Lyapunov functions for stable cascades and applications to global stabilization

This paper generalizes recent Lyapunov constructions for a cascade of two nonlinear systems, one of which is stable rather than asymptotically stable. A new cross-term construction in the Lyapunov function allows us to replace earlier growth conditions by a necessary boundedness condition. This method is instrumental in the global stabilization of feedforward systems, and new stabilization results are derived from the generalized construction.

Active control of high frequency vibration in uncertain structures

The active suppression of structural vibration is normally achieved by either feedforward or feedback control. In the absence of a suitable reference signal feedforward control cannot be employed and feedback control is the only viable approach. Conventional feedback control algorithms (e.g. LQR and LQG) are designed on the basis of a mathematical model of the system and ideally the performance of the system should be robust against uncertainties in this model. The aim of this paper is to numerically investigate the robustness of LQR and LQG algorithms by designing the controller for a nominal system, and then assessing (via Monte Carlo simulation) the effects of uncertainties in the system. The ultimate concern is with the control of high frequency vibrations, where the short wavelength of the structural deformation induces a high sensitivity to imperfection. It is found that standard algorithms such as LQR and LQG are generally unfeasible for this case. This leads to a consideration of design strategies for the robust active control of high frequency vibrations. The system chosen for the numerical simulation concerns two coupled plates, which are randomized by the addition of point masses at random locations.

Modeling and optimization of semiconductor manufacturing process with neural networks

A neural network-based process model is proposed to optimize the semiconductor manufacturing process. Being different from some works in several research groups which developed neural network-based models to predict process quality with a set of process variables of only single manufacturing step, we applied this model to wafer fabrication parameters control and wafer lot yield optimization. The original data are collected from a wafer fabrication line, including technological parameters and wafer test results. The wafer lot yield is taken as the optimization target. Learning from historical technological records and wafer test results, the model can predict the wafer yield. To eliminate the "bad" or noisy samples from the sample set, an experimental method was used to determine the number of hidden units so that both good learning ability and prediction capability can be obtained.

单相机全向立体视觉研究

折反射全向成像系统是由普通透视相机和反射镜面组成的全向成像装置，可实时获取360°无需拼接的全景图像，近年来已成为研究热点并在视频会议、三维重建和移动机器人导航等领域有着广泛的应用。 本文主要对单相机全向立体视觉系统的设计、标定、匹配以及三维重建展开研究。介绍了一种可实时获取全向三维信息的折反射全向立体视觉光学装置OSVOD（Omnidirectional Stereo Vision Optical Device），OSVOD由两个双曲面镜和一个普通透视相机组成。其中两个双曲镜面上下同轴、间隔一定距离固定在一个玻璃筒内，下镜面中间开有一孔，上镜面通过下镜面的孔在相机像平面上成像，这样空间一点经上下反射镜的反射在像平面上有两个像点，用一个相机实现了立体视觉。两镜面的共同轴和相机镜头的光轴共线，共同焦点和镜头的光心重合，该配置能保证系统满足单一视点约束SVP（Single View-Point）。本结构配置也使系统的外极线呈一系列的放射线，对应点匹配简单。此外两镜面的间隔安装也使得系统的等效基线较长，从而具有较高的精度。 本文第一部分对当前的各种全向成像方法进行了简单介绍，并对各方法的特点做了归纳。第二部分介绍折反射全向视觉的研究现状，就各种反射镜面的成像特点做了对比。 第三部分介绍OSVOD的设计方法，包括机构的设计和镜面的设计，并对设计的结果做了误差分析。 第四部分是OSVOD的标定研究。给出了一种包括OSVOD中相机和镜面位置关系在内的系统参数的标定方法。该方法利用空间坐标已知的标定点在像平面上成的像，结合系统成像模型反算出标定点的空间坐标，再利用标定点的已知空间坐标和反算出的空间坐标建立方程，运用基于Levenberg-Marquardt的反向传播算法(backpropagation)标定相机与反射镜面间的安装偏差。该标定方法可推广到所有的折反射成像系统。 第五部分是基于全向图像的匹配研究。针对系统获取的立体图像对之间成像比例存在较大的差异，首先将图像展开成柱面投影图像，然后就下镜面成像展开的柱面做Canny边缘检测，得到了图像的边缘点；就得到的边缘点在展开的两幅柱面图像上做直接相关匹配。最后将获取的匹配点做一致性校验，并对一致性校验通过的匹配点做三维计算，生产稀疏的三维图像。 最后是结论和将来的工作展望。

基于人工神经网络的PC钢棒力学性能预报模型

提出一种PC钢棒抗拉强度的人工神经网络模型方法,采用4×9×1的三层前向BP网络结构,模型主要因素为淬火温度、回火温度、含碳量和单位长度质量。经1500余次训练后,误差平方和

有缆水下机器人主动升沉补偿控制研究

针对母船的升沉运动影响有缆水下机器人的释放与回收的工程实际需求,提出了利用液压绞车降低中继器的升沉速度来实现水下机器人主动升沉补偿控制的方法,来提高水下机器人释放与回收的安全性能。建立液压绞车的数学模型并设计主动升沉补偿前馈控制器。水下机器人主动升沉补偿实验表明液压系统的非线性降低了液压绞车主动升沉补偿前馈控制的升沉补偿效率。针对液压绞车的非线性特性,设计了液压绞车主动升沉补偿预测控制算法,仿真实验表明基于液压系统参数辨识的液压绞车主动升沉补偿预测控制可以得到较高的升沉补偿效率。

自适应神经元实现的直线永磁同步电机伺服系统的预见前馈补偿

介绍了高精度微进给直线永磁同步电机伺服系统的 IP位置控制 ,提出了对该系统的最优预见前馈补偿 ,以提高系统的跟踪性能。为了能适应系统参数的变化 ,采用自适应神经元来实现预见前馈补偿。自适应神经元的输入向量为预见步数 ,权值为预见前馈系数 ,权值具有明确的物理意义 ,而且权的初始值不是随机数 ,而是系统在额定情况下算出的预见前馈系数 ,从而加快了神经元的调整速度 ,提高了系统的跟踪能力。仿真实验结果证明了所提出方案的有效性

机器人机械手的鲁棒性稳定跟踪控制

为工业机器人机械手提出了一种稳定跟踪控制法．这种控制方法由前馈控制器、反馈控制器组成．前馈控制根据期望轨线用计算力矩法得到；反馈控制由线性ＰＩＤ控制项和非线性ＰＤ控制项组成，这种控制方法能使跟踪误差逐渐趋近于零．最后，给出了ＰＵＭＡ５６０机器人的计算机仿真实验验证此控制方法的有效性

工业机器人机械手的小脑模型自适应控制

本文为动力学控制工业机器人机械手提出一种综合控制算法。该控制算法,利用小脑模型算术计算机模块模拟机器人机械手的动力学方程并计算实现期望运动所需力矩作为前馈力矩控制项;利用自适应控制器实现反馈控制,以消除由输入扰动和参数变化而引起的机器人机械手运动误差。这种控制方法在时间上是有效的,且很适合于定点实现。控制方法的有效性通过四自由度的直接驱动机器人前两个关节的计算机仿真实验得到验证。

广义预测极点配置前馈控制的一种算法

本文提出了广义预测极点配置前馈自校正控制算法,计算机仿真结果表明,该算法控制质量好,能够消除系统可测扰动对输出的影响。

前馈在快速高精度数字随动系统中的应用

本文给出了一种具有前馈等值的二阶无静差数字随动系统的设计方法。按照这种设计方法,只要在计算的基础上适当地调整前馈系数和开环增益,即可得到满意的系统性能指标。

基于神经网络的激光熔覆高度预测

激光成形过程中,对熔覆高度进行实时检测,从而实现熔覆高度闭环控制是成形高质量零件的保证。激光成形过程是一个多参数耦合的非线性过程,大量激光参数对成形熔覆表面质量具有重要影响。在分析激光参数对熔覆高度影响的基础上,建立利用激光工艺参数预测熔覆高度的误差反向传播(Backpropagation,BP)神经网络模型,完成了网络算法设计。通过激光成形试验采集样本,利用训练样本对所建立的网络进行训练,完成网络输入输出高度映射关系,并利用测试样本对所训练的网络进行检验。仿真试验表明,神经网络熔覆高度预测模型具有很高的精度,验证了该预测模型在理论和实践上的可行性与有效性。神经网络熔覆高度预测模型为实现激光加工过程熔覆高度实时预测与闭环控制打下基础,对提高成形产品质量具有重要意义。

A Theory of Networks for Appxoimation and Learning

Learning an input-output mapping from a set of examples, of the type that many neural networks have been constructed to perform, can be regarded as synthesizing an approximation of a multi-dimensional function, that is solving the problem of hypersurface reconstruction. From this point of view, this form of learning is closely related to classical approximation techniques, such as generalized splines and regularization theory. This paper considers the problems of an exact representation and, in more detail, of the approximation of linear and nolinear mappings in terms of simpler functions of fewer variables. Kolmogorov's theorem concerning the representation of functions of several variables in terms of functions of one variable turns out to be almost irrelevant in the context of networks for learning. We develop a theoretical framework for approximation based on regularization techniques that leads to a class of three-layer networks that we call Generalized Radial Basis Functions (GRBF), since they are mathematically related to the well-known Radial Basis Functions, mainly used for strict interpolation tasks. GRBF networks are not only equivalent to generalized splines, but are also closely related to pattern recognition methods such as Parzen windows and potential functions and to several neural network algorithms, such as Kanerva's associative memory, backpropagation and Kohonen's topology preserving map. They also have an interesting interpretation in terms of prototypes that are synthesized and optimally combined during the learning stage. The paper introduces several extensions and applications of the technique and discusses intriguing analogies with neurobiological data.

A Connection Between GRBF and MLP

Both multilayer perceptrons (MLP) and Generalized Radial Basis Functions (GRBF) have good approximation properties, theoretically and experimentally. Are they related? The main point of this paper is to show that for normalized inputs, multilayer perceptron networks are radial function networks (albeit with a non-standard radial function). This provides an interpretation of the weights w as centers t of the radial function network, and therefore as equivalent to templates. This insight may be useful for practical applications, including better initialization procedures for MLP. In the remainder of the paper, we discuss the relation between the radial functions that correspond to the sigmoid for normalized inputs and well-behaved radial basis functions, such as the Gaussian. In particular, we observe that the radial function associated with the sigmoid is an activation function that is good approximation to Gaussian basis functions for a range of values of the bias parameter. The implication is that a MLP network can always simulate a Gaussian GRBF network (with the same number of units but less parameters); the converse is true only for certain values of the bias parameter. Numerical experiments indicate that this constraint is not always satisfied in practice by MLP networks trained with backpropagation. Multiscale GRBF networks, on the other hand, can approximate MLP networks with a similar number of parameters.