Identification of time-varying system and energy-based optimization of adaptive control in seismically excited structure

The combination of structural health monitoring and vibration control is of great importance to provide components of smart structures.While synthetic algorithms have been proposed,adaptive control that is compatible with changing conditions still needs to be used,and time-varying systems are required to be simultaneously estimated with the application of adaptive control.In this research,the identification of structural time-varying dynamic characteristics and optimized simple adaptive control are integrated.First,reduced variations of physical parameters are estimated online using the multiple forgetting factor recursive least squares(MFRLS)method.Then,the energy from the structural vibration is simultaneously specified to optimize the control force with the identified parameters to be operational.Optimization is also performed based on the probability density function of the energy under the seismic excitation at any time.Finally,the optimal control force is obtained by the simple adaptive control(SAC)algorithm and energy coefficient.A numerical example and benchmark structure are employed to investigate the efficiency of the proposed approach.The simulation results revealed the effectiveness of the integrated online identification and optimal adaptive control in systems.

Stability Criteria for Large－Scale Linear Systems with Structured Uncertainties

The robust stability analysis for large scale linear systems with structured time varying uncertainties is investigated in this paper.By using the scalar Lyapunov functions and the properties of M matrix and nonnegative matrix,stability robustness measures are proposed.The robust stability criteria obtained are applied to derive an algebric criterion which is expressed directly in terms of plant parameters and is shown to be less conservative than the existing ones.A numerical example is given to demonstrate the stability criteria obtained and to compare them with the previous ones.

Robust H_∞ control for aseismic structures with uncertainties in model parameters

This paper presents a robust H∞ output feedback control approach for structural systems with uncertainties in model parameters by using available acceleration measurements and proposes conditions for the existence of such a robust output feedback controller. The uncertainties of structural stiffness, damping and mass parameters are assumed to be norm-bounded. The proposed control approach is formulated within the framework of linear matrix inequalities, for which existing convex optimization techniques, such as the LM1 toolbox in MATLAB, can be used effectively and conveniently. To illustrate the effectiveness of the proposed robust H∞ strategy, a six-story building was subjected both to the 1940 E1 Centro earthquake record and to a suddenly applied Kanai-Tajimi filtered white noise random excitation. The results show that the proposed robust H∞ controller provides satisfactory results with or without variation of the structural stiffness, damping and mass parameters.

On the Application of Mixed Models of Probability and Convex Set for Time-Variant Reliability Analysis

In time-variant reliability problems,there are a lot of uncertain variables from different sources.Therefore,it is important to consider these uncertainties in engineering.In addition,time-variant reliability problems typically involve a complexmultilevel nested optimization problem,which can result in an enormous amount of computation.To this end,this paper studies the time-variant reliability evaluation of structures with stochastic and bounded uncertainties using a mixed probability and convex set model.In this method,the stochastic process of a limit-state function with mixed uncertain parameters is first discretized and then converted into a timeindependent reliability problem.Further,to solve the double nested optimization problem in hybrid reliability calculation,an efficient iterative scheme is designed in standard uncertainty space to determine the most probable point(MPP).The limit state function is linearized at these points,and an innovative random variable is defined to solve the equivalent static reliability analysis model.The effectiveness of the proposed method is verified by two benchmark numerical examples and a practical engineering problem.

Finite element model updating for large span spatial steel structure considering uncertainties

In order to establish the baseline finite element model for structural health monitoring,a new method of model updating was proposed after analyzing the uncertainties of measured data and the error of finite element model.In the new method,the finite element model was replaced by the multi-output support vector regression machine(MSVR).The interval variables of the measured frequency were sampled by Latin hypercube sampling method.The samples of frequency were regarded as the inputs of the trained MSVR.The outputs of MSVR were the target values of design parameters.The steel structure of National Aquatic Center for Beijing Olympic Games was introduced as a case for finite element model updating.The results show that the proposed method can avoid solving the problem of complicated calculation.Both the estimated values and associated uncertainties of the structure parameters can be obtained by the method.The static and dynamic characteristics of the updated finite element model are in good agreement with the measured data.

Adaptive Variable Structure Control of MIMO Nonlinear Systems with Time-varying Delays and Unknown Dead-zones

In this paper, adaptive variable structure neural control is presented for a class of uncertain multi-input multi-output （MIMO） nonlinear systems with state time-varying delays and unknown nonlinear dead-zones. The unknown time-varying delay uncer- tainties are compensated for using appropriate Lyapunov-Krasovskii functionals in the design. The approach removes the assumption of linear function outside the deadband without necessarily constructing a dead-zone inverse as an added contribution. By utilizing the integral-type Lyapunov function and introducing an adaptive compensation term for the upper bound of the residual and optimal approximation error as well as the dead-zone disturbance, the closed-loop control system is proved to be semi-globally uniformly ultimately bounded. In addition, a modified adaptive control algorithm is given in order to avoid the high-frequency chattering phenomenon. Simulation results demonstrate the effectiveness of the approach.

A Receiver Structure for Frequency-Flat Time-Varying Rayleigh Channels and Performance Analysis

This paper proposes a wavelet based receiver structure for frequency-flat time-varying Rayleigh channels, consisting of a receiver front-end followed by a Maximum A-Posteriori (MAP) detector. Discretization of the received continuous time signal using filter banks is an essential stage in the front-end part, where the Fast Haar Transform (FHT) is used to reduce complexity. Analysis of our receiver over slow-fading channels shows that it is optimal for certain modulation schemes. By comparison with literature, it is shown that over such channels our receiver can achieve optimal performance for Time-Orthogonal modulation. Computed and Monte-Carlo simulated performance results over fast time-varying Rayleigh fading channels show that with Minimum Shift Keying (MSK), our receiver using four basis functions (filters) lowers the error floor by more than one order of magnitude with respect to other techniques of comparable complexity. Orthogonal Frequency Shift Keying (FSK) can achieve the same performance as Time-Orthogonal modulation for the slow-fading case, but suffers some degradation over fast-fading channels where it exhibits an error floor. Compared to MSK, however, Orthogonal FSK provides better performance.

Sensitivity analysis and probability modelling of the structural response of a single-layer reticulated dome subjected to an external blast loading

The structural response of a single-layer reticulated dome to external explosions is shaped by many variables,and the associated uncertainties imply non-deterministic results.Existing deterministic methods for predicting the consequences of specific explosions do not account for these uncertainties.Therefore,the impact of the uncertainties associated with these input variables on the structures’response needs to be studied and quantified.In this study,a parametric uncertainty analysis was conducted first.Then,local and global sensitivity analyses were carried out to identify the drivers of the structural dynamic response.A probabilistic structural response model was established based on sensitive variables and a reasonable sample size.Furthermore,some deterministic empirical methods for explosion-resistance design,including the plane blast load model of CONWEP,the curved blast load model under the 50%assurance level,and the 20%mass-increased method,were used for evaluating their reliability.The results of the analyses revealed that the structural response of a single-layer reticulated dome to an external blast loading is lognormally distributed.Evidently,the MB0.5 method based on the curved reflector load model yielded results with a relatively stable assurance rate and reliability,but CONWEP did not;thus,the 1.2MB0.5 method can be used for making high-confidence simple predictions.In addition,the results indicated that the structural response is very sensitive to the explosion parameters.Based on these results,it is suggested that for explosion proofing,setting up a defensive barrier is more effective than structural strengthening.

Uncertainty quantification of predicting stable structures for high-entropy alloys using Bayesian neural networks

High entropy alloys(HEAs)have excellent application prospects in catalysis because of their rich components and configuration space.In this work,we develop a Bayesian neural network(BNN)based on energies calculated with density functional theory to search the configuration space of the CoNiRhRu HEA system.The BNN model was developed by considering six independent features of Co-Ni,Co-Rh,CoRu,Ni-Rh,Ni-Ru,and Rh-Ru in different shells and energies of structures as the labels.The root mean squared error of the energy predicted by BNN is 1.37 me V/atom.Moreover,the influence of feature periodicity on the energy of HEA in theoretical calculations is discussed.We found that when the neural network is optimized to a certain extent,only using the accuracy indicator of root mean square error to evaluate model performance is no longer accurate in some scenarios.More importantly,we reveal the importance of uncertainty quantification for neural networks to predict new structures of HEAs with proper confidence based on BNN.

Volatility spillovers,structural breaks and uncertainty in technology sector markets

This study uses the dynamic conditional correlation to investigate how technology subsector stocks interact with financial assets in the face of economic and financial uncertainty.Our results suggest that structural breaks have diverse effects on financial asset connectedness and that the level of bond linkage increases when the trend breaks.We see a growing co-movement between the technology sector and major financial assets when uncertainty is considered.Overall,our findings indicate that the connectedness response varies depending on the type of uncertainty shock.

Structural Interval Reliability Algorithm Based on Bernstein Polynomials and Evidence Theory

Structural reliability is an important method to measure the safety performance of structures under the influence of uncertain factors.Traditional structural reliability analysis methods often convert the limit state function to the polynomial form to measure whether the structure is invalid.The uncertain parameters mainly exist in the form of intervals.This method requires a lot of calculation and is often difficult to achieve efficiently.In order to solve this problem,this paper proposes an interval variable multivariate polynomial algorithm based on Bernstein polynomials and evidence theory to solve the structural reliability problem with cognitive uncertainty.Based on the non-probabilistic reliability index method,the extreme value of the limit state function is obtained using the properties of Bernstein polynomials,thus avoiding the need for a lot of sampling to solve the reliability analysis problem.The method is applied to numerical examples and engineering applications such as experiments,and the results show that the method has higher computational efficiency and accuracy than the traditional linear approximation method,especially for some reliability problems with higher nonlinearity.Moreover,this method can effectively improve the reliability of results and reduce the cost of calculation in practical engineering problems.

基于μ综合的平面单臂柔性梁鲁棒振动控制研究

针对平面单臂柔性梁轨迹跟踪及末端弹性振动问题,提出了基于μ综合的控制方法对其进行抑振控制。首先,采用假设模态法描述弹性变形,并基于Lagrange方程建立动力学模型;其次,考虑载荷质量和结构阻尼的参数不确定性及建模摄动,运用线性分式变换,选用加权函数建立混合不确定模型。通过引入理想模型改进μ综合的标准控制结构,提高了系统收敛性并改善了闭环系统性能。基于μ综合控制方法,设计了系统鲁棒控制器。仿真结果表明,控制器的结构奇异值上下界均小于1,表示系统具有良好的鲁棒稳定性及鲁棒性能。通过与PD控制器对比分析,表明在存在不确定性和干扰的情况下,所提出的控制策略能更高精度地进行轨迹跟踪,更有效地衰减末端振动,验证了该控制策略的有效性。

董事网络位置对企业风险承担的影响研究——来自中国上市公司的经验证据

风险承担是企业实现持续发展的重要因素。基于社会网络视角,本文运用中国A股上市公司2011~2022年的样本数据,实证研究了董事网络位置对企业风险承担的影响及其作用机理。结果表明:董事网络中心度显著提升了企业风险承担水平,结构洞对企业风险承担起到显著的抑制作用。知识溢出在董事网络中心度以及结构洞对企业风险承担的影响中均发挥遮掩效应;进一步研究发现,经济政策不确定性正向调节董事网络位置对企业风险承担水平的影响。基于研究结论,企业应该合理构建董事网络关系;优化董事网络结构,适度获取外部知识,提升决策质量。

供应不确定情境下制造商企业采购策略选择

作为供应链条上的关键节点,制造商企业与其上下游企业之间在“四流”方面有着紧密关联,其供应链结构的稳定性受到内、外部等多重因素影响,加之供应中断风险的突发性和多样性,一旦发生供应中断则将会对制造商企业造成巨大经济损失,故研究供应不确定条件下制造商企业的采购应对行为至关重要。基于制造商企业视角,将与其关联度最紧密的一、二级供应商企业作为供应对象,构建模拟供应不确定条件下制造商企业采购应对模式的数学推导模型,运用统计工具绘制出推导结果,并对相关参数进行全面的敏感性分析,然后通过数值分析研究市场价格的敏感性、供应商企业配件合格率等因素对制造商企业采购策略产生的影响,最后为供应中断条件下如何提升供应链结构的稳定性提供相应的建议。通过该研究,以期实现在供应不确定条件下制造商企业最大的期望利润,以及解决因采购策略不当所引起的经济问题。

Delay-dependent criteria for the robust stability of systems with time-varying delay

The problem of delay-dependent robust stability for systems with time-varying delay has been considered. By using the S-procedure and the Park's inequality in the recent issue, a delay-dependent robust stability criterion which is less conservative than the previous results has been derived for time-delay systems with time-varying structured uncertainties. The same idea has also been easily extended to the systems with nonlinear perturbations. Numerical examples illustrated the effectiveness and the improvement of the proposed approach. Keywords Delay-dependent criteria - Robust stability - Time-varying structured uncertainties - Nonlinear perturbations - Linear matrix inequality This work was supported by the Doctor Subject Foundation of China (No. 2000053303).

网络结构自主权的前因、后果及作用条件研究

网络结构自主权是最初由Burt在结构洞理论基础上所提出的一个用于描述“网络结构空间与行动自由”的概念,但并未像结构洞概念一样在战略和创新领域引起足够重视。基于整合关系内容与关系结构双层面分析视角,本研究尝试将结构自主权引入到关系强度-商业模式创新实现路径中,同时考察环境不确定性对整个路径的调节作用,即构建并验证了一个有调节的中介模型。研究发现:(1)关系强度积极影响网络结构自主权和商业模式创新;(2)网络结构自主权在关系强度与商业模式创新间发挥中介作用;(3)环境不确定性负向调节网络结构自主权与商业模式创新间关系,并对“关系强度-结构自主权-商业模式创新”整条路径产生显著的消极影响。本研究不仅有助于促进对结构自主权概念的认识与关注,推动网络结构自主权研究,也能够为解决“网络行动空间是否影响创新”和“如何实现商业模式创新”等问题提供有益启示。

面向变量不确定性的汽车结构可靠性设计研究

针对汽车结构轻量化设计,提出了计入不确定制造精度的汽车结构可靠性设计方法。首先将主要设计变量视为区间变量,其他设计参数被视为概率值,在此基础上采用区间模型的变换方法和概率模型的解耦策略,建立汽车结构制造精度不确定的可靠性设计方法。通过经典悬臂梁和停车机器人机架的轻量化优实例分析,得到了不同可靠性要求下的最优解和最大允许偏差范围,验证了本研究方法的有效性。优化设计结果表明,在可接受的制造条件下,可以得到最优变量和相应的最大允许偏差范围。通过适当增加设计变量的偏差范围,可以降低对制造精度的要求,从而降低制造成本。

基于序贯设计和高斯过程模型的结构动力不确定性量化方法

将直接基于有限元模型的蒙特卡罗方法用于结构动力不确定性量化较耗时,为此采用高斯过程模型取代耗时的有限元模型,提高不确定性量化的计算效率.提出基于序贯设计和高斯过程模型的结构动力不确定性量化方法,通过样本填充准则迭代,选择最优样本点建立自适应高斯过程模型,提升动力不确定性量化精度.在建立的自适应高斯过程模型框架下,动力特性统计矩的高维积分转化为一维积分,进而进行解析计算.采用2个数学函数来展示自适应高斯模型的拟合过程,高斯过程模型的拟合精度随着迭代次数增加而明显增加.将所提方法应用于柱面网壳的固有频率统计矩计算,计算精度与蒙特卡罗法的结果相当.与传统高斯过程模型对比,所提算法的计算效率优势明显,表明所提方法具有计算精度高和效率高的优势.

基于RBF神经网络的闭链下肢康复机器人自适应补偿控制

在下肢康复机器人的康复训练过程中,模型参数、环境干扰等不确定性因素会影响机器人轨迹跟踪的精度。针对这一问题,提出了一种基于径向基函数(Radial Basis Function,RBF)神经网络的自适应补偿控制,该控制方法能够提高机械系统轨迹跟踪的精确性。首先,设计一款具有4种工作模式、运动稳定的闭链卧式下肢康复机器人结构;然后,利用拉格朗日方法求解动力学名义模型,将康复装置的模型参数以及外界干扰等不确定性因素分离出来,并设计基于RBF神经网络的自适应补偿算法对其进行逼近控制;最后,通过Matlab/Simulink环境对其进行仿真验证,证明了该控制策略的有效性。结果显示,在人体步态曲线轨迹跟踪中,提出的基于RBF神经网络的自适应补偿算法相比传统的模糊比例-积分-微分(Proportional Integral Derivative,PID)控制的方法响应速度快、跟踪效果好,且髋关节和膝关节轨迹跟踪的角度误差峰值分别为0.08°和0.13°,远小于患者下肢在康复运动中的转动角度。设计了单腿样机试验,试验结果表明,采用的RBF补偿自适应控制器能够实现高精度的跟踪结果,也能够满足患者在康复训练中安全性的要求。

考虑城市中央商务区规划阶段负荷不确定性的综合能源系统供能结构研究

在考虑机组最小负载率的基础上建立了各机组的约束条件,分别以经济性和环保性为目标构建了优化模型,研究了负荷不确定性对系统供能结构的影响。从概率的角度对规划阶段建筑群的负荷不确定性进行了描述,并应用场景分析法将已知概率分布的随机问题转化为便于求解计算的确定性问题。考虑负荷不确定性后,系统总的容量增大7%,运行费用增加2%,系统年总投资增加4%。对比经济性与环保性目标优化结果,分析了系统运行和供能结构配置产生差异的原因。