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Basic Models

Common models of linear systems, such as transfer functions and state-space models

Numeric linear-time-invariant (LTI) models are the basic building blocks that you use to represent linear systems. Numeric LTI model objects let you store dynamic systems in commonly-used representations. For example, tf models represent transfer functions in terms of the coefficients of their numerator and denominator polynomials, and ss models represent LTI systems in terms of their state-space matrices. There are also LTI model types specialized for representing PID controllers in terms of their proportional, integral, and derivative coefficients.

Build up a more complex model of a control system by representing individual components as LTI models and connecting the components to model your control architecture. For an example, see Control System Modeling with Model Objects.


tfCreate transfer function model, convert to transfer function model
zpkCreate zero-pole-gain model; convert to zero-pole-gain model
ssCreate state-space model, convert to state-space model
frdCreate frequency-response data model, convert to frequency-response data model
filtSpecify discrete transfer functions in DSP format
dssCreate descriptor state-space models
pidCreate PID controller in parallel form, convert to parallel-form PID controller
pidstd Create a PID controller in standard form, convert to standard-form PID controller
pid2Create 2-DOF PID controller in parallel form, convert to parallel-form 2-DOF PID controller
pidstd2 Create 2-DOF PID controller in standard form, convert to standard-form 2-DOF PID controller
rssGenerate random continuous test model
drssGenerate random discrete test model


LTI SystemUse linear time invariant system model object in Simulink
LPV SystemSimulate Linear Parameter-Varying (LPV) systems

Examples and How To

Continuous-Time Models

Transfer Functions

Represent transfer functions in terms of numerator and denominator coefficients or zeros, poles, and gain.

State-Space Models

Represent state-space models in terms of the state-space matrices.

Frequency Response Data (FRD) Models

Represent dynamic systems in terms of the magnitude and phase of their responses at various frequencies.

Proportional-Integral-Derivative (PID) Controllers

Represent PID controllers in terms of controller gains or time constants.

Two-Degree-of-Freedom PID Controllers

2-DOF PID controllers can achieve faster disturbance rejection without significant increase of overshoot in setpoint tracking.

Discrete-Time Models

Discrete-Time Numeric Models

Represent discrete-time numeric models by specifying a sample time when you create the model object.

Discrete-Time Proportional-Integral-Derivative (PID) Controllers

The integrator and filter terms in discrete-time PID controllers can be represented by several different formulas.

MIMO Models

MIMO Transfer Functions

Create MIMO transfer functions by concatenating SISO transfer functions or by specifying coefficient sets for each I/O channel.

MIMO State-Space Models

These examples show how to represent MIMO systems as state-space models.

MIMO Frequency Response Data Models

Use frequency-response data from multiple I/O pairs in a system to create a MIMO frequency response model.

Select Input/Output Pairs in MIMO Models

Extract particular I/O channels from a MIMO dynamic system model.

Simulink Block

Import LTI Model Objects into Simulink

Use the LTI System block to import linear system model objects into Simulink®.


What Are Model Objects?

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

Types of Model Objects

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

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

Static Models

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

Control System Modeling with Model Objects

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.

Using Model Objects

Ways to use model objects include linear analysis, compensator design, and control system tuning.

Numeric Models

Numeric LTI Models represent dynamic elements, such as transfer functions or state-space models, with fixed coefficients.