防治该虫,寻求进一步研究方向及探讨新的防治途径提供帮助。     

非全日制研究生线上线下混合式教学模式研究 ——以《现代电气控制技术》为例

随着教育信息化技术的发展，线上线下混合式教学模式已成为一种趋势。为了解决非全日制研究生课堂教学召集困难、效果不佳等问题，提出了基于“互联网＋虚拟仿真技术”的线上线下混合式教学模式。本文以控制工程专业学位研究生为例，结合《现代电气控制技术》课程，探讨了线上线下混合式教学实现途径、师生互动方法、项目驱动案例教学法以及电气控制系统虚拟仿真实验平台的构建方法，切实提升非全日制研究生的培养质量。     

Ferroptosis-Related Flavoproteins: Their Function and Stability

Ferroptosis has been described recently as an iron-dependent cell death driven by peroxidation of membrane lipids. It is involved in the pathogenesis of a number of diverse diseases. From the other side, the induction of ferroptosis can be used to kill tumor cells as a novel therapeutic approach. Because of the broad clinical relevance, a comprehensive understanding of the ferroptosis-controlling protein network is necessary. Noteworthy, several proteins from this network are flavoenzymes. This review is an attempt to present the ferroptosis-related flavoproteins in light of their involvement in anti-ferroptotic and pro-ferroptotic roles. When available, the data on the structural stability of mutants and cofactor-free apoenzymes are discussed. The stability of the flavoproteins could be an important component of the cellular death processes.     

Engineering Nanorobots for Tumor-Targeting Drug Delivery: From Dynamic Control to Stimuli-Responsive Strategy

Nanotechnology has been widely applied to the fabrication of drug delivery systems in the past decades. Recently, with the progress made in microfabrication approaches, nanorobots are steadily becoming a promising means for tumor-targeting drug delivery. In general, nanorobots can be divided into two categories: nanomotors and stimuli-responsive nanorobots. Nanomotors are nanoscale systems with the ability to convert surrounding energies into mechanical motion, whereas stimuli-responsive nanorobots are featured with activatable capacity in response to various endogenous and exogenous stimulations. In this minireview, the dynamic control of nanomotors and the rational design of stimuli-responsive nanorobots are overviewed, with particular emphasis on their contribution to tumor-targeting therapy. Moreover, challenges and perspectives associated with the future development of nanorobots are presented.     

Form selection of concomitant polymorphs: A case study informed by crystallization kinetics modeling

Molecular mechanisms and process kinetics of crystallizing concomitant polymorphs remain poorly understood. Solvent-mediated phase transformation and concomitant crystallization are difficult to be distinguished in practice, as multiple forms can be detected at the same time. Herein, we developed a population balance model to simulate a concomitant crystallization process of two polymorphs of tolfenamic acid. Our kinetic modeling aims to understand concomitant crystallization and help guide form selection of such a molecular system. Crystallization kinetics of ethanolic solutions were uncovered from induction time measurements, as well as seeded and unseeded crystallization experiments. Experimental and simulation results demonstrate that the stable form I crystallizes concomitantly with the metastable form II. The faster growing form II results in an intermediate decline in the composition of form I in crystallized samples, a characteristic feature of the concomitantly crystallized system. A four-quadrant scheme of attainable polymorph outcome was simulated under various crystallization conditions.