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Uncertain Models

Uncertain state-space and frequency response models


ureal Create uncertain real parameter
umat Create uncertain matrix
ucomplex Create uncertain complex parameter
ucomplexm Create uncertain complex matrix
ultidyn Create uncertain linear time-invariant object
uss Specify uncertain state-space models or convert LTI model to uncertain state-space model
ufrd Uncertain frequency response data model
randatom Generate random uncertain atom objects
randumat Generate random uncertain umat objects
randuss Generate stable, random uss objects
getNominal Nominal value of uncertain model
actual2normalized Transform actual values to normalized values
normalized2actual Convert value for atom in normalized coordinates to corresponding actual value
simplify Simplify representation of uncertain object
isuncertain Check whether argument is uncertain class type
lftdata Decompose uncertain objects into fixed certain and normalized uncertain parts
ltiarray2uss Compute uncertain system bounding given LTI ss array
ucover Fit an uncertain model to set of LTI responses


Uncertain Models

Introduction to Uncertain Elements

Uncertain elements are the building blocks for representing systems with uncertainty.

Uncertain Real Parameters

Represent real-valued system parameters whose values are uncertain.

Uncertain LTI Dynamics Elements

Represent unknown linear time-invariant dynamics whose only known attributes are bounds on the frequency response.

Uncertain Matrices

Represent matrices whose entries include uncertain values.

Uncertain State-Space Models

Represent linear systems with uncertain state-space matrices or uncertain linear dynamics.

Uncertain Complex Parameters and Matrices

Represent complex-valued uncertain parameters.

Create Uncertain Frequency Response Data Models

Represent a dynamic system as uncertain frequency response data.

Systems with Unmodeled Dynamics

Represent completely unknown, multivariable, time-varying nonlinear systems.

Uncertain Model Interconnections

Interconnect models that include systems with uncertain parameters or dynamics.

System with Uncertain Parameters

Build a closed-loop system with uncertain parameters.

Simplifying Representation of Uncertain Objects

Simplify uncertain models built up from uncertain elements to ensure that the internal representation of the model is minimal.

Decomposing Uncertain Objects

Access the normalized LFT representation underlying uncertain models.

Model Object Basics

What Are Model Objects? (Control System Toolbox)

Model objects represent linear systems as specialized data containers that encapsulate model data and attributes in a structured way.

Types of Model Objects (Control System Toolbox)

Model object types include numeric models, for representing systems with fixed coefficients, and generalized models for systems with tunable or uncertain coefficients.

Dynamic System Models (Control System Toolbox)

Represent systems that have internal dynamics or memory of past states, such as integrators, delays, transfer functions, and state-space models.

Static Models (Control System Toolbox)

Represent static input/output relationships, including tunable or uncertain parameters and arrays.

Generalized Models (Control System Toolbox)

Generalized models represent systems having a mixture of fixed coefficients and tunable or uncertain coefficients.

Control System Modeling with Model Objects (Control System Toolbox)

Model objects can represent components such as the plant, actuators, sensors, or controllers. You connect model objects to build aggregate models that represent the combined response of multiple elements.

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