Numeric LTI models are the basic representation of linear systems or components of linear systems whose coefficients are fixed numeric values.
You can use Numeric LTI models to represent block diagram components such as plant or sensor dynamics. By connecting Numeric LTI models together, you can derive Numeric LTI models of block diagrams. Use Numeric LTI models for most modeling, analysis, and control design tasks, including:
Analyzing linear system dynamics using analysis commands
Control System Toolbox™ includes the following types of numeric LTI models:
Control System Toolbox software supports transfer functions that are continuous-time or discrete-time, and SISO or MIMO. You can also have time delays in your transfer function representation.
You can represent linear systems as transfer functions in polynomial or factorized (zero-pole-gain) form. For example, the polynomial-form transfer function:
can be rewritten in factorized form as:
tf model object represents transfer
functions in polynomial form. The
zpk model object
represents transfer functions in factorized form.
MIMO transfer functions are arrays of SISO transfer functions. For example:
is a one-input, two output transfer function.
Use the commands described in the following table to create transfer functions.
State-space models rely on linear differential equations or difference equations to describe system dynamics. Control System Toolbox software supports SISO or MIMO state-space models in continuous or discrete time. State-space models can include time delays. You can represent state-space models in either explicit or descriptor (implicit) form.
State-space models can result from:
Linearizing a set of ordinary differential equations that represent a physical model of the system.
State-space model identification using System Identification Toolbox™ software.
State-space realization of transfer functions. (See Conversion Between Model Types for more information.)
ss model objects
to represent state-space models.
Explicit continuous-time state-space models have the following form:
A discrete-time explicit state-space model takes the following form:
where the vectors x[n], u[n], and y[n] are the state, input, and output vectors for the nth sample.
Use the commands described in the following table to create state-space models.
In the Control System Toolbox software, you can use
frd models to store, manipulate, and
analyze frequency response data. An
stores a vector of frequency points with the corresponding complex
frequency response data you obtain either through simulations or experimentally.
For example, suppose you measure frequency response data for the SISO system you want to model. You can measure such data by driving the system with a sine wave at a set of frequencies ω1, ω2, ,...,ωn, as shown:
The measurement yields the complex frequency response G at each input frequency:
You can do most frequency-domain analysis tasks on
but you cannot perform time-domain simulations with them. For information
on frequency response analysis of linear systems, see Chapter 8 of .
Use the following commands to create FRD models.
You can represent continuous-time Proportional-Integral-Derivative (PID) controllers in either parallel or standard form. The two forms differ in the parameters used to express the proportional, integral, and derivative actions and the filter on the derivative term, as shown in the following table.
Use a controller form that is convenient for your application. For example, if you want to express the integrator and derivative actions in terms of time constants, use Standard form.
Discrete-time PID controllers are expressed by the following formulas.
IF(z) and DF(z)
are the discrete integrator formulas for the integrator and derivative
filter, respectively. Use the
objects to set the IF(z) and DF(z)
formulas. The next table shows available formulas for IF(z)
and DF(z). Ts is
the sample time.
|IF(z) or DF(z)|
If you do not specify a value for
ForwardEuler is used by default.