modred

Model order reduction

Syntax

rsys = modred(sys,elim)
rsys = modred(sys,elim,'method')

Description

rsys = modred(sys,elim) reduces the order of a continuous or discrete state-space model sys by eliminating the states found in the vector elim. The full state vector X is partitioned as X = [X1;X2] where X1 is the reduced state vector and X2 is discarded.

elim can be a vector of indices or a logical vector commensurate with X where true values mark states to be discarded. This function is usually used in conjunction with balreal. Use balreal to first isolate states with negligible contribution to the I/O response. If sys has been balanced with balreal and the vector g of Hankel singular values has M small entries, you can use modred to eliminate the corresponding M states. For example:

[sys,g] = balreal(sys)  % Compute balanced realization
elim = (g<1e-8)         % Small entries of g are negligible states
rsys = modred(sys,elim) % Remove negligible states

rsys = modred(sys,elim,'method') also specifies the state elimination method. Choices for 'method' include

  • 'MatchDC' (default): Enforce matching DC gains. The state-space matrices are recomputed as described in Algorithms.

  • 'Truncate': Simply delete X2.

The 'Truncate' option tends to produces a better approximation in the frequency domain, but the DC gains are not guaranteed to match.

If the state-space model sys has been balanced with balreal and the grammians have m small diagonal entries, you can reduce the model order by eliminating the last m states with modred.

Examples

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Order Reduction by Matched-DC-Gain and Direct-Deletion Methods

Consider the following continuous fourth-order model.

$$h(s) = \frac{{{s^3} + 11{s^2} + 36s + 26}}{{{s^4} + 14.6{s^3} +&#xA;74.96{s^2} + 153.7s + 99.65}}.$$

To reduce its order, first compute a balanced state-space realization with balreal.

h = tf([1 11 36 26],[1 14.6 74.96 153.7 99.65]);
[hb,g] = balreal(h);

Examine the gramians.

g'
ans =

    0.1394    0.0095    0.0006    0.0000

The last three diagonal entries of the balanced gramians are relatively small. Eliminate these three least-contributing states with modred, using both matched-DC-gain and direct-deletion methods.

hmdc = modred(hb,2:4,'MatchDC');
hdel = modred(hb,2:4,'Truncate');

Both hmdc and hdel are first-order models. Compare their Bode responses against that of the original model.

bodeplot(h,'-',hmdc,'x',hdel,'*')

The reduced-order model hdel is clearly a better frequency-domain approximation of h. Now compare the step responses.

stepplot(h,'-',hmdc,'-.',hdel,'--')

While hdel accurately reflects the transient behavior, only hmdc gives the true steady-state response.

Limitations

With the matched DC gain method, A22 must be invertible in continuous time, and IA22 must be invertible in discrete time.

More About

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Algorithms

The algorithm for the matched DC gain method is as follows. For continuous-time models

x˙=Ax+Byy=Cx+Du

the state vector is partitioned into x1, to be kept, and x2, to be eliminated.

[x˙1x˙2]=[A11A12A21A22][x1x2]+[B1B2]uy=[C1C2]x+Du

Next, the derivative of x2 is set to zero and the resulting equation is solved for x1. The reduced-order model is given by

x˙1=[A11A12A22A21]x1+[B1A12A22B2]uy=[C1C2A22A21]x+[DC2A22B2]u

The discrete-time case is treated similarly by setting

x2[n+1]=x2[n]

See Also

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