噪声有源控制系统的性能预测

研究有源噪声控制中系统性能的实验预测方法,通过原理分析表明利用原始声场和次级声通道传递函数测试数据,能够预测有源噪声控制系统的性能并优化设计系统;结合飞机舱内的应用实际,给出了有源噪声控制系统性能预测和优化设计的基本方法和过程。该方法已被应用于国产运七飞机舱内噪声有源控制实验,取得了满意的结果。     

RBF神经网络及其在结构损伤识别中的应用研究

采用具有更好的仿生效果的径向基函数（RBF）网络对单处损伤结构及多处损伤结构的损伤程度，位置，区域，处数进行识别，网络学习方法选择了简单易行，精度高且运算速度快的正交最小二乘（OLS）法，通过实例对该方法进行了测试，并与BP网进行了比较，测试结果可验证：RBF网络及其OLS学习方法可以快速，有效，高精度地识别结构损伤状况。     

风机叶片噪声的有源消声研究

建立了风机转动时产生的动叶气动噪声和静叶干涉噪声的声学模型，推导了声功率的表达式，根据最小辐射声功率准则导出了有源消声控制方程，并探讨了次级声源的配置参数对消声效果的影响。     

叶轮机械气动噪声的研究进展

从机理研究、数值预测和控制方法3方面对叶轮机械气动噪声的研究进展和现状进行综述.主要内容包括: 离散噪声和宽频噪声的机理研究,离散噪声包括叶轮旋转的自身噪声和动静干涉噪声,宽频噪声包括进气畸变噪声、叶顶间隙流动噪声、尾迹噪声、涡脱落噪声;基于声比拟理论的方法在噪声预测方面的应用;有源控制、叶片尾迹控制、非等距叶片和多孔材料等新型噪声控制技术的综述.最后对叶轮机械气动噪声的研究现状进行了总结并对今后的研究工作进行了展望.      

梯度RBF神经网络在MEMS陀螺仪随机漂移建模中的应用

为了提高使用精度，研究了某型号MEMS陀螺仪的随机漂移模型。采用游程检验法分析了该陀螺仪随机漂移数据的平稳性，并根据该漂移为均值非平稳、方差平稳的随机过程的结论，采用梯度径向基（RBF）神经网络对漂移数据进行了建模。实验结果表明：相比经典RBF网络模型而言，这种方法建立的模型能更好地描则EMs陀螺仪的漂移特；相对于季节时间序列模型而言，其补偿效果提高了大约15％。     

基于遗传RBF网络的惯性测量组合模拟电路故障诊断

为解决惯性测量组合模拟电路的诊断不易定位到元件级故障的问题,提出了一种基于遗传RBF网络的智能诊断方法。该方法首先利用RBF神经网络快速准确识别故障的能力,以RBF的训练均方误差为遗传算法的适应度函数,依靠遗传算法强大的全局寻优能力实现故障特征选择。在特征选择的过程中,同时记录使训练均方误差达到最小的最优RBF网络,然后直接利用特征选择过程中训练好的最优RBF网络诊断故障,而无需利用特征选择后的训练数据对RBF网络进行再训练,简化了诊断步骤,同时增强了网络的抗干扰能力。仿真结果表明,该方法能有效去除冗余特征,准确诊断惯性测量组合模拟电路的故障,并有较好的抗噪能力,证明了该方法的有效性和可行性。     

基于提升小波与灰色神经网络的光纤陀螺振动误差建模

光纤陀螺在振动环境下的输出具有噪声大、漂移强的特性,必须建立合理的振动误差模型,以便使用精确的算法进行补偿,从而提高光纤陀螺的输出精度。文中首先使用Allan方差分析法分析了某型号的数字闭环光纤陀螺在振动环境下的输出信号,随后利用提升小波分离出了光纤陀螺误差模型中的白噪声及漂移误差,并提出了基于灰色理论和RBF神经网络的漂移误差建模方法。仿真结果表明,相较于传统的RBF神经网络模型,基于提升小波的灰色RBF神经网络的漂移误差建模方法能有效滤除白噪声,并将漂移误差模型的建模精度提高了一倍左右。该方法能够有效提高光纤陀螺在振动环境下的输出精度,对光纤陀螺在振动环境下的误差研究具有重要指导意义。     

封闭空间有源弹性壁振动声辐射的鲁棒H~∞控制研究

应用H∞ 控制原理 ,设计了封闭空间有源弹性壁声辐射有源力控制系统 ,折衷考虑了系统对外扰力噪声衰减问题和系统参数摄动的鲁棒问题 ;并对流体声速摄动这一常见情况进行了数值仿真。结果表明 ,系统对参数摄动具有较强的鲁棒性 ,同时能有效抑制外扰 ,与目前较先进的基于自适应滤波的前馈控制系统相比 ,具有不需要参考信号 ,系统相对简单 ,易于工程实现等优点     

封闭空间有源弹性壁振动声辐射的鲁棒H∞控制研究

应用H∞控制原理,设计了封闭空间有源弹性壁声辐射有源力控制系统,折衷考虑了系统对外扰力噪声衰减问题和系统参数摄动的鲁棒问题,并对流体声速摄动这一常见情况进行了数值仿真,结果表明,系统对参数摄动具有较强的鲁棒性,同时能有效抑制外扰,与目前较先进的基于自适应滤波的前馈系统相比,具有不需要参考信号,系统相对简单,易于工程实现等优点。∞     

基于RBF网络的激光陀螺标度因数数据建模研究

前向神经网络中的径向基函数(RBF)网络是一种局部逼近网络,它用局部逼近的总和达到对训练数据的全局逼近,在理论上可以实现全局最优.该文利用径向基函数神经网络对某一温度段的陀螺标度因数的温度数据进行建模处理,并利用各组数据建立一种两因素RBF网络,这两个输入因素选择为温度以及各个温度值对于所属组初始温度的增量.仿真结果表明,所建立的两因素RBF网络可以精确地拟合各温度下的标度因数温度数据,仿真数据的误差与均方差比用BP网络训练的数据效果要好,在数值上提高了近一个数量级.     

Structural reliability analysis for implicit performance function using radial basis function network

This is the second paper of our work on structural reliability analysis for implicit performance function. The first paper proposed structural reliability analysis methods using multilayer perceptron artificial neural network [Deng, J., Gu, D.S., Li, X.B., Yue, Z.Q., 2005. Structural reliability analysis for implicit performance function using artificial neural network. Structural Safety 25 (1), 25–48]. This paper presents three radial basis function network (RBF) based reliability analysis methods, i.e. RBF based MCS, RBF based FORM, and RBF based SORM. In these methods, radial basis function network technique is adopted to model and approximate the implicit performance functions or partial derivatives. The RBF technique uses a small set of the actual data of the implicit performance functions, which are obtained via physical experiments or normal numerical analysis such as finite element methods for the complicated structural system, and are used to develop a trained RBF generalization algorithm. Then a large number of the function values and partial derivatives of implicit performance functions can be readily obtained by simply extracting information from the established and successfully trained RBF network. These function values and derivatives are used in conventional MCS, FORM or SORM to constitute RBF based reliability analysis algorithms. Examples are presented in the paper to illustrate how the proposed RBF based methods are used in structural reliability analysis. The results are well compared with those obtained by the conventional reliability methods such as the Monte-Carlo simulation, multilayer perceptrons networks, the response surface method, the FORM method 2, and so on. The examples showed the proposed approach is applicable to structural reliability analysis involving implicit performance functions.     

基于神经网络算法的建筑结构振动分散控制研究

针对地震作用下建筑结构振动分散控制问题,引入神经网络算法,研究结构振动分散神经网络控制策略,来解决分散控制中各子系统的耦合问题和神经网络算法的训练成本问题.利用径向基函数RBF(Radical Basis Function)神经网络模型并基于newrb函数构建了RBF神经网络控制器,对某20层Benchmark结构模型分别进行集中控制和多工况子系统划分分散控制的数值模拟分析,结果表明,提出的各子系统耦合的分散RBF神经网络振动控制策略考虑了子系统间的信息共享,可有效控制结构的振动响应,且子系统达到理想训练结果所需的训练次数与BP网络相比显著降低.     

Potentials based optimization with embedded Markov chain for stochastic constrained system

In this paper, a RBF neural network based on-line optimization algorithm with performance potentials analysis method is presented for a class of stochastic constrained dynamic systems. The control signals of the considered systems are constrained to a range according to a subset of the whole state space. With the conception of an embedded Markov chain, an optimization approach on the basis of potentials is presented for a stochastic constrained system, where the optimization criterion is the long-time average performance. With this approach, the computation burden has been reduced because it only requires one to compute the control strategy on the states concerned, which are a subset of the whole state space. Furthermore, with the characteristic of approximation performance of RBF neural network, the potentials and the transition probability matrix are estimated conveniently by a sample path compared with the statistic approach or the method by solving the Poisson equation. The effectiveness of the optimization approach has been shown by the simulation results, finally.     

基于前向线性预测算法的光纤陀螺零漂的神经网络建模

在详细分析光纤陀螺零漂的基础上,提出了先用滤波算法对光纤陀螺信号进行预处理,然后采用RBF神经网络对滤波后的信号进行建模的方法。针对光纤陀螺信号特点分别采用FLP算法、小波滤波算法、解相关变步长LMS自适应滤波算法对其进行了预处理,比较三种滤波方法,小波滤波算法效果优于其它两种预处理方法,但针对基于预处理后的陀螺信号采用RBF神经网络进行建模时,小波滤波预处理后的信号在建模精度上却是最差的,而对FLP算法滤波后的信号进行RBF建模,建模精度提高了两个数量级。结果表明:基于FLP算法的RBF神经网络在光纤陀螺中的建模是有效的,可大大提高建模的精度。     

Adaptive sliding mode control of dynamic system using RBF neural network

This paper presents a robust adaptive sliding mode control strategy using radial basis function (RBF) neural network (NN) for a class of time varying system in the presence of model uncertainties and external disturbance. Adaptive RBF neural network controller that can learn the unknown upper bound of model uncertainties and external disturbances is incorporated into the adaptive sliding mode control system in the same Lyapunov framework. The proposed adaptive sliding mode controller can on line update the estimates of system dynamics. The asymptotical stability of the closed-loop system, the convergence of the neural network weight-updating process, and the boundedness of the neural network weight estimation errors can be strictly guaranteed. Numerical simulation for a MEMS triaxial angular velocity sensor is investigated to verify the effectiveness of the proposed adaptive RBF sliding mode control scheme.     

空间连续型机器人自适应鲁棒容错控制

针对空间连续型机器人系统三臂节执行器并发故障的问题,提出一种自适应鲁棒容错控制算法.采用非奇异快速终端滑模控制器,并通过自适应RBF(Radial Basis Function)神经网络在线调整控制器的切换项增益,使控制器在模型参数摄动和外部干扰下依旧具有较高的跟踪精度和较强的鲁棒性.基于Lyapunov稳定性理论,证明了该控制器可以保证整个系统的渐进稳定性.仿真结果验证了本文算法的有效性.     

Nonlinear vibration semi-active control of automotive steering using magneto-rheological damper

Traffic accidents are often caused by vibration of automotive steering because the vibration can make a vehicle run like a snake. A?novel semi-active vibration control strategy of automotive steering with magneto-rheological (MR) damper is proposed in this paper. An adaptive RBF neural sliding mode controller is designed for the vibration system. It is showed that an equivalent dynamic model for the vibration system is established by using Lagrange method, and then treats it as actual system partially. A?feedback control law is designed to make this nominal model stable. Uncertain part of system and outside disturbance are estimated using RBF neural network, and their upper boundary is obtained automatically. By constructing reasonable switch function, state variables can arrive at origin asymptotically along the sliding mode. Strong robust character of control system is proved by stability analysis and a numerical simulation example is performed to support this control scheme.     

含分数阻尼特性元件的多体系统动力学研究

在绝对节点坐标体系下研究了具有分数导数阻尼特性元件的多体系统动力学建模、求解问题. 采用基于绝对节点坐标的无闭锁效应剪变梁单元离散柔性构件,建立了含常数质量矩阵的系统动力学方程, 并采用数值耗散可控的广义a方法求解. 通过数值算例计算,对比研究了算法参数与阻尼项的分数指数对系统动力学响应的影响规律.该方法可以进一步扩展到众多工程实际问题研究中.      

模糊—神经网络控制算法及其在离心力—振动复合环境试验系统中的应用

针对离心－振动复合环境试验系统所存在的耦合性、非线性和不确定性提出了一种模糊－神经网络控制算法,利用被控对象输入输出信息离线、在线相结合学习系统的动态特性,对时变、非线性系统进行跟踪控制,并研究了该算法在系统中的实现方法。实现表明了控制系统具有良好的跟踪能力。该算法也适用于快速变化这类系统的实时控制。     

Decentralized adaptive neural control of nonlinear systems with unknown time delays

In this paper, a novel decentralized adaptive neural control scheme is proposed for a class of uncertain multi-input and multi-output (MIMO) nonlinear time-delay systems. RBF neural networks (NNs) are used to tackle unknown nonlinear functions, then the decentralized adaptive NN tracking controller is constructed by combining Lyapunov–Krasovskii functions and the dynamic surface control (DSC) technique along with the minimal-learning-parameters (MLP) algorithm. The proposed controller guarantees semi-global uniform ultimate boundedness (SGUUB) of all the signals in the closed-loop large-scale system, while the tracking errors converge to a small neighborhood of the origin. An advantage of the proposed control scheme lies in that the number of adaptive parameters for each subsystem is reduced to one, and three problems of “computational explosion,” “dimension curse” and “controller singularity” are solved, respectively. Finally, a numerical simulation is presented to demonstrate the effectiveness and performance of the proposed scheme.