Model-Free Predictive Control for Nonlinear Systems

13301甲第4574号博士（工学）金沢大学博士論文本文Full 以下に掲載予定：Journal of Control, Measurement, and System Integration The Society of Instrument and Control Engineers. 共著者：Hongran LI, Shigeru Yamamot

Model-Free Predictive Control for Nonlinear Systems

13301甲第4574号博士（工学）金沢大学博士論文要旨Abstract 以下に掲載予定：Journal of Control, Measurement, and System Integration The Society of Instrument and Control Engineers. 共著者：Hongran LI, Shigeru Yamamot

A Model-Free Predictive Control Method Based on Polynomial Regression

This paper proposes a model-free predictive control method for nonlinear systems on the basis of polynomial regression. In contrast to conventional model predictive control, model-free predictive control does not require mathematical models. Instead, it uses the previous recorded input/output datasets of the controlled system to predict an optimal control input so as to achieve the desired output. The novel point in this paper is the improvement of existing model-free predictive control by adopting polynomial regression, which is a generalization of the so-called Volterra series expansion of nonlinear functions. © 2016 The Society of Instrument and Control Engineers-SICE.2nd SICE International Symposium on Control Systems, ISCS 2016; Nagoya Campus, Nanzan UniversityNagoya; Japan; 7 March 2016 through 10 March 2016; Category numberCFP16TPH-ART; Code 12164

Genome-Wide Profiling of Cardinium-Responsive MicroRNAs in the Exotic Whitefly, Bemisia tabaci (Gennadius) Biotype Q

Although the bacterial symbiont Cardinium has profound effects on the ecological adaptation of its host, the whitefly Bemisia tabaci (Gennadius) biotype Q (hereafter referred to as B. tabaci Q), the molecular mechanism underlying interactions between these two organisms is not yet fully understood. In this study, sRNA libraries were constructed, amplified, and sequenced for Cardinium-infected (C+) and uninfected (C∗−) B. tabaci Q with identical genetic backgrounds. Subsequently, the genes targeted by the differentially expressed miRNAs were predicted by integrating the B. tabaci Q genome data. A total of 125 known and 100 novel miRNAs were identified, among which 23 significant differentially expressed miRNAs were identified in both libraries. It is noteworthy that an analysis of target genes showed that Cardinium-responsive miRNA-regulated genes were functional in apoptosis, reproduction, development, immune response, thermotolerance and insecticide resistance. GO and KEGG pathway analysis revealed that some miRNA-target genes are closely associated with energy metabolism. A major finding of this study was the identification of several miRNAs that may be involved in physiological processes in response to environmental stress, i.e., insecticides and high temperatures. This information will provide a foundation to help further elucidate the functions of Cardinium in B. tabaci Q

TSC1/2 Signaling Complex Is Essential for Peripheral Naïve CD8+ T Cell Survival and Homeostasis in Mice

The PI3K-Akt-mTOR pathway plays crucial roles in regulating both innate and adaptive immunity. However, the role of TSC1, a critical negative regulator of mTOR, in peripheral T cell homeostasis remains elusive. With T cell-specific Tsc1 conditional knockout (Tsc1 KO) mice, we found that peripheral naïve CD8+ T cells but not CD4+ T cells were severely reduced. Tsc1 KO naïve CD8+ T cells showed profound survival defect in an adoptive transfer model and in culture with either stimulation of IL-7 or IL-15, despite comparable CD122 and CD127 expression between control and KO CD8+ T cells. IL-7 stimulated phosphorylation of Akt(S473) was diminished in Tsc1 KO naïve CD8+T cells due to hyperactive mTOR-mediated feedback suppression on PI3K-AKT signaling. Furthermore, impaired Foxo1/Foxo3a phosphorylation and increased pro-apoptotic Bim expression in Tsc1 KO naïve CD8+T cells were observed upon stimulation of IL-7. Collectively, our study suggests that TSC1 plays an essential role in regulating peripheral naïve CD8+ T cell homeostasis, possible via an mTOR-Akt-FoxO-Bim signaling pathway

Ecological Factors Associated with the Distribution of Bemisia tabaci Cryptic Species and Their Facultative Endosymbionts

The sweetpotato whitefly, Bemisia tabaci species complex, comprises at least 44 morphologically indistinguishable cryptic species, whose endosymbiont infection patterns often varied at the spatial and temporal dimension. However, the effects of ecological factors (e.g., climatic or geographical factors) on the distribution of whitefly and the infection frequencies of their endosymbionts have not been fully elucidated. We, here, analyzed the associations between ecological factors and the distribution of whitefly and their three facultative endosymbionts (Candidatus Cardinium hertigii, Candidatus Hamiltonella defensa, and Rickettsia sp.) by screening 665 individuals collected from 29 geographical localities across China. The study identified eight B. tabaci species via mitochondrial cytochrome oxidase I (mtCOI) gene sequence alignment: two invasive species, MED (66.9%) and MEAM1 (12.2%), and six native cryptic species (20.9%), which differed in distribution patterns, ecological niches, and high suitability areas. The infection frequencies of the three endosymbionts in different cryptic species were distinct and multiple infections were relatively common in B. tabaci MED populations. Furthermore, the annual mean temperature positively affected Cardinium sp. and Rickettsia sp. infection frequencies in B. tabaci MED but negatively affected the quantitative distribution of B. tabaci MED, which indicates that Cardinium sp. and Rickettsia sp. maybe play a crucial role in the thermotolerance of B. tabaci MED, although the host whitefly per se exhibits no resistance to high temperature. Our findings revealed the complex effects of ecological factors on the expansion of the invasive whitefly

Model-Free Predictive Control Using Polynomial Regressors

Model-free predictive control directly computes the control input from massive input/output datasets and does not use a mathematical model. In contrast, conventional model predictive control relies on mathematical models. Although the underlying principle of model-free predictive control utilizes linear regression vectors comprising input/output data, it can also be applied to control nonlinear systems. In this study, the linear regression vectors are extended to polynomial regression vectors, improving the control performance. Using numerical simulations, we demonstrate the effectiveness of this approach