Fitting data to a nonlinear model to estimate parameters

I am trying estimate two unknown parameters in a nonlinear model. The script that is have made works for some iterations but suddenly stops. The code is as follows:

 ws; % some data with 8 columns and n rows

 a = 0.0144; % constant
 g = 9.81; % constant
 k = 0.4; % constant
 h = [30,40,50,60,70,80,90,100,106];

 x0 = [100,-1];
 options = optimoptions(@lsqnonlin,'Algorithm','trust-region-reflective');
 for i = 1:size(ws,1)
     v = ws(:,i);
     fun = @(x) x(1)/k*(log(h/(a*x(1)^2/g))-x(2))-v;
     x = lsqnonlin(fun,x0,[],[],options);
     ustar(i) = x(1);
     Psi_M(i) = x(2);
 end

Is there a more effecient way of fitting the data to the model which also outputs the two unknown parameters?