User`s guide

3 Linear Model Identification
The follow ing commands compare e stimated model m and its continuous-time
counterpart
mc on a Bode plot:
% Estimate discrete-time ARMA X model
% from the data
m = armax(data,[2 3 1 2]) ;
% Convert to continuous-time form
mc = d2c(m);
% Plot bode plot for both models
bode(m,mc)
Specifying Intersample Behavior
A sampled signal is characterized only by its values at the sampling instants.
However, when you apply a continuous-time input to a continuous-time
system, the output values at the sampling instants depend on the inputs at
the sampling instants and on the inputs b etween these points. Thus, the
InterSample data property describes how the algorithms should handle the
input between samples. For example, you can specify the behavior betwe en
the samples to be piece-wise constant (zero-order hold,
zoh)orlinearly
interpolated be tween the samples ( first order hold,
foh). The tra n sformation
formulas for
c2d and d2c are affected by the intersample behavior of the input.
By default,
c2d and d2c use the intersample behavior you assigned to the
estimation data. To override this setting during transformation, add an extra
argument in the syntax. For example:
% Set first-order hold intersample behavior
mod_d = c2d(mod_c,T,'foh')
How d2c Handles Input Delays
The discrete-to-continuous-time conversion d2c properly handles any
input delays in the discrete-time model, and stores this information in the
continuous-time model. An input delay is the delay in the response of the
output to the input signal.
The relationship between discrete-time and continuous-time delays depends
on the input intersample behavior. For example, a continuous-time system
without a delay shows a delay when sampled with a zero-order-hold input.
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