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Arreglos de los modelos

Generación de recopilaciones de modelos, como en estudios de parámetros

Los arreglos de los modelos permiten analizar recopilaciones de varios modelos lineales, que se almacenan como elementos en un único arreglo de MATLAB®.

Funciones

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stackBuild model array by stacking models or model arrays along array dimensions
nmodelsNumber of models in model array
permuteRearrange array dimensions in model arrays
reshapeChange shape of model array
repsysReplicate and tile models
voidModelMark missing or irrelevant models in model array
sampleBlockSample Control Design blocks in generalized model
rsampleBlockRandomly sample Control Design blocks in generalized model

Bloques

LPV SystemSimulate Linear Parameter-Varying (LPV) systems

Temas

Conceptos básicos sobre los arreglos de modelos

Model Arrays

Store multiple dynamic system objects in a single MATLAB array for multiple-model design and analysis.

Model Array with Single Parameter Variation

Use the stack command to create a 1-D array of transfer functions with a parameter that varies from model to model.

Model Array with Variations in Two Parameters

Create an array of models over a grid of parameter values, and use the SamplingGrid property to keep track of parameter values across the array.

Study Parameter Variation by Sampling Tunable Model

Sample a parametric model of a second-order filter across a grid of parameter values using sampleBlock.

Select Models from Array

Select individual models or sets of models from a model array using array indexing.

Query Array Size and Characteristics

Query array attributes such as the array dimensions, and query characteristics of the models in the array, such as I/O dimensions and stability.

Sistemas LPV

Using LTI Arrays for Simulating Multi-Mode Dynamics

This example shows how to construct a Linear Parameter Varying (LPV) representation of a system that exhibits multi-mode dynamics.

Linear Parameter-Varying Models

An LPV system is a linear state-space model whose dynamics vary as a function of time-varying parameters. Represent an LPV model in a state-space form using parameter-dependent coefficients.