Estimate state-space model using time-domain or frequency-domain data
estimates a continuous-time state-space model sys
= ssest(data
,nx
)sys
of order
nx
, using data data
that can be in the time
domain or the frequency domain. sys
is a model of the following
form:
A, B, C,
D, and K are state-space matrices.
u(t) is the input,
y(t) is the output,
e(t) is the disturbance, and
x(t) is the vector of nx
states.
All entries of A, B, C, and
K are free estimable parameters by default. D is
fixed to zero by default, meaning that there is no feedthrough, except for static systems
(nx = 0
).
incorporates additional options specified by one or more name-value pair arguments. For
example, estimate a discrete-time model by specifying the sample time
sys
= ssest(data
,nx
,Name,Value
)'Ts'
name-value pair argument. Use the 'Form'
,
'Feedthrough'
, and 'DisturbanceModel'
name-value pair arguments to modify the default behavior of the A,
B, C, D, and
K matrices.
ssest
initializes the parameter estimates using either a noniterative
subspace approach or an iterative rational function estimation approach. It then refines the
parameter values using the prediction error minimization approach. For more information, see
pem
and ssestOptions
.
[1] Ljung, L. System Identification: Theory for the User, Second Edition. Upper Saddle River, NJ: Prentice Hall PTR, 1999.
canon
| iddata
| idfrd
| idgrey
| idss
| n4sid
| pem
| polyest
| procest
| ssestOptions
| ssregest
| tfest