Estimate state-space model using a subspace method.


sys = n4sid(data,nx)
sys = n4sid(data,nx,Name,Value)
sys = n4sid(___,opt)
[sys,x0] = n4sid(___)


sys = n4sid(data,nx) estimates an nx order state-space model, sys, using measured input-output data, data.

sys is an idss model representing the system:


A,B,C, and D are state-space matrices. K is the disturbance matrix. u(t) is the input, y(t) is the output, x(t) is the vector of nx states and e(t) is the disturbance.

All the entries of the A, B, C and K matrices are free estimation parameters by default. D is fixed to zero by default, meaning that there is no feedthrough, except for static systems (nx=0).

sys = n4sid(data,nx,Name,Value) specifies additional attributes of the state-space structure using one or more Name,Value pair arguments. Use the Form, Feedthrough and DisturbanceModel name-value pair arguments to modify the default behavior of the A, B, C, D, and K matrices.

sys = n4sid(___,opt) specifies estimation options, opt, that configure the initial states, estimation objective, and subspace algorithm related choices to be used for estimation.

[sys,x0] = n4sid(___) also returns the estimated initial state.

Input Arguments

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Estimation data.

For time domain estimation, data is an iddata object containing the input and output signal values.

For frequency domain estimation, data can be one of the following:

  • Recorded frequency response data (frd or idfrd)

  • iddata object with its properties specified as follows:

    • InputData — Fourier transform of the input signal

    • OutputData — Fourier transform of the output signal

    • Domain — ‘Frequency'

For multiexperiment data, the sample times and intersample behavior of all the experiments must match.

You can only estimate continuous-time models using continuous-time frequency domain data. You can estimate both continuous-time and discrete-time models (of sample time matching that of data) using time-domain data and discrete-time frequency domain data.


Order of estimated model.

Specify nx as a positive integer. nx may be a scalar or a vector. If nx is a vector, then n4sid creates a plot which you can use to choose a suitable model order. The plot shows the Hankel singular values for models of different orders. States with relatively small Hankel singular values can be safely discarded. A default choice is suggested in the plot.

You can also specify nx as 'best', in which case the optimal order is automatically chosen from nx = 1,..,10.


Estimation options.

opt is an options set, created using n4sidOptions, which specifies options including:

  • Estimation objective

  • Handling of initial conditions

  • Subspace algorithm related choices

Name-Value Pair Arguments

Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside single quotes (' '). You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

'Ts' — Sample time sample time of data (data.Ts) (default) | positive scalar | 0

Sample time. For continuous-time models, use Ts = 0. For discrete-time models, specify Ts as a positive scalar whose value is equal to that of the data sample time.

'Form' — Type of canonical form'free' (default) | 'modal' | 'companion' | 'canonical'

Type of canonical form of sys, specified as one of the following strings.

  • 'modal' — Obtain sys in modal form.

  • 'companion' — Obtain sys in companion form.

  • 'free' — All entries of the A, B and C matrices are estimated.

  • 'canonical' — Obtain sys in observability canonical form [1].

Use the Form, Feedthrough and DisturbanceModel name-value pair arguments to modify the default behavior of the A, B, C, D, and K matrices.

'Feedthrough' — Direct feedthrough from input to output

Logical specifying direct feedthrough from input to output.

Feedthrough is a logical vector of length of length Nu, where Nu is the number of inputs.

If Feedthrough is specified as a logical scalar, this value is applied to all the inputs.

'DisturbanceModel' — Specify whether to estimate the K matrix

Specifies if the noise component, the K matrix, is to be estimated.

DisturbanceModel requires one of the following values:

  • 'none' — Noise component is not estimated. The value of the K matrix, is fixed to zero value.

  • 'estimate' — The K matrix is treated as a free parameter.

DisturbanceModel must be 'none' when using frequency domain data.

'InputDelay' — Input delays0 (default) | scalar | vector

Input delay for each input channel, specified as a numeric vector. For continuous-time systems, specify input delays in the time unit stored in the TimeUnit property. For discrete-time systems, specify input delays in integer multiples of the sample time Ts. For example, InputDelay = 3 means a delay of three sampling periods.

For a system with Nu inputs, set InputDelay to an Nu-by-1 vector. Each entry of this vector is a numerical value that represents the input delay for the corresponding input channel.

You can also set InputDelay to a scalar value to apply the same delay to all channels.

Output Arguments


Identified state-space model.

sys is an idss model, which encapsulates the identified state-space model.


Initial states computed during the estimator of sys.

If data contains multiple experiments, then x0 is an array with each column corresponding to an experiment.


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Estimate State-Space Model and Specify Estimation Options

Load estimation data.

load iddata2 z2

Specify the estimation options.

opt = n4sidOptions('Focus','simulation','Display','on');

Estimate the model.

nx = 3;
sys = n4sid(z2,nx,opt);

sys is a third-order, state-space model.

Estimate State-Space Model from Closed-Loop Data

Estimate a state-space model from closed-loop data using the subspace algorithm SSARX. This algorithm is better at capturing feedback effects than other weighting algorithms.

Generate closed-loop estimation data for a second-order system corrupted by white noise.

N = 1000; K = 0.5;
w = randn(N,1);
z = zeros(N,1); u = zeros(N,1); y = u;
e = randn(N,1);
v = filter([1 0.5],[1 1.5 0.7],e);
z(1) = 0; z(2) = 0; u(1) = 0; u(2) = 0; y(1) = 0; y(2) = 0;
for k = 3:N
   u(k-1) = -K*y(k-2)+w(k);
   u(k-1) = -K*y(k-1)+w(k);
   z(k) = 1.5*z(k-1)-0.7*z(k-2)+u(k-1)+0.5*u(k-2);
   y(k) = z(k) + .8*v(k);
dat = iddata(y, u, 1);

Specify the weighting scheme used by the N4SID algorithm. In one options set, specify the algorithm as CVA and in the other, specify as SSARX.

optCVA = n4sidOptions('N4weight','CVA');
optSSARX = n4sidOptions('N4weight','SSARX');

Estimate state-space models using the options sets.

sysCVA = n4sid(dat, 2, optCVA);
sysSSARX = n4sid(dat, 2, optSSARX);

Compare the fit of the two models with the estimation data.

compare(dat, sysCVA, sysSSARX);

From the plot, you see that the model estimated using the SSARX algorithm produces a better fit than the CVA algorithm.

Estimate a Canonical-Form, Continuous-Time Model

Estimate a continuous-time, canonical-form model.

Load estimation data.

load iddata1 z1

Specify the estimation options.

opt = n4sidOptions('Focus','simulation','Display','on');

Estimate the model.

nx = 2;
sys = n4sid(z1,nx,'Ts',0,'Form','canonical',opt);

sys is a second-order, continuous-time, state-space model in the canonical form.

More About

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Modal Form

In modal form, A is a block-diagonal matrix. The block size is typically 1-by-1 for real eigenvalues and 2-by-2 for complex eigenvalues. However, if there are repeated eigenvalues or clusters of nearby eigenvalues, the block size can be larger.

For example, for a system with eigenvalues (λ1,σ±jω,λ2), the modal A matrix is of the form


Companion Form

In the companion realization, the characteristic polynomial of the system appears explicitly in the right-most column of the A matrix. For a system with characteristic polynomial


the corresponding companion A matrix is


The companion transformation requires that the system be controllable from the first input. The companion form is poorly conditioned for most state-space computations; avoid using it when possible.


[1] Ljung, L. System Identification: Theory for the User, Appendix 4A, Second Edition, pp. 132–134. Upper Saddle River, NJ: Prentice Hall PTR, 1999.

[2] van Overschee, P., and B. De Moor. Subspace Identification of Linear Systems: Theory, Implementation, Applications. Springer Publishing: 1996.

[3] Verhaegen, M. "Identification of the deterministic part of MIMO state space models." Automatica, 1994, Vol. 30, pp. 61—74.

[4] Larimore, W.E. "Canonical variate analysis in identification, filtering and adaptive control." Proceedings of the 29th IEEE Conference on Decision and Control, 1990, pp. 596–604.

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