盾构机土仓压力自动控制技术

盾构机土仓压力的大小直接影响到掘进工作面的稳定性和地表的变形。根据盾构机土仓渣土的非线性本构关系,建立了土压平衡盾构机土仓压力控制数学模型。采用优化方法,提出了盾构机土仓压力优化控制算法。在实验台上,验证了盾构机土仓压力控制模型和算法的有效性。     

基于推力磁链的永磁直线同步电机自适应观测器

永磁直线同步电机由于结构的特殊性使其直交轴电感不相等,数学模型变得较为复杂,传统的观测器不再适用于直线电机。同时直接推力控制依赖观测器观测结果的准确性,尤其在低速阶段,以线性模型为基础建立的观测器不能很好地适应电机参数变化。根据永磁直线同步电机的数学模型,采用了推力磁链等效的方法,简化了其数学模型,从而解决了交直轴电感不相等引起的推力观测误差。同时,在自适应观测器中引入定子电阻自适应律,减小了低速段由于电机参数变化引起的定子磁链观测误差,提高了直接推力控制的低速段性能。并建立了相关的仿真模型,对定子磁链和电磁推力的观测效果进行了分析,从而验证了基于推力磁链的自适应观测器的有效性。     

双护盾盾构辅助推进液压系统设计

推进系统是盾构掘进机的一个关键部分,承担着整个盾构机掘进的关键性任务,其工作特性直接关系到盾构工程施工的正确性、可靠性和安全性。参考单护盾推进液压系统原理图,并根据双护盾推进液压系统特点,设计了双护盾盾构辅助推进液压系统,并分析和研究了其工作原理及特点。该系统采用推进缸分组控制技术进行盾构掘进方向控制及纠偏,在符合控制要求的前提下,可达到既控制成本,又简化结构的目的。利用SolidWorks软件对集成块进行三维实体建模、装配,并转化为二维CAD工程图,提高了设计效率。     

ARMA建模在神经网络卡钻预测方法中的应用研究

为准确预测卡钻事故的发生,利用一种基于时间序列的神经网络卡钻预测方法,将时间序列ARMA建模与神经网络非线性建模相结合。选取与卡钻事故相关性较大的参数作为神经网络的输入项,运用现场数据对神经网络进行训练,再利用神经网路的强非线性和自适应学习能力来建立卡钻事故预测模型;通过时间序列对历史数据的挖掘功能,揭示实际钻井过程中对卡钻事故影响较大的各参数的隐含规律,建立时序ARMA模型,求出卡钻时刻钻井相关参数的预测值;将预测值放入神经网络模型进行测试训练,从而达到预测卡钻事故的效果。运用延安地区实际现场数据证实该方法具有精确的卡钻预测能力及较好的泛化能力。     

基于模糊逼近的非线性多时延系统的自适应跟踪控制

针对一类多输入多输出非线性多时延系统,提出了基于模糊逼近的自适应跟踪控制方案.该方案构建了基于模糊T-S模型的自适应时延模糊逻辑系统,用来逼近未知非线性时延函数.从而实现了对非线性系统的建模.根据跟踪误差给出了时延模糊逻辑系统的参数自适应律.设计了H_∞补偿器来抵消模糊逼近误差和外部扰动.基于Lyapunov稳定性理论,提出的控制方案保证了闭环系统的稳定性并获得了期望的H_∞跟踪性能.机械臂的仿真结果表明了该方案的有效性.     

多模型自适应控制在空分系统中的应用

多模型自适应控制(MMAC)是解决非线性、变工况参数、不确定性等复杂问题的一种有效方法。作为非线性、变工况参数、动态变化复杂的空分系统来说,运用多模型自适应控制能够很好的解决这些问题。     

隧道围岩反分析与施工数值模拟

结合殷家岩隧道施工,对隧道围岩进行了位移反分析,得到了围岩松动区的弹性模量;应用反分析结果进行数值模拟,预测后续施工步的位移和应力;同时,将预测结果与现场观测进行比较,检验反分析结果。结果表明,利用反分析得到的围岩参数预测后续施工步的位移与现场观测结果比较吻合。     

自动巡航汽车自适应逆控制的建模与仿真

研究神经网络非线性系统的自适应建模和逆建模策略用于非线性的自动巡航系统的控制及可行性。通过对自适应逆控制方法与现行的反馈控制、模糊控制、PID控制进行对比,并在有干扰的情况下系统需要一定的收敛时间,通过运用Matlab软件进行仿真。根据仿真结果分析,当对象输出没有受到干扰时,其在线辨识对象模型和逆模型有十分好的效果;当对象输出存在一些干扰时,由于干扰的存在,需要一段时间来将两个辨识模型收敛。因此,基于动态神经网络的非线性自适应逆控制系统是十分可行的。     

卫星联合定轨的参数化融合模型

 针对基于双星定位系统的近地卫星的多星高精度联合定轨问题,首先建立了不同轨道高度的卫星动力学的物理参数模型与基于稀疏参数表示和时间序列分析的数学模型相结合的轨道动力学高精度表示模型,然后建立了基于测量系统误差参数建模和模型误差非参数分量表示相结合的非线性半参数联合观测模型,在此基础上建立了联合定轨的参数化融合模型,设计了联合定轨参数化融合模型的非线性半参数联合估计算法.理论分析和仿真计算结果表明,卫星轨道动力学模型的高精度表示方法能够进一步提高动力学模型的表示精度,非线性半参数联合观测模型优化建模方法能够进一步细化观测模型,而基于此设计的参数化融合模型的联合估计算法能够使最终的卫星联合定轨精度得到较大程度的改善.     

模型参考自适应控制技术在雷达伺服系统中的应用

针对大型精密机械跟踪雷达测角分系统中的伺服系统,提出了用模型参考自适应技术解决伺服中慢时变的或未知非线性参数的影响。这些非线性参数包括天线刚度,粘滞摩擦,静摩擦等。采用滤波导数取代微分导数以简化模型参考自适应技术的实现。结合工程,给出了线性控制和模型参考自适应控制的仿真结果。仿真结果表明,采用模型参考自适应控制技术的系统优于传统的线性控制。     

激光诱导击穿光谱技术在环境监测中的应用综述

激光诱导击穿光谱技术具有可以实现多种元素同时测量、可以实现原位/在线测量、可以对气体、液体、固体及气溶胶等多种物质进行测量等优点,被用于空气、水、土壤等环境监测的各个领域。针对不同的检测对象,从样品准备、实验设计、数据处理、应用结果等4个方面介绍了近年国内外的研究进展。概述了激光诱导击穿光谱技术在环境监测领域中的应用现状和发展前景。     

Measuring soil moisture with imaging radars

An empirical algorithm for the retrieval of soil moisture content and surface root mean square (RMS) height from remotely sensed radar data was developed using scatterometer data. The algorithm is optimized for bare surfaces and requires two copolarized channels at a frequency between 1.5 and 11 GHz. It gives best results for kh⩽2.5, μ_υ⩽35%, and &thetas;⩾30°. Omitting the usually weaker hv-polarized returns makes the algorithm less sensitive to system cross-talk and system noise, simplifies the calibration process and adds robustness to the algorithm in the presence of vegetation. However, inversion results indicate that significant amounts of vegetation (NDVI>0.4) cause the algorithm to underestimate soil moisture and overestimate RMS height. A simple criteria based on the σ_hv⁰/σ_vv⁰ ratio is developed to select the areas where the inversion is not impaired by the vegetation. The inversion accuracy is assessed on the original scatterometer data sets but also on several SAR data sets by comparing the derived soil moisture values with in-situ measurements collected over a variety of scenes between 1991 and 1994. Both spaceborne (SIR-C) and airborne (AIRSAR) data are used in the test. Over this large sample of conditions, the RMS error in the soil moisture estimate is found to be less than 4.2% soil moisture     

一种裸露土壤参数反演结果的验证方法

该文分析了Oh(1992)以及Dubois(1995)等人提出的两种裸露土壤地表的经验模型和反演算法,提出了在缺少地表实测数据的条件下,这两种经验模型均适用的参数反演结果的验证方法。由于实验证明理论散射模型IEM(积分方程模型)可以在一个很宽的地表粗糙度和含水量范围内准确地模拟后向散射系数与地表参数间的关系,该文深入分析了经验模型的建模思想及其与理论模型的关系之后,将二者有机地结合,并采用AIRSAR数据进行实验。实验结果表明,该方法用于验证经验模型提取的裸露土壤地表参数是有效的。     

全光纤微型土壤水分含量传感器

基于掺杂光纤光热转换效应和光纤光栅测温原理研发了一种全光纤微型土壤水分含量传感器。利用1480 nm泵浦激光对埋设在土壤中的掺钴光纤加热，同时利用刻写在掺钴光纤上的布拉格光栅测定其温度变化，根据加热过程中温度特征值与含水率之间存在的线性关系实现土壤水分含量的原位测量。为了验证该方法的可行性和传感器准确度，搭建了土壤水分含量测试平台并进行实验，实验表明温度特征值与土壤含水率之间存在线性关系；且研发的全光纤微型土壤水分含量传感器所测土壤含水率与烘干法有较好的一致性，最大误差?1.41%，明显优于电导率法。这种新型的土壤水分含量测量方法具有微型化、低功耗的特点。     

In-situ OMVPE process sensing of GaAs and AlGaAs by photoreflectance

The contactless electromodulation method of photoreflectance has been successfully applied as an in-situ sensor of the OMVPE process. The direct band gap,E ₀, of GaAs and AIGaAs has been measured, in-situ, under OMVPE growth conditions. To the best of our knowledge, this is the first report of an in-situ photoreflectance measurement of III-V materials properties in an OMVPE system. This is significant in that it illustrates the potential for the application of photoreflectance as an in-situ process monitor, analogous to the use of RHEED measurements in MBE. The GaAs substrate temperature of 650°, as measured by an optical pyrometer, corresponds to the temperature derived using the Varshni equation and published Varshni coefficients to within the error of the published data.     

In-situ study of the degradation behaviour of GaAs MESFETs for hi-rel applications

In this paper, an accelerated ageing experiment on GaAs MESFET's is presented applying a novel in-situ technique on a reduced time scale. The ageing behaviour of several dc parameters is monitored continuously while thermoelectrical stress is applied. As different ageing processes leave distinct fingerprints in the degradation curves, the increased measurement resolution and data density, which are typical for in-situ measurements, provide additional information compared with conventional ex-situ tests. The result of the numerical analysis of the experimental data is presented, which provide an initial basis for comparison with conventional data and lifetime prediction.     

Soil moisture estimation from ERS/SAR data: toward an operational methodology

Previous studies have shown the possibility of using European Remote Sensing/synthetic aperture radar (ERS/SAR) data to monitor surface soil moisture from space. The linear relationships between soil moisture and the SAR signal have been derived empirically and, thus, were a priori specific to the considered watershed. In order to overcome this limit, this study focused on two objectives. The first one was to validate over two years of data the empirical sensitivity of the radar signal to soil moisture, in the case of three agricultural watersheds with different soil compositions and land cover uses. The slope of the observed relationship was very consistent. Conversely, the offset could change, making the soil moisture retrieval only relative (and not absolute). The second one was to propose an "operational" methodology for soil moisture monitoring based on ERS/SAR data. The implementation of this methodology is based on two steps: the calibration period and the operational period. During the calibration period, ground truth campaigns are performed to measure vegetation parameters (to correct the SAR signal from the vegetation effect), and the ERS/SAR data is processed only once a field land cover map is established. In contrast, during the operational period, no vegetation field campaigns are performed, and the images are processed as soon as they are available. The results confirm the relevance of this operational methodology, since no loss of performance (in soil moisture retrieval) is observed between the calibration and operational periods.     

激光诱导现场探测月壤成分的可行性分析

为了验证激光诱导离解光谱技术在月球环境条件下月壤成分现场探测的可行性，设计了一套实验模拟装置，以合成月壤样品为检测目标，Nd:YAG激光器为光源，线列CCD光栅光谱仪为光谱的采集系统，真空罐模拟月球气压环境。用高纯度Fe2O3,Al2O3作为标准参考光谱样品提高识别精度。实验结果说明在模拟环境下利用激光诱导探测手段能够采集到样品的原子发射光谱，并完全可以定性识别物质中含有的成分。     

Spatial characterization of wafer state using principal componentanalysis of optical emission spectra in plasma etch

Optical emission spectroscopy (OES) is often used to obtain in-situ estimates of process parameters and conditions in plasma etch processes. Two barriers must be overcome to enable the use of such information for real-time process diagnosis and control. The first barrier is the large number of measurements in wide-spectrum scans, which hinders real-time processing. The second barrier is the need to understand and estimate not only process conditions, but also what is happening on the surface of wafer, particularly the spatial uniformity of the etch. This paper presents a diagnostic method that utilizes multivariable OES data collected during plasma etch to estimate spatial asymmetries in commercially available reactor technology. Key elements of this method are: first, the use of principal component analysis (PCA) for dimensionality reduction, and second, regression and function approximation to correlate observed spatial wafer information (i.e., line width reduction) with these reduced measurements. Here we compare principal component regression (PCR), partial least squares (PLS), and principal components combined with multilayer perceptron neural networks (PCA/MLP) for this in-situ estimation of spatial uniformity. This approach has been verified for a 0.35-μm aluminum etch process using a Lam 9600 TCP etcher. Models of metal line width reduction across the wafer are constructed and compared: the root mean square prediction errors on a test set withheld from training are 0.0134 μm for PCR, 0.014 μm for PLS, and 0.016 μm for PCA/MLP. These results demonstrate that in-situ spatially resolved OES in conjunction with principal component analysis and linear or nonlinear function approximation can be effective in predicting important product characteristics across the wafer     

Tests of sequential data assimilation for retrieving profile soilmoisture and temperature from observed L-band radiobrightness

Sequential data assimilation (Kalman filter optimal estimation) techniques are applied to the problem of retrieving near-surface soil moisture and temperature state from periodic terrestrial radiobrightness observations that update soil heat and moisture diffusion models. The retrieval procedure uses a time-explicit numerical model to continuously propagate the soil state profile, its error of estimation, and its interdepth covariances through time. The model's coupled soil moisture and heat fluxes are constrained by micrometeorology boundary conditions drawn from observations or atmospheric modeling. When radiometer data are available, the Kalman filter updates the state profile estimate by weighing the propagated state, error, and covariance estimates against an a priori estimate of radiometric measurement error. The Kalman filter compares predicted and observed radiobrightnesses directly, so no inverse algorithm relating brightness to physical parameters is required. The authors demonstrate Kalman filter model effectiveness using field observations and a simulation study. An observed 1 m soil state profile is recovered over an eight-day period from daily L-band observations following an intentionally poor initial state estimate. In a four-month simulation study, they gauge the longer term behavior of the soil state retrieval and Kalman gain through multiple rain events, soil dry-downs, and updates from radiobrightnesses