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Description
Discussed in#781
Originally posted byzhengzl18 October 17, 2022
Hi,
I'm running the example code of theoptimal module:https://python-control.readthedocs.io/en/0.9.2/optimal.html#example
When calling thesolve_ocp method, a parameter called "horizon" is required:
horizon = np.linspace(0, Tf, 3, endpoint=True)result = opt.solve_ocp( vehicle, horizon, x0, traj_cost, constraints, terminal_cost=term_cost, initial_guess=u0)In this setting, it works just fine.
But when I tried to change the horizon into something like (changing nothing but the number of points in the linspace):
horizon = np.linspace(0, Tf, 4, endpoint=True)the result seemed to be extremely strange, with the inputs u always remaining the same:
Summary statistics:* Cost function calls: 9* Constraint calls: 20* System simulations: 27* Final cost: 16000.0 cost: 16000.0 fun: 16000.0 inputs: array([[10., 10., 10., 10.], [ 0., 0., 0., 0.]]) jac: array([ 0.00000000e+00, 2.44140625e-04, 1.22070312e-04, 0.00000000e+00, -3.62099047e+06, -6.84054738e+06, -2.12256850e+06, -7.49211857e+05]) message: 'Optimization terminated successfully' nfev: 9 nit: 5 njev: 1 problem: <control.optimal.OptimalControlProblem object at 0x7ff0cb281df0> states: array([[ 0. , 33.33333333, 66.66666667, 100. ], [ -2. , -2. , -2. , -2. ], [ 0. , 0. , 0. , 0. ]]) status: 0 success: True time: array([ 0. , 3.33333333, 6.66666667, 10. ]) x: array([10., 10., 10., 10., 0., 0., 0., 0.])It's the case for allnum except 3 in the np.linspace(0, Tf, num).
I'm confused by this results and wonder what's the actual use of 'horizon'? (I do notice from the comments that it's a "List of times at which the optimal input should be computed", but I still can't understand)
Hope for any help.
Thanks!