Adaptive backstepping repetitive learning control design for nonlinear discrete‐time systems with periodic uncertainties

This paper addresses a tracking problem for uncertain nonlinear discrete‐time systems in which the uncertainties, including parametric uncertainty and external disturbance, are periodic with known periodicity. Repetitive learning control (RLC) is an effective tool to deal with periodic unknown components. By using the backstepping procedures, an adaptive RLC law with periodic parameter estimation is designed. The overparameterization problem is overcome by postponing the parameter estimation to the last backstepping step, which could not be easily solved in robust adaptive control. It is shown that the proposed adaptive RLC law without overparameterization can guarantee the perfect tracking and boundedness of the states of the whole closed‐loop systems in presence of periodic uncertainties. In addition, the effectiveness of the developed controller is demonstrated by an implementation example on a single‐link flexible‐joint robot. Copyright © 2014 John Wiley & Sons, Ltd.     

渣油催化临氢热转化钼铁双金属催化剂的研究

Replacement of precious single metal catalysts with cost-effective, highly-dispersed composite catalysts for catalytic hydrothermal conversion of residue holds tremendous promise for the residue upgrading technologies. Organic metals were added to the feed as the oil-soluble precursors, and transformed into the catalytic active phases in this work. Physical properties and structures of the composite catalysts had been investigated by X-ray fluorescence spectroscopy, X-ray photoelectron spectroscopy, X-ray diffraction, scanning electron microscope and transmission electron microscopy. The composite catalysts were found to be highly efficient in the catalytic hydrothermal conversion of both model compound and residue. Increased metal dispersion and synergistic effects of two metals played indispensable roles in such catalytic system. Results showed that under the test conditions in the article, the catalyst had the best catalytic performance when the mass ratio of molybdenum to iron was 1.5.     

An effective coding approach for multiobjective integrated resource selection and operation sequences problem

In this paper, we consider an integrated Resource Selection and Operation Sequences (iRS/OS) problem in Intelligent Manufacturing System (IMS). Several kinds of objectives are taken into account, in which the makespan for orders should be minimized; workloads among machine tools should be balanced; the total transition times between machines in a local plant should also be minimized. To solve this multiobjective iRS/OS model, a new two vectors-based coding approach has been proposed to improve the efficiency by designing a chromosome containing two kinds of information, i.e., operation sequences and machine selection. Using such kind of chromosome, we adapt multistage operation-based Genetic Algorithm (moGA) to find the Pareto optimal solutions. Moreover a special technique called left-shift hillclimber has been used as one kind of local search to improve the efficiency of our algorithm. Finally, the experimental results of several iRS/OS problems indicate that our proposed approach can obtain best solutions. Further more comparing with previous approaches, moGA performs better for finding Pareto solutions. Received: May 2005/Accepted: December 2005     

非线性扩频序列的三项式特性

讨论了三类非线性扩频序列(GMW序列、No序列和Kasami序列)的三项式特性，利用这三类序列的迹表示，证明了GMW序列、No序列和Kasami序列均具有正则三项式对。     

聚合物分散液晶(PMMA:5CB:C60)光折变特性的实验研究

用溶致相分离方法制备了3种不同组分的聚合物分散液晶薄膜(PMMA：5CB：C60)，C60作为敏化剂。利用二波耦合实验测量了不同样品的衍射效率和响应时间等光折变特性，实验发现，液晶含量不同的薄膜光学性质有很大的不同。     

DNA序列比对分析中的统计特征方法

提出了一种新的基于统计的序列特征定义,并以此对进化树进行构建.同时对这一方法进行了分类测试和进化树构建测试,发现所得测试结果与同样是用进化树进行构建的基于两序列比对的传统算法比较,其性能和适用性都有明显改善.     

电沉积法制备铜分散电极

研究了电沉积制备铜分散电极的方法,通过对阴极极化曲线的测试,考察了电流密度及不同冲击电流方式对铜粉粒度的影响。结果表明:适当的电流密度、合适的分散剂、间歇式的电流是得到较细铜粉的条件。同时对铜分散电极的电化学性质进行初步测试,结果表明铜粉细化以后,铜分散差电池的电动势值有所上升。     

Influence of nano-scaled dispersed second phase on substructure of deformed dispersion strengthened copper alloy

The deformation behavior of dispersion strengthened copper alloy Cu-Al2O3 was studied by TEM. The results show that nano-scaled dispersed second phase not only increases dislocation density in matrix, but also has an important influence on the dislocation substructure. The presence of fine dispersed Al2 O3 particles results in a uniform and random dislocation distribution in matrix copper and causes the difficulty in formation of dislocation cell structure and the decrease in the amount of cell structure during deformation. Deformation gives rise to much more dislocations and dislocation cells form more difficultly and the decrease in the cell size with the increase of dispersion degree.     

A machine learning system for identifying transmembrane domains from amino acid sequences

We present our machine learning system, that uses inductive logic programming techniques to learn how to identify transmembrane domains from amino acid sequences. Our system facilitates the use of operators such as ‘contains’, that act on entire sequences, rather than on individual elements of a sequence. The prediction accuracy of our new system is around 93%, and this compares favourably with earlier results. This work was carried out with the support of a research grant from ISIS, Fujitsu Laboratories.