Uncertain Models

Uncertain state-space and frequency response models

Uncertain state-space (uss) models are linear systems with uncertain state-space matrices, uncertain linear dynamics, or both. Most functions that work on numeric LTI models also work on uss models. These include model interconnection functions such as connect and feedback, and linear analysis functions such as bode and stepinfo. Some functions that generate plots, such as bode and step, plot random samples of the uncertain model to give you a sense of the distribution of uncertain dynamics.

In addition, you can use functions such as robstab and wcgain to perform robustness and worst-case analysis of uncertain systems represented by uss models. You can also use tuning functions such as systune for robust controller tuning.

Functions

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ussUncertain state-space model
urealCreate uncertain real parameter
ucomplexCreate uncertain complex parameter
ucomplexmCreate uncertain complex matrix
ultidynCreate uncertain linear time-invariant object
umarginModel gain and phase uncertainty
umatCreate uncertain matrix
ufrdUncertain frequency response data model
randatomGenerate random uncertain atom objects
randumatGenerate random uncertain umat objects
randussGenerate stable, random uss objects
ucoverFit an uncertain model to set of LTI responses
getNominalNominal value of uncertain model
uscaleScale uncertainty of block or system
plot (umargin)Visualize gain and phase uncertainty of a umargin block
actual2normalizedTransform actual values to normalized values
normalized2actualConvert value for atom in normalized coordinates to corresponding actual value
getLimitsValidity range for uncertain real (ureal) parameters
simplifySimplify representation of uncertain object
isuncertainCheck whether argument is uncertain class type
lftdataDecompose uncertain objects into fixed certain and normalized uncertain parts
ltiarray2ussCompute uncertain system bounding given LTI ss array

Topics

Uncertain Models

Introduction to Uncertain Elements

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

Create Models of Uncertain Systems

Represent uncertain parameters and unmodeled dynamics in linear time-invariant models.

Uncertain Model Interconnections

Interconnect models that include systems with uncertain parameters or dynamics.

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.

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.

Featured Examples

Building and Manipulating Uncertain Models

Building and Manipulating Uncertain Models

Use Robust Control Toolbox™ to build uncertain state-space models and analyze the robustness of feedback control systems with uncertain elements.

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Model Gain and Phase Uncertainty in Feedback Loops

Model Gain and Phase Uncertainty in Feedback Loops

Model gain and phase uncertainty in feedback loops and check for robust stability against such uncertainty.

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Modeling a Family of Responses as an Uncertain System

Modeling a Family of Responses as an Uncertain System

Use the Robust Control Toolbox™ command ucover to model a family of LTI responses as an uncertain system. This command is useful to fit an uncertain model to a set of frequency responses representative of the system variability, or to reduce the complexity of an existing uncertain model to facilitate the synthesis of robust controllers with musyn.

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Robust Control Toolbox Documentation
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