羟氨苄青霉素引起大疱性类天疱疮样疹

Robust stability and control for systems that combine continuous-time and discrete-time dynamics. This article is a tutorial on modeling the dynamics of hybrid systems, on the elements of stability theory for hybrid systems, and on the basics of hybrid control. The presentation and selection of material is oriented toward the analysis of asymptotic stability in hybrid systems and the design of stabilizing hybrid controllers. Our emphasis on the robustness of asymptotic stability to data perturbation, external disturbances, and measurement error distinguishes the approach taken here from other approaches to hybrid systems. While we make some connections to alternative approaches, this article does not aspire to be a survey of the hybrid system literature, which is vast and multifaceted.

Hybrid dynamical systems

Underactuated mechanical systems are those possessing fewer actuators than degrees of freedom. Examples of such systems abound, including flexible joint and flexible link robots, space robots, mobile robots, and robot models that include actuator dynamics and rigid body dynamics together. Complex internal dynamics, nonholonomic behavior, and lack of feedback linearizability are often exhibited by such systems, making the class a rich one from a control standpoint. In this article the author studies a particular underactuated system known as the Acrobot: a two-degree-of-freedom planar robot with a single actuator. The author considers the so-called swing up control problem using the method of partial feedback linearization. The author gives conditions under which the response of either degree of freedom may be globally decoupled from the response of the other and linearized. This result can be used as a starting point to design swing up control algorithms. Analysis of the resulting zero dynamics as well as analysis of the energy of the system provides an understanding of the swing up algorithms. Simulation results are presented showing the swing up motion resulting from partial feedback linearization designs. >

The swing up control problem for the Acrobot

During the last decade, a big progress has been achieved in the analysis and numerical treatment of Initial Value Problems (IVPs) in Differential Algebraic Equations (DAEs) and Ordinary Differential Equations (ODEs). In spite of the rich variety of results available in the literature, there are still many specific problems that require special attention. Two of such, which are considered in this work, are the optimization of order of accuracy and reduction of cost of functional evaluations of Explicit Runge - Kutta (ERK) methods.

Traditionally, the maximum attainable order p of an s-stage ERK method for advancing the solution of an IVP is such that
p(s)
 4
 
In 1999, Goeken presented an s-stage ERK Method of order p(s)=s +1,s>2.
However, this work focuses on the design of a new approximation algorithm that reduces the cost of functional evaluations and yet increases the attainable order higher 
U n and Jonhson [94]; and the classical ERK methods. The order p of 
the new scheme called Multiderivative Explicit Runge-Kutta (MERK) Methods is such that p(s) 2. The stability, convergence and implementation for the optimization of IVPs in DAEs and ODEs systems are also considered.

Numerical solution of initial value problems in differential - algebraic equations

Digital twin can be defined as a virtual representation of a physical asset enabled through data and simulators for real-time prediction, optimization, monitoring, controlling, and improved decision making. Recent advances in computational pipelines, multiphysics solvers, artificial intelligence, big data cybernetics, data processing and management tools bring the promise of digital twins and their impact on society closer to reality. Digital twinning is now an important and emerging trend in many applications. Also referred to as a computational megamodel, device shadow, mirrored system, avatar or a synchronized virtual prototype, there can be no doubt that a digital twin plays a transformative role not only in how we design and operate cyber-physical intelligent systems, but also in how we advance the modularity of multi-disciplinary systems to tackle fundamental barriers not addressed by the current, evolutionary modeling practices. In this work, we review the recent status of methodologies and techniques related to the construction of digital twins mostly from a modeling perspective. Our aim is to provide a detailed coverage of the current challenges and enabling technologies along with recommendations and reflections for various stakeholders.

/pdf/digital-twin-values-challenges-and-enablers-from-a-modeling-3hpoelaogn.pdf

Digital Twin: Values, Challenges and Enablers From a Modeling Perspective

The Functional Mockup Interface (FMI) is a tool independent standard for the exchange of dynamic models and for co-simulation. The development of FMI was initiated and organized by Daimler AG within the ITEA2 project MODELISAR. The primary goal is to support the exchange of simulation models between suppliers and OEMs even if a large variety of different tools are used. The FMI was developed in a close collaboration between simulation tool vendors and research institutes. In this article an overview about FMI is given and technical details about the solution are discussed.

https://www.ep.liu.se/ecp/063/013/ecp11063013.pdf

The Functional Mockup Interface for Tool independent Exchange of Simulation Models

The Functional Mockup Interface (FMI) is a tool independent standard for the exchange of dynamic models and for co simulation. The first version, FMI 1.0, was published in 2010. Already more than 30 tools support FMI 1.0. In this paper an overview about the recently published version 2.0 of FMI is given that combines the formerly separated interfaces for Model Exchange and Co-Simulation in one standard. Based on the experience on using FMI 1.0, many small details have been improved and new features ease the usability and increase the performance especially for larger models. Additionally, a free FMI compliance checker will become soon available and FMI models from different tools are made available on the web to simplify testing.

/pdf/functional-mockup-interface-2-0-the-standard-for-tool-4cimolga2r.pdf

Functional Mockup Interface 2.0: The Standard for Tool independent Exchange of Simulation Models

Physical system modeling with Modelica

https://www.modelica.org/documents/Modelica1.pdf

Modelica — A unified object-oriented language for physical systems modeling

A model language, called DYMOLA, for continuouo dynamical systems is presented. Large modele &re conveniently described hiersrchically using ¿ submodcl concept. The. ordinary differen[ial equations ¿nd algebraic equa-tions of thc modcl nced noü be converted to assignmint staüemcnts. There is a concept, cut, which corresponds to connection mechanisms of complex üype, an{ tþere arå facilities to conveniently desmibe the connection structure of a-rnodel. A model can bc manipulated for differenü pu¡poses such as simulaüion or static design calculations. The model equations ¿re sorted ond ühey are converüed üo assigirment statements using formula manipulaüion. W A common principle for solving large problems is decomposiúion of ü¡e problem into a set of smaller subproblems which are eiüher solved directly or decomposed further. The origínal problem is then solved by combining the äubproblem soluüions. This principle is used when modelling large systems. The system is considered qs a-se-ü of subsystems. This decomposition is often inherent in ühephysical system. An industrial plant, for example, is in facü designed according üo the decompäeiüion principle. The language to describe the model should refleiü thie ond encourage the use of submodels. The languagce of CSSL-üype have a MACRO-concept whiãh corresponds to submodele. A modcl must aiso contain a description of how submodele interact wiùh each other. The introduction of submodels are often done in s $'sy ühat the interactions between submodeiõ &re r¿üher limited. The interaction ie ofüen restricted üo a set of conncction mechanisms. Such connecüion mechanisms often correspond to 6ome physical devices such as shaf{,s, pipes,electrical wires etc. In the languages of ClSL-üype there ôre no constructs corresponding üo such connection mechanisme, The connections are done by meane of vari¿blis. Each

/pdf/dymola-a-structured-model-language-for-large-continuous-4ntbytc65n.pdf

Hilding Elmqvist

Papers

The Functional Mockup Interface for Tool independent Exchange of Simulation Models

Functional Mockup Interface 2.0: The Standard for Tool independent Exchange of Simulation Models

Physical system modeling with Modelica

Modelica — A unified object-oriented language for physical systems modeling

DYMOLA - A Structured Model Language for Large Continuous Systems