Padé approximation of model with time delays
[num,den] = pade(T,N)
sysx = pade(sys,N)
sysx = pade(sys,NU,NY,NINT)
pade approximates time delays by rational
models. Such approximations are useful to model time delay effects
such as transport and computation delays within the context of continuous-time
systems. The Laplace transform of a time delay of T seconds
is exp(–sT). This exponential transfer function
is approximated by a rational transfer function using Padé approximation
[num,den] = pade(T,N)
returns the Padé approximation
N of the continuous-time
I/O delay exp(–sT) in transfer function
form. The row vectors
the numerator and denominator coefficients in descending powers of s.
When invoked without output arguments,
the step and phase responses of the
approximation and compares them with the exact responses of the model
with I/O delay
T. Note that the Padé approximation
has unit gain at all frequencies.
sysx = pade(sys,N) produces
a delay-free approximation
sysx of the continuous
sys. All delays are replaced by their
Padé approximation. See Models with Time Delays for more information about
models with time delays.
sysx = pade(sys,NU,NY,NINT) specifies independent approximation
orders for each input, output, and I/O or internal delay. Here
NINT are integer arrays such that
NU is the vector of approximation
orders for the input channel
NY is the vector of approximation
orders for the output channel
NINT is the approximation order
for I/O delays (TF or ZPK models) or internal delays (state-space
You can use scalar values for
NINT to specify a uniform approximation order.
You can also set some entries of
Inf to prevent approximation
of the corresponding delays.
Compute a third-order Padé approximation of a 0.1-second I/O delay.
s = tf('s'); sys = exp(-0.1*s); sysx = pade(sys,3)
sysx = -s^3 + 120 s^2 - 6000 s + 1.2e05 -------------------------------- s^3 + 120 s^2 + 6000 s + 1.2e05 Continuous-time transfer function.
sys is a dynamic system representation of the exact time delay of 0.l s.
sysx is a transfer function that approximates that delay.
Compare the time and frequency responses of the true delay and its approximation. Calling the
pade command without output arguments generates the comparison plots. In this case the first argument to
pade is just the magnitude of the exact time delay, rather than a dynamic system representing the time delay.
High-order Padé approximations produce transfer functions
with clustered poles. Because such pole configurations tend to be
very sensitive to perturbations, Padé approximations with order
 Golub, G. H. and C. F. Van Loan, Matrix Computations, Johns Hopkins University Press, Baltimore, 1989, pp. 557-558.