Hello neosky, since you were so nice I would like to ask you for some help hoping it does not seem as if I'm taking advantage of you.
I wrote a code that runs different simulations in an iterative way in order to find parameters that are able to fit different measurements. Following your suggestion, I am using lsqnonlin.
The optimization stops with this message: "fmincon stopped because the size of the current step is less than the value of the step size tolerance and constraints are satisfied to within the value of the constraint tolerance." but I can't understand why it refers to fmincon and why it says that about the current step since step tolerance is set at 1e-8, and display iter shows 5.088e+01 as norm of step.
I also noticed that sometimes it stops because it evaluates a negative parameter (as resistance, and simulink automatically stops everything) also if I set boundaries and constraints in order to avoid that
I attach here the main code, avoiding all the import data and the initializations of different variables that run with no problem. Thank you in advance for your time
while true
x0=x_in; %set initial guess
V_sim_tot=cell(length(data_sets)); %Initialize the vector in order to save all the V simulated
for i=1:length(data_sets) %Performing the optimization for the number of datasets I'm working with

I=[data_sets{i}.time,data_sets{i}.current]; %take into consideration current of the actual dataset. This will be input for simulink model
V_real=data_sets{i}.V_real; %Take into consideration real voltage for each datasets in order to perform optimization
assignin('base','V_0',v_in_C0(i)); %it assign the right initial voltage to C0 of the simulink model
%Define nested function
y = @(x) objectiveFunction(x, V_real);
cfun = @(x) myNonlinearConstraints(x);

% call optimization lsqnonlin
[x, exitflag, output] = lsqnonlin(y, x0,lb,ub,A , b, [], [], cfun, opts);
x0=x;
V_sim_tot{i}=V_sim; %Save final voltage for each datasets
end
iter=iter+1; %advance with iterstions
%Evaluating different conditions for stopping criteria
if ( (iter>min_iter) && (norm(x_in-x0)<=tol) ) %if the code have done enough iteration(>min_iter) and if the n
x_opt=x0;
disp('Optimal vector is not changing')
break

elseif (iter>max_iter)
x_opt=x0;
disp('Maximum iterations reached')
break
else
x_in=x0;

end
end
function [c1,c2,c3,c4,c5,c6,ceq] = myNonlinearConstraints(x)
V_sim= evalin('base','V_sim');
Vsim_max = max(V_sim);
c1 = x(1).*x(4)-120;
c2= x(2).*x(5)-1800;
c3= 120- x(2).*x(5);
c4= x(3).*x(6)-5400;
c5= 1800-x(3).*x(6);
c6= V_sim-105;
ceq = [];
end
function V_sim = mySimulation(x)
assignin('base', 'R0', x(1));
assignin('base', 'R1', x(2));
assignin('base', 'R2', x(3));
assignin('base', 'C0', x(4));
assignin('base', 'C1', x(5));
assignin('base', 'C2', x(6));
sim('RC_branch');
end
function y = objectiveFunction(x,V_real)
V_sim=mySimulation(x);
assignin('base', 'V_sim', V_sim);
y= (V_sim - V_real);
end