Description
Discussed in #781
Originally posted by zhengzl18 October 17, 2022
Hi,
I'm running the example code of the optimal module: https://python-control.readthedocs.io/en/0.9.2/optimal.html#example
When calling the solve_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 all num 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!