Estimate initial states of model
x0 = findstates(sys,Data)
x0 = findstates(sys,Data,Horizon)
x0 = findstates(sys,Data,Horizon,Options)
[x0,Report]=
findstates(___)
Create a nonlinear greybox model. The model is a linear DC motor with one input (voltage), and two outputs (angular position and angular velocity). The structure of the model is specified by dcmotor_m.m file.
FileName = 'dcmotor_m'; Order = [2 1 2]; Parameters = [0.24365;0.24964]; nlgr = idnlgrey(FileName,Order,Parameters); nlgr = setinit(nlgr, 'Fixed', false(2,1)); % set initial states free
Load data for initial state estimation.
load(fullfile(matlabroot,'toolbox','ident',... 'iddemos','data','dcmotordata')); z = iddata(y,u,0.1);
Estimate the initial states such that the model's response using the estimated states X0 and measured input u is as close as possible to the measured output y.
X0 = findstates(nlgr,z,Inf);
Estimate an idss
model and simulate it such that the response of the estimated model matches the estimation data's output signal as closely as possible.
Load sample data.
load iddata1 z1;
Estimate a linear model from the data.
model = ssest(z1,2);
Estimate the value of the initial states to best fit the measured output z1.y
.
x0est = findstates(model,z1,Inf);
Simulate the model.
opt = simOptions('InitialCondition',x0est);
sim(model,z1(:,[],:),opt);
Estimate the initial states of a model selectively by fixing the first state and allowing the second state of the model to be estimated.
Create a nonlinear greybox model.
FileName = 'dcmotor_m';
Order = [2 1 2];
Parameters = [0.24365;0.24964];
nlgr = idnlgrey(FileName,Order,Parameters);
The model is a linear DC motor with one input (voltage), and two outputs (angular position and angular velocity). The structure of the model is specified by dcmotor_m.m
file.
Load the estimation data.
load(fullfile(matlabroot,'toolbox','ident',... 'iddemos','data','dcmotordata')); z = iddata(y,u,0.1);
Hold the first state fixed at zero, and estimate the value of the second.
x0spec = idpar('x0',[0;0]);
x0spec.Free(1) = false;
opt = findstatesOptions;
opt.InitialState = x0spec;
[X0,Report] = findstates(nlgr,z,Inf,opt)
X0 = 2×1
0
0.0061
Report = Status: 'Estimated by simulation error minimization' Method: 'lsqnonlin' Covariance: [2x2 double] DataUsed: [1x1 struct] Termination: [1x1 struct]
Create a nonlinear greybox model.
FileName = 'dcmotor_m';
Order = [2 1 2];
Parameters = [0.24365;0.24964];
nlgr = idnlgrey(FileName,Order,Parameters);
The model is a linear DC motor with one input (voltage), and two outputs (angular position and angular velocity). The structure of the model is specified by dcmotor_m.m
file.
Load the estimation data.
load(fullfile(matlabroot,'toolbox','ident',... 'iddemos','data','dcmotordata')); z = iddata(y,u,0.1);
Specify an initial guess for the initial states.
x0spec = idpar('x0',[10;10]);
x0spec.Free
is true by default
Estimate the initial states
opt = findstatesOptions; opt.InitialState = x0spec; x0 = findstates(nlgr,z,Inf,opt)
x0 = 2×1
0.0362
0.1322
Create a nonlinear greybox model.
FileName = 'dcmotor_m'; Order = [2 1 2]; Parameters = [0.24365;0.24964]; nlgr = idnlgrey(FileName,Order,Parameters); set(nlgr, 'InputName','Voltage','OutputName', ... {'Angular position','Angular velocity'});
The model is a linear DC motor with one input (voltage), and two outputs (angular position and angular velocity). The structure of the model is specified by dcmotor_m.m
file.
Load the estimation data.
load(fullfile(matlabroot,'toolbox','ident',... 'iddemos','data','dcmotordata')); z = iddata(y,u,0.1,'Name','DCmotor',... 'InputName','Voltage','OutputName',... {'Angular position','Angular velocity'});
Create a threeexperiment data set.
z3 = merge(z,z,z);
Choose experiment for estimating the initial states:
Estimate initial state 1 for experiments 1 and 3
Estimate initial state 2 for experiment 1
The fixed initial states have zero values.
x0spec = idpar('x0',zeros(2,3));
x0spec.Free(1,2) = false;
x0spec.Free(2,[2 3]) = false;
opt = findstatesOptions;
opt.InitialState = x0spec;
Estimate the initial states
[X0,EstInfo] = findstates(nlgr,z3,Inf,opt);
sys
— Identified modelidss
object  idgrey
object  idnlarx
object  idnlhw
object  idnlgrey
objectIdentified model whose initial states are estimated, represented
as a linear statespace (idss
or idgrey
)
or nonlinear model (idnlarx
, idnlhw
,
or idnlgrey
).
Data
— Estimation dataiddata
objectEstimation data, specified as an iddata
object
with input/output dimensions that match sys
.
If sys
is a linear model, Data
can
be a frequencydomain iddata
object. For easier
interpretation of initial conditions, make the frequency vector of Data
be
symmetric about the origin. For converting timedomain data into frequencydomain
data, use fft
with 'compl'
input
argument, and ensure that there is sufficient zero padding. Scale
your data appropriately when you compare x0
between
the timedomain and frequencydomain. Since for an Npoint
fft, the input/output signals are scaled by 1/sqrt(N)
,
the estimated x0
vector is also scaled by this
factor.
Horizon
— Prediction horizon for computing model response1
(default)  positive integer between 1
and Inf
Prediction horizon for computing the response of sys
,
specified as a positive integer between 1
and Inf
.
The most common values used are:
Horizon = 1
— Minimizes
the 1step prediction error. The 1–step ahead prediction response
of sys
is compared to the output signals in Data
to
determine x0
. See predict
for
more information.
Horizon = Inf
— Minimizes
the simulation error. The difference between measured output, Data.y
,
and simulated response of sys
to the measured
input data, Data.u
is minimized. See sim
for more information.
Specify Horizon
as any positive integer
between 1 and Inf
, with the following restrictions:
Scenario  Horizon 

Continuoustime model with timedomain data  1 or Inf 
Continuoustime frequencydomain data (  Inf 
Output Error models (trivial noise component):

Irrelevant Any value of 
Nonlinear ARX (idnlarx )  1 or Inf 
Options
— Estimation options for findstates
findstates
Option setEstimation options for findstates
, specified
as an option set created using findstatesOptions
x0
— Estimated initial statesEstimated initial states of model sys
,
returned as a vector or matrix. For multiexperiment data, x0
is
a matrix with one column for each experiment.
Report
— Initial state estimation informationInitial state estimation information, returned as a structure. Report
contains
information about the data used, state covariance, and results of
any numerical optimization performed to search for the initial states. Report
has
the following fields:
Report Field  Description  

Status  Summary of how the initial state were estimated.  
Method  Search method used.  
Covariance  Covariance of state estimates, returned as a NsbyNs matrix, where Ns is the number of states.  
DataUsed  Attributes of the data used for estimation, returned as a structure with the following fields:
 
Termination  Termination conditions for the iterative search used for initial state estimation of nonlinear models. Structure with the following fields:

Parallel computing support is available for estimation using the
lsqnonlin
search method (requires Optimization
Toolbox™). To enable parallel computing, use findstatesOptions
, set SearchMethod
to
'lsqnonlin'
, and set
SearchOptions.Advanced.UseParallel
to
true
.
For example:
opt = findstatesOptions;
opt.SearchMethod = 'lsqnonlin';
opt.SearchOptions.Advanced.UseParallel = true;
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