Fitting simulink model with optimization toolbox?

I don't currently have Simulink Design Optimization.

I'm using optimization and global optimization toolboxes to fit a simulink model to data

I'd like to use something like the pseudo code example below. Am I barking up the right tree, or is there a better approach? The optimization examples on the mathworks site don't have any simulink optimization by toolbox examples. Are there examples out there?

Anyone have feedback on Simulation Design Optimization vs using optimization toolbox?

Thanks!

FitModel.m

Program FitModel

load_system('mymodel') ;    % mymodel uses named parameters, and loads them with preload function
        % For this example, the preload function is prlfcn.m
x0(1) = a;      % a and b are matlab variables in defined in the script prlfcn.m  
x0(2) = b;      % and are parameters in the simulink model mymodel. For example, 
                              % the value of a simulink constant block might be set to "a", and  
                              % a simulink integrator initial condition might be set to "b"
        % prlfcn.m contains my best current guess for a and b
ofh = @objfun(x)            % objfun is the objective function, x is the vector of parameters
              
bestx = fminsearch(ofh,x0);

% end FitModel.m

objfun.mat

function value = objfun(x)

a = x(1);    % Here, I'm putting the passed parameter x values into the matlab
b = x(2);       % variables a and b, which are used by the model mymodel

sim('mymodel') ;    % mymodel writes data to a "to file" block output.mat

load('output.mat')    % output.mat has t and y vectors
load('data.mat')      % data.mat has tdata and ydata vectors

value = (y-ydata)'*(y-ydata)
return
% end objfun.m

The preload function is represented by this simple example:

prlfcn.m

a = 5;  % value for constant 1
b = 10; % initial condition for the model integrator
% end prlfcn.m

Answer 1

[Yogananda Jeppu]

I use a global variable for this and this works well. Define the tunable parameters in simulink as X array. X(1) is a gain in the simulink. X(2) is initial condition etc. Dont load data inside the function. Do that outside and just pass values as parameters of global.

global X inp y ydata

function value = objfun(x) global X inp y ydata % perhaps you have all absolute values X = abs(x); sim('mymodel') ; % mymodel writes data to a "to file" block output.mat

value = norm(y-ydata); return

This has been working for me. Not an elegant code in terms of software engineering but it gets the job done.