首页Identification，Analysis and Control of Dynamic System
1. Systems analysis (stability, controllability, observability), and synthesis of
feedback controllers and state estimators
2. Systems identiﬁcation (=get dynamical models from data)
3. Analysis and control of linear parameter-varying systems
©2018 A. Bemporad - ``Identiﬁcation, Analysis and Control of Dynamical Systems'' 2/144
• A dynamical system is an object (or a set of objects) that evolves over time,
possibly under external excitations.
• Examples: an engine, a satellite, a tank reactor, a human transporter, ...
©2018 A. Bemporad - ``Identiﬁcation, Analysis and Control of Dynamical Systems'' 3/144
• ... a supply chain, a portfolio, a computer server
• The way the system evolves over time is called the dynamics of the system.
©2018 A. Bemporad - ``Identiﬁcation, Analysis and Control of Dynamical Systems'' 4/144
以下是一篇即将投稿Minerals期刊（MDPI出版社）的论文初稿的部分内容，请按照该期刊对论文格式的要求，将以下内容进行压缩凝练（注意：可对内容进行删减，对错误进行修正，对语句顺序进行调整，符合美式英语标准，符合英语母语者语言习惯，句子简明易懂，术语使用准确，保留文章结构、不偏离论文主要内容）： Rocks and ore components directly enter the soil and water system sediments through physical weathering and chemical weathering, and the geochemical anomalies originally present in the rocks further spread with the entry into the soil or directly into the water system, forming soil anomalies and water system sediment anoma-lies.Geochemical anomaly detection is essentially the detection of signal anomalies in geochemical data, which refers to finding out the anomalous distribution of chemical elements themselves and the anomalous distribution of multiple elements in combination through feature extraction and analysis processing of geochemical data in the study area, and reflecting the mineral distribution through the distribution of geochemical ele-ments.Through the method of geochemical anomaly finding, the detected anomalies may contain information indicating specific minerals, which facilitates the rapid tracing of prospective areas and favorable areas for mineralization, identifies possible mineralizing elements and distribution characteristics in the work area, provides basic information for the strategic deployment of mineralization search, and provides good indications for later mineralization search.
A fundamental question of data analysis is how to distinguish noise corrupted deterministic chaotic dynamics from time-(un)correlated stochastic fluctuations when just short length data is available. Despite its importance, direct tests of chaos vs stochasticity in finite time series still lack of a definitive quantification. Here we present a novel approach based on recurrence analysis, a nonlinear approach to deal with data. The main idea is the identification of how recurrence microstates and permutation patterns are affected by time reversibility of data, and how its behavior can be used to distinguish stochastic and deterministic data. We demonstrate the efficiency of the method for a bunch of paradigmatic systems under strong noise influence, as well as for real-world data, covering electronic circuit, sound vocalization and human speeches, neuronal activity, heart beat data, and geomagnetic indexes. Our results support the conclusion that the method distinguishes well deterministic from stochastic fluctuations in simulated and empirical data even under strong noise corruption, finding applications involving various areas of science and technology. In particular, for deterministic signals, the quantification of chaotic behavior may be of fundamental importance because it is believed that chaotic properties of some systems play important functional roles, opening doors to a better understanding and/or control of the physical mechanisms behind the generation of the signals
翻译：Iron–sulfur (Fe–S) clusters are ubiquitous metallocofactors involved in redox chemistry, radical generation and gene regulation. Common methods to monitor Fe–S clusters include spectroscopic analysis of purified proteins and autoradiographic visualization of radiolabeled iron distribution in proteomes. Here, we report a chemoproteomic strategy that monitors changes in the reactivity of Fe–S cysteine ligands to inform on Fe–S cluster occupancy. We highlight the utility of this platform in Escherichia coli by (1) demonstrating global disruptions in Fe–S incorporation in cells cultured under iron-depleted conditions, (2) determining Fe–S client proteins reliant on five scaffold, carrier and chaperone proteins within the Isc Fe–S biogenesis pathway and (3) identifying two previously unannotated Fe–S proteins, TrhP and DppD. In summary, the chemoproteomic strategy described herein is a powerful tool that reports on Fe–S cluster incorporation directly within a native proteome, enabling the interrogation of Fe–S biogenesis pathways and the identification of previously uncharacterized Fe–S proteins.
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