athulpg007
commented
6 years ago I have been able to solve the above mentioned issue. The problem was that I used
prob.dynamics = [dynamics, dynamics] prob.knot_states_smooth = [False]
where I should have used,
prob.dynamics = [dynamics, dynamics, dynamics, dynamics] prob.knot_states_smooth = [False, False, False]
since I have four "sections" and three "knots" in the problem.
Hi there!
I have been trying to extend the 12_two_stage_rocket problem in OpenGoddard examples to solve a 3DoF Delta III launch problem described in this article.
I have read in earlier issues here that the developer is aware of some convergence issues. However, I am having some difficulty getting the solver to accept the problem set up. My problem formulation source code is attached here along with this issue.delta-iii-problem.txt
I am faced with the error message before even starting iterations, and cannot seem to find what might be wrong in the code! Can the developer kindly take a look at the code or suggest any reasons why I am running into this issue? Thanks!
---- iteration : 1 ----
IndexErrorTraceback (most recent call last) C:\Users\athul\Dropbox\Codes\OpenGoddard\OpenGoddard-master\examples\delta-iii-problem-lab.py in()
654 print("final time : {0:.3f}".format(tf))
655
--> 656 prob.solve(obj, display_func, ftol=1e-3)
657
658
C:\Users\athul\AppData\Local\Enthought\Canopy\edm\envs\User\lib\site-packages\OpenGoddard\optimize.pyc in solve(self, obj, display_func, options)
679 options={"disp": True,
680 "maxiter": maxiter,
--> 681 "ftol": ftol})
682 print(opt.message)
683 display_func()
C:\Users\athul\AppData\Local\Enthought\Canopy\edm\envs\User\lib\site-packages\scipy\optimize_minimize.pyc in minimize(fun, x0, args, method, jac, hess, hessp, bounds, constraints, tol, callback, options)
456 elif meth == 'slsqp':
457 return _minimize_slsqp(fun, x0, args, jac, bounds,
--> 458 constraints, callback=callback, options)
459 elif meth == 'dogleg':
460 return _minimize_dogleg(fun, x0, args, jac, hess,
C:\Users\athul\AppData\Local\Enthought\Canopy\edm\envs\User\lib\site-packages\scipy\optimize\slsqp.pyc in _minimize_slsqp(func, x0, args, jac, bounds, constraints, maxiter, ftol, iprint, disp, eps, callback, *unknown_options)
309 # Set the parameters that SLSQP will need
310 # meq, mieq: number of equality and inequality constraints
--> 311 meq = sum(map(len, [atleast_1d(c['fun'](x, c['args'])) for c in cons['eq']]))
312 mieq = sum(map(len, [atleast_1d(c['fun'](x, c['args'])) for c in cons['ineq']]))
313 # m = The total number of constraints
C:\Users\athul\AppData\Local\Enthought\Canopy\edm\envs\User\lib\site-packages\OpenGoddard\optimize.pyc in for_solver(p, arg0, arg1)
645 def for_solver(p, arg0, arg1):
646 self.p = p
--> 647 return func(arg0, arg1)
648 return for_solver
649
C:\Users\athul\AppData\Local\Enthought\Canopy\edm\envs\User\lib\site-packages\OpenGoddard\optimize.pyc in equality_add(equality_func, obj)
616 tix = self.time_start(i) / self.unit_time
617 tfx = self.time_final(i) / self.unit_time
--> 618 dx = self.dynamics[i](self, obj, i)
619 result = np.hstack((result, derivative - (tfx - tix) / 2.0 dx))
620
IndexError: list index out of range