Using Minpack to solve S-curve - fortran

Question

I'd like to use Minpack (fortran) to estimate the D parameter in the following generalized form of the S-curve: y = (A - D) / (1 + (x**B/C)) + D
The idea is that in this application, the user provides A [which is always 0 to force passage through (0,0)], B, and C, and from there Minpack will find a value for D that forces passage through (1,y), where y is also supplied by the user but must be <= 1. I was able to accomplish this task with the code below, however, minpack is claiming it hasn't converged when in fact it appears that it has. For example, when running this code and entering the values 1 (at the first prompt) and 0 4 0.1 (at the second prompting), minpack returns info = 2, which according to the comments in lmdif means:
relative error between two consecutive iterates is at most xtol.
I'm tempted to comment out line 63, but am worried that's playing with fire...are there any seasoned minpack users out there who could comment on this? Line 63 is the one that reads:
if (info /= 1) stop "failed to converge"
Am I mis-using Minpack even though it appears to converge (based on my verifying the value in pars)?
module types
implicit none
private
public dp
integer, parameter :: dp=kind(0d0)
end module
module f_vals
DOUBLE PRECISION, SAVE, DIMENSION(:), POINTER:: fixed_vals
end module
module find_fit_module
! This module contains a general function find_fit() for a nonlinear least
! squares fitting. The function can fit any nonlinear expression to any data.
use minpack, only: lmdif1
use types, only: dp
implicit none
private
public find_fit
contains
subroutine find_fit(data_x, data_y, expr, pars)
! Fits the (data_x, data_y) arrays with the function expr(x, pars).
! The user can provide any nonlinear function 'expr' depending on any number of
! parameters 'pars' and it must return the evaluated expression on the
! array 'x'. The arrays 'data_x' and 'data_y' must have the same
! length.
real(dp), intent(in) :: data_x(:), data_y(:)
interface
function expr(x, pars) result(y)
use types, only: dp
implicit none
real(dp), intent(in) :: x(:), pars(:)
real(dp) :: y(size(x))
end function
end interface
real(dp), intent(inout) :: pars(:)
real(dp) :: tol, fvec(size(data_x))
integer :: iwa(size(pars)), info, m, n
real(dp), allocatable :: wa(:)
tol = sqrt(epsilon(1._dp))
!tol = 0.001
m = size(fvec)
n = size(pars)
allocate(wa(m*n + 5*n + m))
call lmdif1(fcn, m, n, pars, fvec, tol, info, iwa, wa, size(wa))
open(222, FILE='D_Value.txt')
write(222,4) pars(1)
4 format(E20.12)
close(222)
if (info /= 1) stop "failed to converge"
contains
subroutine fcn(m, n, x, fvec, iflag)
integer, intent(in) :: m, n, iflag
real(dp), intent(in) :: x(n)
real(dp), intent(out) :: fvec(m)
! Suppress compiler warning:
fvec(1) = iflag
fvec = data_y - expr(data_x, x)
end subroutine
end subroutine
end module
program snwdeplcrv
! Find a nonlinear fit of the form y = (A - D) / (1 + (x**B/C)) + D.
use find_fit_module, only: find_fit
use types, only: dp
use f_vals
implicit none
real(dp) :: pars(1), y_int_at_1
real(dp) :: y(1) = 1.0 ! Initialization of value to be reset by user (y: value of S-curve # x=1)
real(dp) :: A, B, C
integer :: i
allocate(fixed_vals(3)) ! A, B, C parameters
pars = [1._dp] ! D parameter in S-curve function
! Read PEST-specified parameters
write(*,*) ' Enter value that S-curve should equal when SWE=1 (must be <= 1)'
read(*,*) y_int_at_1
if(y_int_at_1 > 1.0) y_int_at_1 = 1
y = y_int_at_1
! Read PEST-specified parameters
write(*,*) ' Enter S-curve parameters: A, B, & C. D parameter to be estimated '
read(*,*) A, B, C
fixed_vals(1) = A
fixed_vals(2) = B
fixed_vals(3) = C
call find_fit([(real(i, dp), i=1,size(y))], y, expression, pars)
print *, pars
contains
function expression(x, pars) result(y)
use f_vals
real(dp), intent(in) :: x(:), pars(:)
real(dp) :: y(size(x))
real(dp) :: A, B, C, D
A = fixed_vals(1)
B = fixed_vals(2)
C = fixed_vals(3)
D = pars(1)
y = (A - D) / (1 + (x**B / C)) + D
end function
end program