fmincon very sensitive to DiffminChange

I have a bounded non-linear optimization problem, which gives the initial value as the final solution when using the default settings for fmincon. So I changed the algorithm to SQP, and made changes to the DiffminChange by trial and error till I get the actual optima (in this test case I know the minimum value of the objective function is -1 at R > 0.76).

fun = @(R)split_mix_feed(R,x_F,x_R,hpd,p,Wunsch);

options = optimset('Display','iter-detailed','Algorithm','sqp',...
'DiffminChange',0.009, 'DiffmaxChange',2,'MaxFunEvals',10,...
'Tolx',1e-1);

[R, fval, exitflag, output] = fmincon(fun,0.1,[],[],[],[],0,5,[],options)

function recovery = split_mix_feed(R,x_F,x_R,hpd,p,Wunsch)
mix_point = (x_F+R*x_R)/(1+R);
split = single_column_product_split(hpd,p,mix_point,Wunsch);
recovery = -split.max_yield;

This is the output I obtain when using the settings specified above

Iter F-count            f(x) Feasibility  Steplength        step  optimality
    0       2   -8.500000e-01   0.000e+00                           6.944e-01
    1       4   -1.000000e+00   0.000e+00   1.000e+00   6.944e-01   0.000e+00

Optimization completed: The relative first-order optimality measure, 0.000000e+00,
is less than options.OptimalityTolerance = 1.000000e-06, and the relative maximum constraint
violation, 0.000000e+00, is less than options.ConstraintTolerance = 1.000000e-06.

Optimization Metric                                            Options
relative first-order optimality =   0.00e+00       OptimalityTolerance =   1e-06 (default)
relative max(constraint violation) =   0.00e+00    ConstraintTolerance =   1e-06 (default)


R =

    0.7944


fval =

    -1


exitflag =

     1


output = 

         iterations: 1
          funcCount: 4
          algorithm: 'sqp'
            message: 'Local minimum found that satisfies the constraints.…'
    constrviolation: 0
           stepsize: 0.6944
       lssteplength: 1
      firstorderopt: 0

Elapsed time is 1.930386 seconds.

The problem now is that when I change the system, i.e. change the input arguments of the objective function, the optimizer is not able to perform well. I cannot tune the fmincon settings for each system. How can I make the optimization routine more robust?