基于顶点法向量约束的Catmull-Clark细分插值方法

提出一种基于顶点法向量约束实现插值的两步Catmull-Clark细分方法.第一步,通过改造型Catmull-Clark细分生成新网格.第二步,通过顶点法向量约束对新网格进行调整.两步细分分别运用渐进迭代方法和拉格朗日乘子法,使得极限曲面插值于初始控制顶点和法向量.实验结果证明了该方法可同时实现插值初始控制顶点和法向量,极限曲面具有较好的造型效果.     

A new iterative approach for multi-objective fault detection observer design and its application to a hypersonic vehicle

This paper addresses the multi-objective fault detection observer design problems for a hypersonic vehicle. Owing to the fact that parameters’ variations, modelling errors and disturbances are inevitable in practical situations, system uncertainty is considered in this study. By fully utilising the orthogonal space information of output matrix, some new understandings are proposed for the construction of Lyapunov matrix. Sufficient conditions for the existence of observers to guarantee the fault sensitivity and disturbance robustness in infinite frequency domain are presented. In order to further relax the conservativeness, slack matrices are introduced to fully decouple the observer gain with the Lyapunov matrices in finite frequency range. Iterative linear matrix inequality algorithms are proposed to obtain the solutions. The simulation examples which contain a Monte Carlo campaign illustrate that the new methods can effectively reduce the design conservativeness compared with the existing methods.     

热轧带钢层流冷却系统的前馈-反馈控制及其优化

以热轧层流冷却系统为研究对象,对如何提高带钢卷取温度控制精度进行了研究;在带钢冷却模型的基础上,对预设定模块进行了前馈补偿;考虑模型的不确定性和大时滞特性,采用模糊Smith预估器提高了反馈控制的精度;结果证明所采用的这些措施大大提高了卷取温度的控制精度。     

智能假肢感知和约束变增益迭代学习控制

针对膝关节输入力矩为受限的阻尼力矩,步态跟随控制缺乏在线调节问题,提出用离线和在线集成的变增益一阶D型闭环迭代学习方法进行步态跟随控制。建立系统动力学模型,给出基于有限状态机的步态感知和人腿摆动相步速间接感知方法,减小跟随滞后。设计在线变步长指数增益调节算子,并针对输入力矩受限,设计离线的带惩罚因子的指数增益调节算子。利用谱半径约束条件,分析算法的收敛性。在真人比例虚拟样机模型上进行控制仿真实验,给出了固定增益和变增益调节下轨迹跟踪曲线。结果证明,提出的增益调节算子可保证输入在有效范围内,并可提高跟踪速度。     

基于迭代继电反馈的多变量PID控制器自整定

PID控制是工业过程中最常用的控制方法,但在实际生产过程中,被控过程往往是多变量、有耦合的,常规PID控制器参数往往整定不良、性能欠佳,对运行工况的适应性较差。为此,将迭代反馈理论和继电整定方法有机结合起来,提出一种适用于存在耦合的多变量系统PID控制器的参数整定方法。运用该方法整定PID参数,不需要被控对象的数学模型,而且具有速度快、效果好等优点。     

分域设计迭代学习控制器抑制非重复性扰动

为了抑制迭代方向上已知重复样式的非重复性输出扰动,提出了迭代学习控制(Iterative Learning Control,ILC)的分域算法。时间域内设计传统PID型迭代学习控制器,并且优化其参数;迭代域内利用内模原理抑制非重复性输出扰动,跟踪期望轨迹;利用加权思想将两者相结合,得到迭代学习控制器的分域设计算法。相对于已有算法,建立了针对一般扰动的设计框架,并且合理配置了算法的参数,使收敛速度及精度有所提高。仿真结果说明了该算法的有效性。     

Expansion of a matrix in terms of external products of configuration vectors

The paper proposes a neural-net iterative algorithm that allows us to represent any random symmetrical N×N matrix as a weighted Hebbian series of configuration vectors with a given accuracy. The iterative algorithm is shown to demonstrate the fastest convergence when the vectors of expansion are stable nods of the N-dimensional space corresponding to the extremums of the neural-net energy functional. It so proves that all conclusions about neural networks and optimization algorithms that are based on Hebbian matrices are true for any other type of matrix. The text was submitted by the author in English.     

The application of a proportional difference type iterative learning control in active vibration control

Active vibration control is used to instead passive solutions in order to increase the performance at low frequencies, in a variety of different engineering systems. This method has improved the performance specifically. The paper will propose a proportional difference type iterative learning control algorithm to deal with the periodic sources and to investigate the active solution as the three degrees of freedom ＂mass spring damping＂ mount. Simulation shows that this method could get a better tracking performance, also the displacement could converge to zero with a fast speed.     

Neural Network Method for Solving Partial Differential Equations

A method is presented to solve partial differential equations (pde's) and its boundary and/or initial conditions by using neural networks. It uses the fact that multiple input, single output, single hidden layer feedforward networks with a linear output layer with no bias are capable of arbitrarily well approximating arbitrary functions and its derivatives, which is proven by a number of authors and well known in literature. Knowledge about the pde and its boundary and/or initial conditions is incorporated into the structures and the training sets of several neural networks. In this way we obtain networks of which some are specifically structured. To find the solution of the pde and its boundary and/or initial conditions we have to train all obtained networks simultaneously. Therefore we use an evolutionary algorithm to train the networks. We demonstrate the working of our method by applying it to two problems.     

一种时序电路的测试生成算法

本文提出了一种高效的时序电路测试生成算法,该算法是建立在自适应算法的基础上,并使用了十七值逻辑模型。文章详细介绍了该测试算法的内容及其实现过程,并举例说明了该算法的测试效率。