基于能量平衡Smith预估器的母管压力控制系统

本文针对母管制火电厂母管压力控制中所存在的实际问题,提出了一种新型的基于能量平衡Smith预估器的优化控制方法,并将该优化控制方法成功地应用于扬子热电厂1~8号锅炉的母管压力控制控制中,取得了十分优良的品质,从而验证了本文的优化控制方法是十分有效的。     

An improved generalized analytical predictor for chemical process control

To improve the regulatory performance of Wellon and Edgar's Generalized Analytical Predictor (WE-GAP), a novel algorithm is developed to predict the load disturbance's effect on the process output through the disturbance response tendency. Meanwhile, a corrective parameter is introduced to improve the predictor precision and the system robustness. The corrective parameter is optimized on-line to adapt to different load disturbances. This improvement removes the assumption of WE-GAP that the disturbance transfer function should be known exactly. Additionally, the disturbance predictor can also be obtained even though the disturbance transfer function order is higher than second-order. Simulation results indicate that the proposed predictive scheme is able to improve the properties of WE-GAP and shows good control performance for chemical process with large dead time.     

An Interior Method for Nonconvex Semidefinite Programs

In several applications, semidefinite programs arise in which the matrix depends nonlinearly on the unknown variables. We propose a new solution method for such semidefinite programs that also applies to other smooth nonconvex programs. The method is an extension of a primal predictor corrector interior method to nonconvex programs. The predictor steps are based on Dikin ellipsoids of a “convexified” domain. The corrector steps are based on quadratic subprograms that combine aspects of line search and trust region methods. Convergence results are given, and some preliminary numerical experiments suggest a high robustness of the proposed method.     

纯滞后系统的单神经元-Smith预估控制

工业过程控制中,纯滞后系统普遍存在,单纯的Smith预估控制的鲁棒性差,单神经元具有自学习、自适应能力,将单神经元与Smith二者有机结合,形成单神经元—Smith复合控制器,并利用MATLAB/Simulink对其进行仿真研究。仿真结果表明,这种控制器充分发挥了神经元自适应、自学习的功能,弥补了单纯的Smith控制器在处理纯滞后问题中的不足,具有良好的抗干扰能力和适应性。     

基于非线性预测的改进连续潮流算法研究

阐述了一种改进的连续潮流法。该方法预测过程中采用了非线性预测,在功率极限点附近不需要减小步长,校正过程中采用局部参数法求解校正方程,从而减少了绘制PV曲线的时间;潮流计算采用牛拉法,保证了PV曲线绘制过程中的稳定性。通过对IEEE 39和IEEE 118节点测试系统进行仿真计算,取得了良好的计算结果,从而验证了本算法的有效性和快速性。     

基于LS-SVM的网络延时单神经元Smith控制

提出了一种Smith预估器和单神经元PID控制器的时变网络时延补偿策略。将网络时延和被控对象作为广义对象,运用最小二乘支持向量机对网络时延进行实时预测,得到广义对象预测模型。先设计Smith预估器,初步实现了网络控制系统的性能改善。然后将Smith预估控制与单神经元PID控制相结合,提出一种基于时延辨识的单神经元Smith控制策略。仿真表明,该策略能有效提高系统性能,对模型失配情况具有较高的鲁棒性和适应性。     

非最小相位系统的复杂控制算法研究

针对非最小相位系统右半平面零点所造成的负调问题,文中以抑制非最小相位系统的负调和时滞问题为目的,将两步PID参数整定法与Smith预估控制器相融合,提出了一种复杂控制算法。该算法通过调整系统前置系数和PID参数,达到抑制非最小相位负调的作用,并将其作用于Smith预估控制结构中,达到提升系统响应时间的目的。利用MATLAB软件中的Simulink对典型二阶控制系统建立仿真模型。仿真结果表明,该复杂控制算法在消除系统负调的同时也提升了系统的响应时间,验证了该复杂控制算法的有效性和鲁棒性。     

基于单站单波段的红外被动测距

针对单站单波段红外被动测距问题,对红外被动测距原理及其实现进行了论述;对影响单站单波段被动测距精度的因素进行了分析,提出了一种基于线性预测的被动测距方法.该方法能对测距中出现的奇异值进行有效的抑制,从而降低测距误差.利用外场实测中、长波红外图像对该算法进行了验证,并对测距结果和误差进行了详细分析.     

基于量测一致性的联邦滤波故障检测方法研究

针对联邦滤波器的信息融合特点,提出了一种基于测量一致性的传感器故障检验方法.该方法利用融合状态递推器对各个子传感器的量测预报与各传感器量测之间的偏差构造统计量,进行故障检测,可以实现先检测后滤波,在子滤波器启动之前确定对应传感器量测的有效性.仿真结果表明,该方法具有较高的故障检测灵敏度.     

Hot topic: A unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score

The first national single-step, full-information (phenotype, pedigree, and marker genotype) genetic evaluation was developed for final score of US Holsteins. Data included final scores recorded from 1955 to 2009 for 6,232,548 Holsteins cows. BovineSNP50 (Illumina, San Diego, CA) genotypes from the Cooperative Dairy DNA Repository (Beltsville, MD) were available for 6,508 bulls. Three analyses used a repeatability animal model as currently used for the national US evaluation. The first 2 analyses used final scores recorded up to 2004. The first analysis used only a pedigree-based relationship matrix. The second analysis used a relationship matrix based on both pedigree and genomic information (single-step approach). The third analysis used the complete data set and only the pedigree-based relationship matrix. The fourth analysis used predictions from the first analysis (final scores up to 2004 and only a pedigree-based relationship matrix) and prediction using a genomic based matrix to obtain genetic evaluation (multiple-step approach). Different allele frequencies were tested in construction of the genomic relationship matrix. Coefficients of determination between predictions of young bulls from parent average, single-step, and multiple-step approaches and their 2009 daughter deviations were 0.24, 0.37 to 0.41, and 0.40, respectively. The highest coefficient of determination for a single-step approach was observed when using a genomic relationship matrix with assumed allele frequencies of 0.5. Coefficients for regression of 2009 daughter deviations on parent-average, single-step, and multiple-step predictions were 0.76, 0.68 to 0.79, and 0.86, respectively, which indicated some inflation of predictions. The single-step regression coefficient could be increased up to 0.92 by scaling differences between the genomic and pedigree-based relationship matrices with little loss in accuracy of prediction. One complete evaluation took about 2 h of computing time and 2.7 gigabytes of memory. Computing times for single-step analyses were slightly longer (2%) than for pedigree-based analysis. A national single-step genetic evaluation with the pedigree relationship matrix augmented with genomic information provided genomic predictions with accuracy and bias comparable to multiple-step procedures and could account for any population or data structure. Advantages of single-step evaluations should increase in the future when animals are pre-selected on genotypes.