Uncertain state-space (
uss) models are linear
systems with uncertain state-space matrices, uncertain linear dynamics,
or both. Like their numeric (that is, not uncertain) counterpart, the
ss model object, you can build them from
state-space matrices using the
ss command. When one
or more of the state-space matrices contain uncertain elements (also
called uncertain Control Design Blocks), the result is a
uss model object.
Most functions that work on numeric LTI models also work on
uss models. These include model interconnection
functions such as
feedback, and linear analysis functions such as
functions that generate plots, such as
step, plot random samples of the uncertain
model to give you a sense of the distribution of uncertain dynamics.
When you use these commands to return data, however, they operate on the
nominal value of the system only.
In addition, you can use functions such as
perform robustness and worst-case analysis of uncertain systems
uss models. You can also use tuning
functions such as
systune for robust controller
|Uncertain state-space model|
|Create uncertain real parameter|
|Create uncertain matrix|
|Create uncertain complex parameter|
|Create uncertain complex matrix|
|Create uncertain linear time-invariant object|
|Uncertain frequency response data model|
|Generate random uncertain atom objects|
|Generate random uncertain umat objects|
|Generate stable, random uss objects|
|Nominal value of uncertain model|
|Transform actual values to normalized values|
|Convert value for atom in normalized coordinates to corresponding actual value|
|Validity range for uncertain real (ureal) parameters|
|Simplify representation of uncertain object|
|Check whether argument is uncertain class type|
|Decompose uncertain objects into fixed certain and normalized uncertain parts|
|Compute uncertain system bounding given LTI ss array|
|Fit an uncertain model to set of LTI responses|
Uncertain elements are the building blocks for representing systems with uncertainty.
Represent uncertain parameters and unmodeled dynamics in linear time-invariant models.
Represent real-valued system parameters whose values are uncertain.
Represent unknown linear time-invariant dynamics whose only known attributes are bounds on the frequency response.
Represent matrices whose entries include uncertain values.
Represent linear systems with uncertain state-space matrices or uncertain linear dynamics.
Represent complex-valued uncertain parameters.
Represent a dynamic system as uncertain frequency response data.
Represent completely unknown, multivariable, time-varying nonlinear systems.
Interconnect models that include systems with uncertain parameters or dynamics.
Build a closed-loop system with uncertain parameters.
Simplify uncertain models built up from uncertain elements to ensure that the internal representation of the model is minimal.
Access the normalized LFT representation underlying uncertain models.
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.