Hankel minimum degree approximation (MDA) without balancing
GRED = hankelmr(G) GRED = hankelmr(G,order) [GRED,redinfo] = hankelmr(G,key1,value1,...) [GRED,redinfo] = hankelmr(G,order,key1,value1,...)
hankelmr returns a reduced
G and a
redinfo containing the error bound
of the reduced model and Hankel singular values of the original system.
The error bound is computed based on Hankel singular
G. For a stable system Hankel singular
values indicate the respective state energy of the system. Hence,
reduced order can be directly determined by examining the system Hankel
With only one input argument
G, the function
will show a Hankel singular value plot of the original model and prompt
for model order number to reduce.
This method guarantees an error bound on the infinity norm of
for well-conditioned model reduced problems :
It seems this method is similar to the additive model reduction
This table describes input arguments for
LTI model to be reduced (without any other inputs will plot its Hankel singular values and prompt for reduced order)
(Optional) an integer for the desired order of the reduced model, or optionally a vector packed with desired orders for batch runs
A batch run of a serial of different reduced order models can
be generated by specifying
order = x:y, or a vector of integers.
By default, all the anti-stable part of a system is kept, because
from control stability point of view, getting rid of unstable state(s)
is dangerous to model a system.
be specified in the same fashion as an alternative for
In this case, reduced order will be determined when the sum of the
tails of the Hankel sv's reaches the '
Real number or vector of different errors
Reduce to achieve H∞ error.
Optimal 1x2 cell array of LTI weights
Display Hankel singular plots (default
Integer, vector or cell array
Order of reduced model. Use only if not specified as 2nd argument.
Weights on the original model input and/or output can make the model reduction algorithm focus on some frequency range of interests. But weights have to be stable, minimum phase and invertible.
This table describes output arguments.
LTI reduced order model. Become multi-dimensional array when input is a serial of different model order array.
A STRUCT array with 4 fields:
G can be stable or unstable, continuous or
Given a continuous or discrete, stable or unstable system,
the following commands can get a set of reduced order models based
on your selections:
rng(1234,'twister'); G = rss(30,5,4); [g1, redinfo1] = hankelmr(G); % display Hankel SV plot % and prompt for order (try 15:20) [g2, redinfo2] = hankelmr(G,20); [g3, redinfo3] = hankelmr(G,[10:2:18]); [g4, redinfo4] = hankelmr(G,'MaxError',[0.01, 0.05]); for i = 1:4 figure(i); eval(['sigma(G,g' num2str(i) ');']); end
Singular Value Bode Plot of G (30-state,
5 outputs, 4 inputs) shows
a singular value Bode plot of a random system
20 states, 5 output and 4 inputs. The error system between
its Zeroth order Hankel MDA has it infinity norm
equals to an all pass function, as shown in All-Pass Error System Between G and Zeroth
Order G Anticausal.
The Zeroth order Hankel MDA and its error system sigma plot are obtained via commands
[g0,redinfo0] = hankelmr(G,0); sigma(G-redinfo0.Ganticausal)
This interesting all-pass property is unique in Hankel MDA model reduction.
Singular Value Bode Plot of G (30-state, 5 outputs, 4 inputs)
All-Pass Error System Between G and Zeroth Order G Anticausal
Given a state-space (A,B,C,D) of a system and k, the desired reduced order, the following steps will produce a similarity transformation to truncate the original state-space system to the kth order reduced model.
Find the controllability and observability grammians P and Q.
Form the descriptor
where , and descriptor state-space
Take SVD of descriptor E and partition the result into kth order truncation form
Apply the transformation to the descriptor state-space system above we have
Form the equivalent state-space model.
The final kth order
Hankel MDA is the stable part of the above state-space realization.
Its anticausal part is stored in
 Glover, K., "All Optimal Hankel Norm Approximation of Linear Multivariable Systems, and Their L∝-error Bounds," Int. J. Control, vol. 39, no. 6, pp. 1145-1193, 1984.
 Safonov, M.G., R.Y. Chiang, and D.J.N. Limebeer, "Optimal Hankel Model Reduction for Nonminimal Systems," IEEE Trans. on Automat. Contr., vol. 35, no. 4, April 1990, pp. 496-502.