A robust design of time-varying internal model principle-based control for ultra-precision tracking in a direct-drive servo stage

This paper proposes a robust design of the time-varying internal model principle-based control (TV-IMPC) for tracking sophisticated references generated by linear time-varying (LTV) autonomous systems. The existing TV-IMPC design usually requires a complete knowledge of the plant I/O (input/output) model, leading to the lack of structural robustness. To tackle this issue, we, in the paper, design a gray-box extended state observer (ESO) to estimate and compensate unknown model uncertainties and external disturbances. By means of the ESO feedback, the plant model is kept as nominal, and hence the structural robustness is achieved for the time-varying internal model. It is shown that the proposed design has bounded ESO estimation errors, which can be further adjusted by modifying the corresponding control gains. To stabilize the ESO-based TV-IMPC, a time-varying stabilizer is developed by employing Linear Matrix Inequalities (LMIs). Extensive simulation and experimental studies are conducted on a direct-drive servo stage to validate the proposed robust TV-IMPC with ultra-precision tracking performance ($\sim 60$nm RMSE out of $\pm80$mm stroke)

Integrated Application of Active Controls (IAAC) technology to an advanced subsonic transport project: Current and advanced act control system definition study. Volume 2: Appendices

The current status of the Active Controls Technology (ACT) for the advanced subsonic transport project is investigated through analysis of the systems technical data. Control systems technologies under examination include computerized reliability analysis, pitch axis fly by wire actuator, flaperon actuation system design trade study, control law synthesis and analysis, flutter mode control and gust load alleviation analysis, and implementation of alternative ACT systems. Extensive analysis of the computer techniques involved in each system is included

Numerical Simulations

This book will interest researchers, scientists, engineers and graduate students in many disciplines, who make use of mathematical modeling and computer simulation. Although it represents only a small sample of the research activity on numerical simulations, the book will certainly serve as a valuable tool for researchers interested in getting involved in this multidisciplinary field. It will be useful to encourage further experimental and theoretical researches in the above mentioned areas of numerical simulation

風力発電とエネルギー貯蔵システムを用いたマイクログリッドの運用制御方式の開発

京都大学0048新制・課程博士博士(工学)甲第16087号工博第3410号新制||工||1514(附属図書館)28666京都大学大学院工学研究科電気工学専攻(主査)教授 引原 隆士, 教授 萩原 朋道, 講師 山本 修学位規則第4条第1項該当Doctor of Philosophy (Engineering)Kyoto UniversityDFA

6th International Probabilistic Workshop - 32. Darmstädter Massivbauseminar: 26-27 November 2008 ; Darmstadt, Germany 2008 ; Technische Universität Darmstadt

These are the proceedings of the 6th International Probabilistic Workshop, formerly known as Dresden Probabilistic Symposium or International Probabilistic Symposium. The workshop was held twice in Dresden, then it moved to Vienna, Berlin, Ghent and finally to Darmstadt in 2008. All of the conference cities feature some specialities. However, Darmstadt features a very special property: The element number 110 was named Darmstadtium after Darmstadt: There are only very few cities worldwide after which a chemical element is named. The high element number 110 of Darmstadtium indicates, that much research is still required and carried out. This is also true for the issue of probabilistic safety concepts in engineering. Although the history of probabilistic safety concepts can be traced back nearly 90 years, for the practical applications a long way to go still remains. This is not a disadvantage. Just as research chemists strive to discover new element properties, with the application of new probabilistic techniques we may advance the properties of structures substantially. (Auszug aus Vorwort

Aeronautical engineering: A cumulative index to a continuing bibliography

This bibliography is a cumulative index to the abstracts contained in NASA SP-7037(210) through NASA SP-7037(221) of Aeronautical Engineering: A Continuing Bibliography. NASA SP-7037 and its supplements have been compiled through the cooperative efforts of the American Institute of Aeronautics and Astronautics (AIAA) and the National Aeronautics and Space Administration (NASA). This cumulative index includes subject, personal author, corporate source, foreign technology, contract number, report number, and accession number indexes

Degradation Modeling and Remaining Useful Life Estimation: From Statistical Signal Processing to Deep Learning Models

Aging critical infrastructures and valuable machineries together with recent catastrophic incidents such as the collapse of Morandi bridge, or the Gulf of Mexico oil spill disaster, call for an urgent quest to design advanced and innovative prognostic solutions, and efficiently incorporate multi-sensor streaming data sources for industrial development. Prognostic health management (PHM) is among the most critical disciplines that employs the advancement of the great interdependency between signal processing and machine learning techniques to form a key enabling technology to cope with maintenance development tasks of complex industrial and safety-critical systems. Recent advancements in predictive analytics have empowered the PHM paradigm to move from the traditional condition-based monitoring solutions and preventive maintenance programs to predictive maintenance to provide an early warning of failure, in several domains ranging from manufacturing and industrial systems to transportation and aerospace. The focus of the PHM is centered on two core dimensions; the first is taking into account the behavior and the evolution over time of a fault once it occurs, while the second one aims at estimating/predicting the remaining useful life (RUL) during which a device can perform its intended function. The first dimension is the degradation that is usually determined by a degradation model derived from measurements of critical parameters of relevance to the system. Developing an accurate model for the degradation process is a primary objective in prognosis and health management. Extensive research has been conducted to develop new theories and methodologies for degradation modeling and to accurately capture the degradation dynamics of a system. However, a unified degradation framework has yet not been developed due to: (i) structural uncertainties in the state dynamics of the system and (ii) the complex nature of the degradation process that is often non-linear and difficult to model statistically. Thus even for a single system, there is no consensus on the best degradation model. In this regard, this thesis tries to bridge this gap by proposing a general model that able to model the true degradation path without having any prior knowledge of the true degradation model of the system. Modeling and analysis of degradation behavior lead us to RUL estimation, which is the second dimension of the PHM and the second part of the thesis. The RUL is the main pillar of preventive maintenance, which is the time a machine is expected to work before requiring repair or replacement. Effective and accurate RUL estimation can avoid catastrophic failures, maximize operational availability, and consequently reduce maintenance costs. The RUL estimation is, therefore, of paramount importance and has gained significant attention for its importance to improve systems health management in complex fields including automotive, nuclear, chemical, and aerospace industries to name but a few. A vast number of researches related to different approaches to the concept of remaining useful life have been proposed, and they can be divided into three broad categories: (i) Physics-based; (ii) Data-driven, and; (iii) Hybrid approaches (multiple-model). Each category has its own limitations and issues, such as, hardly adapt to different prognostic applications, in the first one, and accuracy degradation issues, in the second one, because of the deviation of the learned models from the real behavior of the system. In addition to hardly sustain good generalization. Our thesis belongs to the third category, as it is the most promising category, in particular, the new hybrid models, on basis of two different architectures of deep neural networks, which have great potentials to tackle complex prognostic issues associated with systems with complex and unknown degradation processes