Extended active disturbance rejection controller

Multiple designs, systems, methods and processes for controlling a system or plant using an extended active disturbance rejection control (ADRC) based controller are presented. The extended ADRC controller accepts sensor information from the plant. The sensor information is used in conjunction with an extended state observer in combination with a predictor that estimates and predicts the current state of the plant and a co-joined estimate of the system disturbances and system dynamics. The extended state observer estimates and predictions are used in conjunction with a control law that generates an input to the system based in part on the extended state observer estimates and predictions as well as a desired trajectory for the plant to follow

Extended Active Disturbance Rejection Controller

Multiple designs, systems, methods and processes for controlling a system or plant using an extended active disturbance rejection control (ADRC) based controller are presented. The extended ADRC controller accepts sensor information from the plant. The sensor information is used in conjunction with an extended state observer in combination with a predictor that estimates and predicts the current state of the plant and a co-joined estimate of the system disturbances and system dynamics. The extended state observer estimates and predictions are used in conjunction with a control law that generates an input to the system based in part on the extended state observer estimates and predictions as well as a desired trajectory for the plant to follow

On semi-supervised estimation using exponential tilt mixture models

Consider a semi-supervised setting with a labeled dataset of binary responses and predictors and an unlabeled dataset with only the predictors. Logistic regression is equivalent to an exponential tilt model in the labeled population. For semi-supervised estimation, we develop further analysis and understanding of a statistical approach using exponential tilt mixture (ETM) models and maximum nonparametric likelihood estimation, while allowing that the class proportions may differ between the unlabeled and labeled data. We derive asymptotic properties of ETM-based estimation and demonstrate improved efficiency over supervised logistic regression in a random sampling setup and an outcome-stratified sampling setup previously used. Moreover, we reconcile such efficiency improvement with the existing semiparametric efficiency theory when the class proportions in the unlabeled and labeled data are restricted to be the same. We also provide a simulation study to numerically illustrate our theoretical findings

Study on the Utilization of Aphid Resistant Character in Wild Soybean. I. Aphid Resistant Performance of F2 Generation from Crosses Between Cultivated and Wild Soybeans

Three crosses between 3 cultivat ed (G. max) and 2 aphid resistant wild soybean (G. soja) lines were made to study the inheritance of resistance and segregation patterns in F 2 generation with artificial inoculation technique s. The aphid resistance of F2 plants showed continuous mono-peek distributions which are inclined towards the susceptible parents. Most F2 plants were susceptible or highly susceptible. Only about 7.2 to 10.2% of the F2 plants were resistant to soybean aphid. The segregations in 2 out of 3 crosses studied fitted the hyp othesis that there are 2 independent gene pairs controlling the aphid resistant character of the wild soybean lines (P > 0.50). The segregation of the other cross also tended to fit the 2 major gene model. These results indicated that the aphid resistance of the wild parents might be controlled by 2 independent recessive genes and some other minor genes. Supposing that the aphid resistant character was quantitatively inherited, the overall inheritability estimated from the variances of parents and F 2 populations was between 85.5% and 97.0%. Despite of the high inheritability of aphid resistance, it is recommended that the selection of resistance should be postponed to later generations such as F3 or F4 , because the scarcity of resistant genes may be recessively inherited, and the number of backcrosses made in each cycle of selection should be limited to avoid the resistant genes being lost.Originating text in Chinese.Citation: Sun, Zhiqiang, Tian, Peizhan, Wang, Jian. (1991). Study on the Utilization of Aphid Resistant Character in Wild Soybean. I. Aphid Resistant Performance of F2 Generation from Crosses Between Cultivated and Wild Soybeans. Soybean Science, 10(2), 98-103

Charge trapping and detrapping in polymeric materials: Trapping parameters

Space charge formation in polymeric materials can cause some serious concern for design engineers as the electric field may severely be distorted, leading to part of the material being overstressed. This may result in material degradation and possibly premature failure at the worst. It is therefore important to understand charge generation, trapping, and detrapping processes in the material. Trap depths and density of trapping states in materials are important as they are potentially related to microstructure of the material. Changes in these parameters may reflect the aging taken place in the material. In the present paper, characteristics of charge trapping and detrapping in low density polyethylene (LDPE) under dc electric field have been investigated using the pulsed electroacoustic (PEA) technique. A simple trapping and detrapping model based on two trapping levels has been used to qualitatively explain the observation. Numerical simulation based on the above model has been carried out to extract parameters related to trapping characteristics in the material. It has been found that the space charge decaying during the first few hundred seconds corresponding to the fast changing part of the slope was trapped with the shallow trap depth 0.88 eV, with trap density 1.47 × 1020 m-3 in the sample volume measured. At the same time, the space charge that decays at longer time corresponding to the slower part of the slope was trapped with the deep trap depth 1.01 eV, with its trap density 3.54 × 1018 m-3. The results also indicate that trap depths and density of both shallow and deep traps may be used as aging markers as changes in the material will certainly affect trapping characteristics in terms of trap depth and density

Modeling and Algorithms of the Crew Rostering Problem with Given Cycle on High-Speed Railway Lines

This paper studies the modeling and algorithms of crew roster problem with given cycle on highspeed railway lines. Two feasible compilation strategies for work out the crew rostering plan are discussed, and then an integrated compilation method is proposed in this paper to obtain a plan with relatively higher regularity in execution and lower crew members arranged. The process of plan making is divided into two subproblems which are decomposition of crew legs and adjustment of nonmaximum crew roster scheme. The decomposition subproblem is transformed to finding a Hamilton chain with the best objective function in network which was solved by an improved ant colony algorithm, whereas the adjustment of nonmaximum crew rostering scheme is finally presented as a set covering problem and solved by a two-stage algorithm. The effectiveness of the proposed models and algorithms are testified by a numerical example