Hankel singular values of dynamic system
hsv = hsvd(
hsv = hsvd(
[hsv,baldata] = hsvd(___)
the Hankel singular values
hsv = hsvd(
hsv of the dynamic
sys. In state coordinates that equalize
the input-to-state and state-to-output energy transfers, the Hankel
singular values measure the contribution of each state to the input/output
behavior. Hankel singular values are to model order what singular
values are to matrix rank. In particular, small Hankel singular values
signal states that can be discarded to simplify the model (see
For models with unstable poles,
computes the Hankel singular values of the stable part and entries
hsv corresponding to unstable modes are set
the Hankel singular values using options that you specify using
hsv = hsvd(
hsvdOptions. Options include offset
and tolerance options for computing the stable-unstable decompositions.
The options also allow you to limit the HSV computation to energy
contributions within particular time and frequency intervals. See
hsvdOptions for details.
[hsv,baldata] = hsvd(___) returns
additional data to speed up model order reduction with
balred. You can use this syntax with
any of the previous combinations of input arguments.
hsvd(___) displays a Hankel
singular values plot.
Create a system with a stable pole very near to 0, and display the Hankel singular values.
sys = zpk([1 2],[-1 -2 -3 -10 -1e-7],1); hsv = hsvd(sys)
hsv = 5×1 105 × 1.6667 0.0000 0.0000 0.0000 0.0000
Notice the dominant Hankel singular value with magnitude , which is so much larger that the significant digits of the other modes are not displayed. This value is due to the near-unstable mode at . Use the
'Offset' option to treat this mode as unstable.
opts = hsvdOptions('Offset',1e-7); hsvu = hsvd(sys,opts)
hsvu = 5×1 Inf 0.0688 0.0138 0.0024 0.0001
The Hankel singular value of modes that are unstable, or treated as unstable, is returned as
Inf. Create a Hankel singular-value plot while treating this mode as unstable.
The unstable mode is shown in red on the plot.
hsvd uses a linear scale. To switch the plot to a log scale, right-click on the plot and select Y Scale > Log. For information about programmatically changing properties of HSV plots, see
Compute the Hankel singular values of a model with low-frequency and high-frequency dynamics. Focus the calculation on the high-frequency modes.
Load the model and examine its frequency response.
load modeselect Gms bodeplot(Gms)
Gms has two sets of resonances, one at relatively low frequency and the other at relatively high frequency. Compute the Hankel singular values of the high-frequency modes, excluding the energy contributions to the low-frequency dynamics. To do so, use
hsvdOptions to specify a frequency interval above 30 rad/s.
opts = hsvdOptions('FreqInterval',[30 Inf]); hsvd(Gms,opts)
To create a Hankel singular-value plot with more flexibility
to programmatically customize the plot, use
hsvdOptions are only used for models with
unstable or marginally stable dynamics. Because Hankel singular values
are only meaningful for stable dynamics,
first split such models into the sum of their stable and unstable
G = G_s + G_ns
This decomposition can be tricky when the model has modes close
to the stability boundary (e.g., a pole at
or clusters of modes on the stability boundary (e.g., double or triple
hsvd is able to overcome these
difficulties in most cases, it sometimes produces unexpected results
Large Hankel singular values for the stable part.
This happens when the stable part
some poles very close to the stability boundary. To force such modes
into the unstable group, increase the
to slightly grow the unstable region.
Too many modes are labeled "unstable." For example, you see 5 red bars in the HSV plot when your model had only 2 unstable poles.
The stable/unstable decomposition algorithm has built-in accuracy
checks that reject decompositions causing a significant loss of accuracy
in the frequency response. Such loss of accuracy arises, e.g., when
trying to split a cluster of stable and unstable modes near
Because such clusters are numerically equivalent to a multiple pole
s=0, it is actually desirable to treat the whole
cluster as unstable. In some cases, however, large relative errors
in low-gain frequency bands can trip the accuracy checks and lead
to a rejection of valid decompositions. Additional modes are then
absorbed into the unstable part
G_ns, unduly increasing
Such issues can be easily corrected by adjusting the
AbsTol to a fraction of smallest gain
of interest in your model, you tell the algorithm to ignore errors
below a certain gain threshold. By increasing
you tell the algorithm to sacrifice some relative model accuracy in
exchange for keeping more modes in the stable part
If you use the
the computation of state energy contributions on time-limited or frequency-limited
controllability and observability Gramians. For information about
calculating time-limited and frequency-limited Gramians, see
gram and .
 Gawronski, W. and J.N. Juang. “Model Reduction in Limited Time and Frequency Intervals.” International Journal of Systems Science. Vol. 21, Number 2, 1990, pp. 349–376.