LTPDA Toolbox™ contents Create simulated experiment data sets   Calculate expected covariance of the parameters (FIM)

Build state-space LTP models for system identification

The model to use for the estimation of the parameters can be constructed in a similar way as we did before when we simulated the signals. The difference is that the parameters to be estimated must remain symbolic. This is achieved with the 'SYMBOLIC PARAMS' key of the plist. The model must be continuous. 

 
% cell array contains the names of the parameters. 
params = {'FEEPS_XX', 'CAPACT_TM2_XX', 'IFO_X12X1', 'EOM_TM1_STIFF_XX', 'EOM_TM2_STIFF_XX'};
      
%% built ssm model                        
model = ssm(plist('built-in', 'LTP', ...
  'DIM', 1, ...                     % dimensions of LPF model 
  'CONTINUOUS', 1, ...              % Continuous or descrete 
  'SYMBOLIC PARAMS', params, ...    % symbolic parameters to estimate 
  'VERSION', 'Standard'));        % Version of the LPF 

%% save fitting model
save(model, 'fitting_model.mat');

We can view all available information about the model if we type model.viewDetails in the command window. First of all, the structure of the SSM LTP model is presented and then the available inputs and outputs of the system. 

>> model.viewDetails


Create simulated experiment data sets  Create simulated experiment data sets   Calculate expected covariance of the parameters (FIM) Calculate expected covariance of the parameters (FIM)

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