I am trying to understand exactly the underlying theory that pem uses working with merged data. Specifically, I am using a grey box model where the initial conditions and Kalman gain is parameterized by me. Is it just performing the identification separately and then combining the result or is it estimated in one run with two sets of initial conditions. Code is included as an example.
%%Define system
A = [0.8 0.1; 0.1 0.7];B = [0.2; 0.7];C = [1 0];D = 0;Q = 0.1;R = 0.1;Ts = 1;%%Simulate the system twice
n = 200;y = cell(2, 1);y{1} = zeros(1, n);y{2} = y{1};u = [0*ones(1, 20), 1*ones(1, 30), 2*ones(1, 20), -1*ones(1, 20), 0*ones(1, 30), 2*ones(1, 20), 0*ones(1, 30), -2*ones(1, 30)];x = zeros(2, n + 1);for i = 1:2; if( i == 1 ) x(:, 1) = [4; 4]; else x(:, 1) = [-1; -1]; end for k = 1:n; y{i}(k) = C*x(:, k) + D*u(:, k) + sqrt(R)*randn; x(:, k + 1) = A*x(:, k) + B*u(:, k) + sqrt(Q)*randn; end end%%plot output
plot([y{1}', y{2}']);%%gather data
data1 = iddata(y{1}', u', Ts);data2 = iddata(y{2}', u', Ts);data_all = merge(data1, data2);%%identify
model1 = idgrey('sysmodel', zeros(1, 6), 'd');options = greyestOptions('Display', 'On', 'Focus', 'Prediction');options.SearchOption.MaxIter = 1000;model_out = pem(data_all, model1, options);%, 'OutputWeight', [1 0; 0 0]);
MODEL FUNCTION
function [A, B, C, D, K, X0] = sysmodel(phi, Ts, extra)%SYSMODEL Summary of this function goes here
% Detailed explanation goes here
A = [phi(1) 0.1; 0.1 phi(2)];B = [0.2; 0.7];C = [1 0];D = 0;K = [phi(3); phi(4)];X0 = [phi(5); phi(6)];end
Bump.
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