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Dynamic System Models

Dynamic System Models generally represent systems that have internal dynamics or memory of past states such as integrators, delays, transfer functions, and state-space models.

Most commands for analyzing linear systems, such as bode, margin, and linearSystemAnalyzer, work on most Dynamic System Model objects. For Generalized Models, analysis commands use the current value of tunable parameters and the nominal value of uncertain parameters. Commands that generate response plots display random samples of uncertain models.

The following table lists the Dynamic System Models.

Model FamilyModel Types
Numeric LTI models — Basic numeric representation of linear systemstf
zpk
ss
frd
pid
pidstd
pid2
pidstd2
Sparse State-Space Models — Represent large sparse state-space modelsmechss
sparss
Identified LTI models — Representations of linear systems with tunable coefficients, whose values can be identified using measured input/output data.idtf (System Identification Toolbox)
idss (System Identification Toolbox)
idfrd (System Identification Toolbox)
idgrey (System Identification Toolbox)
idpoly (System Identification Toolbox)
idproc (System Identification Toolbox)
Identified nonlinear models — Representations of nonlinear systems with tunable coefficients, whose values can be identified using input/output data. Limited support for commands that analyze linear systems.idnlarx (System Identification Toolbox)
idnlhw (System Identification Toolbox)
idnlgrey (System Identification Toolbox)
Generalized LTI models — Representations of systems that include tunable or uncertain coefficientsgenss
genfrd
uss (Robust Control Toolbox)
ufrd (Robust Control Toolbox)
Dynamic Control Design Blocks — Tunable, uncertain, or switch analysis points for constructing models of control systemstunableGain
tunableTF
tunableSS
tunablePID
tunablePID2
ultidyn (Robust Control Toolbox)
udyn (Robust Control Toolbox)
AnalysisPoint

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