Multipore Zeolites: Synthesis and Catalytic Applications

In the last few years, important efforts have been made to synthesize so‐called “multipore” zeolites, which contain channels of different dimensions within the same crystalline structure. This is a very attractive subject, since the presence of pores of different sizes would favor the preferential diffusion of reactants and products through those different channel systems, allowing unique catalytic activities for specific chemical processes. In this Review we describe the most attractive achievements in the rational synthesis of multipore zeolites, containing small to extra‐large pores, and the improvements reported for relevant chemical processes when these multipore zeolites have been used as catalysts.     

考虑时滞的框架结构控制可靠性研究

提出了一种适合于确定框架结构可靠性计算方法．利用模态变换，把系统变为模态独立的振动方程，根据Field提出的假设，先定义阈值，假设控制后系统响应对阚值的穿阚率服从泊松分布，然后根据各模态的控制结果确定相应的可靠性．最后根据Veneziano提出的方法给出系统的可靠性。本文提出针对框架重点部位的控制输出来确定系统可靠性概念。考虑到实际情况，在控制方法选择针对系统不确定性具有较好控制效果的广义预测控制并考虑时滞影响。本文最后给出了计算实例。     

采场顶板稳定性动态预测及控制研究

运用人工神经网络技术,综合岩石介质条件、赋存环境条件以及工程因素3大方面的5个指标,即岩石单轴抗压强度、岩石质量指标、煤体强度、地下水状况、工作面月推进速度,建立了采场顶板稳定性动态预测模型。并以工作面月推进速度40m、60m、80m、100m分别预测了新集井田顶板稳定性分区。根据5个指标因素分析结果,对顶板稳定性影响程度由大到小排序为岩石质量指标、地下水状况、岩石单轴抗压强度、煤体强度、工作面月推进速度。     

参数灵敏度分析的神经网络方法及其工程应用

在系统分析中，参数灵敏度分析不仅为判断各系统参数的重要性大小提供了依据，量化的灵敏度指标也是后续参数估计的前提。然而，在多效实际系统中，系统参数与系统状态间的显式函数关系不易得到，导致一阶灵敏度指标无法直接求取。简化的单因素分析方法亦存在模型粗糙、精度不高的缺点。本文研究采用人工神经网络的高精度泛化映射，通过少量样本的训练，建立复杂系统中多个系统参数与系统状态间的近似映射关系，继而推导得到统一的灵敏度计算列式。简单结构的神经网络方法和解析方法的对比计算显示了方法的有效性和可靠性。最后，应用该法对某斜拉桥结构的荷载参数和刚度参数进行了考查，得到一般性结论。     

一类多体系统非完整运动最优规划的拟牛顿算法

研究带有非完整约束的一类多体系统运动规划问题。多体系统中的非完整约束通常是由不可积的速度约束或不可积的守恒律引起。在系统动量和动量矩守恒情况下,动力学方程降阶为非完整形式约束方程,系统的控制问题可转化为无漂移系统的非完整运动规划问题。文中首先导出具有多体开链系统的非完整运动模型。利用最优控制理论和最优化技术,采用输入参数化的方法将连续的最优控制问题转化为离散的最优控制问题,提出一种非完整多体系统运动规划的拟牛顿算法。最后将该方法用于自由漂浮的空间三连杆机构,仿真结果验证了该方法的有效性。     

A REVIEW ON THE APPLICATIONS OF DEEP LEARNING IN AIRCRAFT DYNAMICS AND CONTROL

飞行器动力学与控制历经多年的发展取得辉煌成就,也面临着一系列亟需解决的难题。深度学习基于存储、记忆、预训练的新模式为动力学与控制难题的解决提供了新途径。本文以提升飞行器控制自主性和智能水平为研究主题,从三个方面总结了深度学习在飞行器动力学与控制中的应用,包括：在动力学建模中应用深度学习来提升模型计算效率和建模精度、求解模型反问题;在最优控制中应用深度学习来提升轨迹规划速度、最优控制实时性和自主性;在飞行器任务设计中应用深度学习来提升任务优化的计算效率和决策水平。在此基础上,梳理了各个方案的优缺点和部分代表性成果。最后,给出了深度学习应用于飞行器动力学与控制的四点建议。     

Liquid single- and gas—liquid two-phase flows of magnetic fluid in the presence of a high non-uniform parallel magnetic field

The effect of a non-uniform parallel high magnetic field on flow control characteristics is investigated experimentally for a magnetic fluid single-phase flow and an air—magnetic fluid two-phase flow in a vertical channel. It is found that as the magnetic field strength is increased, the friction factor of the single-phase flow increases significantly. For the two-phase flow, the friction pressure loss and the head pressure loss, which is smaller than the friction loss, are negligibly small compared with the magnetic pressure loss. In the case where air is injected 27.9d upstream from the maximum magnetic field, the air flow is blocked by the magnetic force in the entrance of the magnetic field, which leads to increases in both local void fraction and pressure drop there. In the case where air is injected 1.43d downstream from the maximum magnetic field, the air flow is accelerated, resulting in a decrease in void fraction and an increase in pressure rise. In the latter case and under the present range of experimental conditions, the magnetic pumping head reaches 0.02 MPa at the highest, and the maximum circulation flow rate reaches twice as high as non-magnetically driven flow rate.     

线性时滞系统的离散最优控制

介绍了对线性时滞系统进行最优控制的设计，将具有时滞控制的线性系统离散后引入增广状态向量。获得不显含时滞的差分方程，根据时滞量的两种分类情况采用连续和离散形式的性能指标函数导出了最优控制律。控制律包含当前状态和此前若干步状态向量的叠加，最优控制律直接从时滞方程中得到，可保证系统的稳定性，此方法亦适用于大时滞的情况。数值算例验证了控制策略的有效性。     

转子振动的灰色Verhuslt预测优化控制研究

以灰色预测控制理论为基础，首次将非线性GM模型引入到转子振动主动控制中，并采用现代控制理论中的二次型优化原理，以控制力和响应加权最小为目标函数，设计了转子系统振动的灰色Verhuslt预测优化控制方案。将该方案应用于带电磁阻尼器转子轴承系统的转子振动控制中，仿真结果显示转子振动的灰色预测Verhuslt优化控制是有效的、可行的。     

Adaptive Control of Nonlinear Dynamical Systems Using a Model Reference Approach

In this paper we consider using a model reference adaptive control approach to control nonlinear systems. We consider the controller design and stability analysis associated with these type of adaptive systems. Then we discuss the use of model reference adaptive control algorithms to control systems which exhibit nonlinear dynamical behaviour using the example of a Duffing oscillator being controlled to follow a linear reference model. For this system we show that if the nonlinearity is small then standard linear model reference control can be applied. A second example, which is often found in synchronization applications, is when the nonlinearities in the plant and reference model are identical. Again we show that linear model reference adaptive control is sufficient to control the system. Finally we consider controlling more general nonlinear systems using adaptive feedback linearization to control scalar nonlinear systems. As an example we use the Lorenz and Chua systems with parameter values such that they both have chaotic dynamics. The Lorenz system is used as a reference model and a single coordinate from the Chua system is controlled to follow one of the Lorenz system coordinates.