Novel model reference adaptive control architecture using semi‐initial excitation‐based switched parameter estimator

This paper is a generalization of the recently developed techniques of initial excitation (IE)–based adaptive control with an introduction to the definition of semi‐initial excitation (semi‐IE), a still more relaxed notion than IE. Classical adaptive controllers typically ensure Lyapunov stability of the extended error dynamics (tracking error + parameter estimation error) and asymptotic tracking, while requiring a stringent condition of persistence of excitation (PE) for parameter convergence. Of late, the authors have proposed a new adaptive control architecture, which guarantees parameter convergence under the online‐verifiable IE condition leading to exponential stability of the extended error dynamics. In earlier works, it has been established that the IE condition is significantly milder than the classical PE condition. The current work further slackens the excitation condition by proposing the concept of semi‐IE. The proposed adaptive controller is proved to ensure convergence of the parameter estimation error to a lower‐dimensional manifold under the weaker semi‐IE condition, while the stronger condition of IE guarantees convergence of the parameter estimation error to zero. The designed algorithm is shown to improve transient response of tracking error sufficiently in contrast to conventional adaptive controllers.     

Robust method to provide exponential convergence of model parameters solving linear time-invariant plant identification problem

The scope of this research is a problem of parameters identification of a linear time-invariant plant, which (1) input signal is not frequency-rich, (2) is subjected to initial conditions and external disturbances. The memory regressor extension (MRE) scheme, in which a specially derived differential equation is used as a filter, is applied to solve the above-stated problem. Such a filter allows us to obtain a bounded regressor value, for which a condition of the initial excitation (IE) is met. Using the MRE scheme, the recursive least-squares method with the forgetting factor is used to derive an adaptation law. The following properties have been proved for the proposed approach. If the IE condition is met, then: (1) the parameter error of identification is bounded and converges to zero exponentially (if there are no external disturbances) or to a set (in the case of them) with an adjustable rate, (2) the parameters adaptation rate is a finite value. The above-mentioned properties are mathematically proved and demonstrated via simulation experiments.     

Homotopy continuation enhanced branch and bound algorithms for strongly nonconvex mixed-integer nonlinear optimization

Large-scale strongly nonlinear and nonconvex mixed-integer nonlinear programming (MINLP) models frequently appear in optimization-based process synthesis, integration, intensification, and process control. However, they are usually difficult to solve by existing algorithms within acceptable time. In this study, we propose two robust homotopy continuation enhanced branch and bound (HCBB) algorithms (denoted as HCBB-FP and HCBB-RB) where the homotopy continuation method is employed to gradually approach the optimum of the NLP subproblem at a node from the solution at its parent node. A variable step length is adapted to effectively balance feasibility and computational efficiency. The computational results from solving four existing process synthesis problems demonstrate that the proposed HCBB algorithms can find the same optimal solution from different initial points, while the existing MINLP algorithms fail or find much worse solutions. In addition, HCBB-RB is superior to HCBB-FP due to much lower computational effort required for the same locally optimal solution.     

Toughness enhancement and equivalent initial fracture toughness of cementitious composite reinforced with aligned steel fibres

Based on theoretical analysis and numerical simulation, the impact of steel fibres on the stress intensity factor (SIF) at the crack tip for cementitious composite was studied. The enhanced toughness of steel fibre reinforced cementitious composite (SFRC) in resisting cracks was explained by the decrement of SIF caused by steel fibre inclusions at the crack tip of the composite. The equivalent initial fracture toughness was used to characterize the crack initiation of SFRC. A simplified method for determining the of SFRC was proposed based on a linear regression method. Fracture tests were conducted on three‐point bending notched beams with different steel fibre volume fractions and specimen sizes to study the crack initiation behaviour of aligned steel fibre reinforced cementitious composite (ASFRC). of ASFRC was calculated, and the size effect of was analysed. The results showed that slightly increased with the steel fibre volume fraction and gradually became stable. For the tested specimens, whose heights varied between 40 and 100 mm, the specimen size had little impact on the .     

基于分支定界法的相控阵雷达事件调度算法

为解决相控阵雷达事件调度问题中调度成功率、时间偏移率与算法时效性三者之间的矛盾，本文提出一种基于分支定界法的调度算法；该算法首先在现有调度结果上调取所有可执行事件，即获得若干"节点"，多步递推后由多"层"节点形成若干条备选"分支"，扩大解空间范围，提高算法性能；然后通过"删减"操作删减掉收益较低分支，将解空间控制在一适当范围内，减小搜索盲目性，降低算法计算量.仿真实验表明，与基于综合优先级算法的调度结果相比，利用本文算法调度时调度成功率提升了52%；与基于时间指针算法的调度结果相比，本身算法时间偏移率降低了61%；与基于遗传算法的调度结果相比，本文算法调度耗时仅为前者1~2%.     

海上稠油油田蒸汽吞吐产量确定新方法

针对目前蒸汽吞吐产量预测模型假设条件简单、普适性差等问题,一般采用测试法和类比法综合确定海上稠油油田蒸汽吞吐初期产量。由于目前海上油田通常只开展常规测试,无法直接获得热采开发初期产量。笔者提出海上稠油油田蒸汽吞吐初期产量确定新方法,建立蒸汽吞吐相对于常规开发的初期产量倍数预测模型,通过蒸汽吞吐产量倍数,将常规测试确定的产量转化为蒸汽吞吐产量。研究表明,蒸汽吞吐初期产量倍数主要受储集层渗透率、原油黏度、注入强度、蒸汽干度等因素影响,利用正交试验设计和多元回归等方法,建立海上稠油油田蒸汽吞吐初期产量倍数与油藏地质参数及注入参数之间的非线性预测模型,该模型经实际生产数据验证,预测误差小于5%,可靠性高,能够为海上稠油油田蒸汽吞吐初期产量的确定提供依据。     

初始制动时预测湿式制动器热对流特性的积分方法

湿式制动器对重型车辆安全具有关键影响，以湿式制动器摩擦副间隙的冷却液压油（Automotive transmission fluid，ATF）为研究对象，考虑车辆初始制动阶段流体的黏性摩擦和层流流动特征，利用积分方法建立了摩擦副流体的非稳态能量方程，并获得了能同时满足轴向和径向边界条件的三维温度和热流密度显式解析表达式，通过简化动量方程和多项式分布假设也获得了径向速度和压力的理论解，与以往试验对比表明，压力和温度解析解与试验结果具有较好的一致性，理论模型有望推广用于液黏离合器等其他HVD装置的ATF速度场、温度场和热流密度场的理论预测。     

基于优化初始聚类中心的K中心点算法

针对K中心点算法的初始聚类中心可能过于临近、代表性不足、稳定性差等问题，提出一种改进的K中心点算法。将样本集间的平均距离与样本间的平均距离的比值作为样本的密度参数，精简了高密度点集合中候选代表点的数量，采用最大距离乘积法选择密度较大且距离较远的K个样本作为初始聚类中心，兼顾聚类中心的代表性和分散性。在UCI数据集上的实验结果表明，与传统K中心点算法和其他2种改进聚类算法相比，新提出的算法不仅聚类结果更加准确，同时也具有更快的收敛速度和更高的稳定性。     

多分量数据全波形反演在气藏检测中的关键问题——以苏里格气田为例

苏里格气田是中国典型的致密砂岩气藏，构造简单、平缓，横向非均质性强，有效储层与围岩声学特征差别小，地震响应不明显，常规地震监测方法预测难度大，但气田含气砂岩泊松比低，是地震气藏检测的有效参数。利用弹性全波形反演精度高和能处理复杂非均质介质的优势，反演地层拉梅常数、剪切模量和密度，并计算泊松比，从而进行气藏预测。重点阐述了苏里格气田多分量数据全波形反演初始模型建模、先验模型建模和地震数据预处理3个关键问题的处理方法。二维三分量数据反演和"甜点"预测结果表明：①对于具有强非均质性的苏里格气田，利用全波形反演获得精度较高的地层弹性参数能显著提高气藏预测的准确度；②苏里格地区构造简单、平缓，利用常规叠加速度并结合构造解释可以建立比较好的初始模型，从而有效地解决了周波跳跃和局部极小的难题；③先验知识的约束和地震数据的预处理是全波形反演成功应用于苏里格气田气藏检测的关键。     

全尾砂最佳絮凝沉降浓度及调控方式研究

针对新疆某铜矿充填溢流跑浑现象严重、充填尾砂利用率低等问题，对影响尾砂沉降速度的主要因素进行了研究，包括尾砂粒级组成、表面形状和进砂浓度等。通过试验探究了进砂质量浓度分别为10%、15%和20%条件下尾砂沉降高度(H)与时间(T)之间的拟合关系式，其相关系数(R ²)均大于0.975，推导出最有利于该铜矿尾砂沉降的进砂浓度值为16%~17%。通过对比进砂浓度为16.5%条件下沉降试验实测值与预测值，证明该方法能够较好地预测尾砂的最佳沉降进砂浓度值。试验研究及分析为现场生产提供了实际指导，通过增加进砂浓度调节设施，并将各设施的相关参数在充填站控制系统中进行数据联锁，使进砂浓度始终保持在16%~17%之间，絮凝剂的添加量与进砂量匹配并维持在最佳添加量范围内，实现了砂仓顶溢流水澄清，提高了充填尾砂的利用率。