Simulation Of Continuous-time Systems Research Articles

Mixed-mode state–time discretization in ODE numerical integration

This article introduces the joint usage of Quantized State System (QSS) methods and classic numerical integration algorithms in the simulation of continuous time systems described by systems of Ordinary Differential Equations (ODEs). The proposed mixed-mode scheme consists of splitting an ODE, using QSS algorithms where they perform better than classic algorithms (i.e., in presence of frequent discontinuities or stiffness under certain particular sparse structures) and using classic algorithms where they are a better choice.Besides describing the methodology – where the key issue is the interface between both algorithms – the article studies some properties of the resulting scheme including convergence and numerical stability.In addition, the performance of the proposed mixed-mode algorithm is analyzed in the simulation of two large systems with heterogeneous dynamics, showing an important reduction of the simulation times – more than one order of magnitude – compared to the most efficient QSS and classic approaches.

Limiting sampling results for continuous-time ARMA systems

The objective of this paper is to present some general properties of discrete-time systems originating from fast sampled continuous-time autoregressive moving average (CARMA) systems. In particular, some results concerning the zero locations and the innovations variance of fast sampled CARMA systems will be stated. Knowledge of these properties is of importance in various fast sampling applications, such as discrete-time simulation of continuous-time systems and identification of continuous-time systems using discrete-time measurements. The main contribution, however, is to provide a mean to evaluate limiting properties for various problems. For example, how to determine the accuracy of approximate sampling schemes, how to describe the characteristics of different estimators, and how to examine the behaviour of the dynamics of a fast sampled system. The results are illustrated by an extensive set of examples.

A note on windowing in the simulation of continuous-time communication systems

Simulations of continuous-time systems are frequently used by designers of signal processing and communication systems. Windowed finite-impulse response models are often used in these simulations to model continuous-time linear filters. We investigate the performance of some common windows with respect to waveform fidelity, which is a primary goal in waveform simulation, and we also obtain the form of optimum windows for this criterion. Our results indicate that the rectangular window is generally a practical and reasonably good choice for waveform simulation.

Petri net based qualitative simulation with quantitative information

Abstract This paper deals with qualitative modeling and simulation of continuous-time systems based on Petri nets. Qualitative simulation faces an intrinsic problem of scale. As the number of variables increases, the number of possible qualitative behaviors may grow exponentially in the worst case. We extend conventional Petri net and propose a method for qualitative simulation with time and quantity, and show that the proposed method can predict system behaviors precisely.

Impulse invariance and multiple-order poles

The impulse-invariant method of IIR digital filter design from a given analog filter is useful both in filter design and especially in discrete-time simulation of continuous-time systems. The article clarifies aspects of impulse invariance and, in particular, the handling of multiple-order poles. The user is currently faced with incorrect formulas and Matlab software. The problems are discussed, and a method valid for any order poles is presented via a numerical example using original Matlab code.

Simul, A New Continuous Systems Simulation Language For Minicomputers

SIMUL is an interactive equation-oriented language for simulation of continuous-time systems on minicomputers; it has designed for a rmximum case of programming and use, allowing the user to write ...

PDEL-ID

Mathematical models have been commonly used for the simulation of continuous time systems on a digital computer. In many cases, the system models involve parameters which have to be identified from observed data. The digital simulation language PDEL was extended to provide the facilities and capabilities for the identification of parameters in distributed parameter systems. The extended program is designated as PDEL-ID. The extension includes new language statements and convenient facilities for observed data input and parameter identification. The use of PDEL-ID to identify parameters in partial differential equations requires minimum amount of programming effort and little knowledge on numerical analysis and mathematical programming. The syntax and semantics of PDEL-ID are given. Major facilities and capabilities of the language are described and illustrated by an example.