In this setting, it works just fine.
\nBut when I tried to change the horizon into something like (changing nothing but the number of points in the linspace):
\nhorizon = np.linspace(0, Tf, 4, endpoint=True)\nthe result seemed to be extremely strange, with the inputs u always remaining the same:
\nSummary statistics:\n* Cost function calls: 9\n* Constraint calls: 20\n* System simulations: 27\n* Final cost: 16000.0\n cost: 16000.0\n fun: 16000.0\n inputs: array([[10., 10., 10., 10.],\n [ 0., 0., 0., 0.]])\n jac: array([ 0.00000000e+00, 2.44140625e-04, 1.22070312e-04, 0.00000000e+00,\n -3.62099047e+06, -6.84054738e+06, -2.12256850e+06, -7.49211857e+05])\n message: 'Optimization terminated successfully'\n nfev: 9\n nit: 5\n njev: 1\n problem: <control.optimal.OptimalControlProblem object at 0x7ff0cb281df0>\n states: array([[ 0. , 33.33333333, 66.66666667, 100. ],\n [ -2. , -2. , -2. , -2. ],\n [ 0. , 0. , 0. , 0. ]])\n status: 0\n success: True\n time: array([ 0. , 3.33333333, 6.66666667, 10. ])\n x: array([10., 10., 10., 10., 0., 0., 0., 0.])\n\nIt'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)
\nHope for any help.
\nThanks!
","upvoteCount":1,"answerCount":3,"acceptedAnswer":{"@type":"Answer","text":"This problem is addressed in PR #799 by switching to using collocation as the default method for solving optimal control problems. This is much more numerically stable and provides good solutions to the example problem from the documentation (and without the issues are the solution only working for a specific number of time points).
","upvoteCount":1,"url":"https://github.com/python-control/python-control/discussions/781#discussioncomment-4240712"}}}-
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Hi, 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): the result seemed to be extremely strange, with the inputs u always remaining the same: It's the case for all 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! |
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Replies: 3 comments 1 reply
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I am struggling with the same problem. |
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I've got this on my list to take a look at, but haven't had a chance. One quick thing is that if you want to change the parameters sent to the optimizer, use the |
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A bit more information: the problem seems to be with the default method used by If you switch the solver to The (abridged) result is now The cost is now I'll keep looking at this, but the quick (but slow -:) fix is to use a different optimization method. BTW, the much faster way to solve this problem is to exploit the fact that the system is differentially flat: https://python-control.readthedocs.io/en/0.9.2/kincar-flatsys.html. Approach #3 in the example script is solving the same problem and returns a result instantaneously. |
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This problem is addressed in PR #799 by switching to using collocation as the default method for solving optimal control problems. This is much more numerically stable and provides good solutions to the example problem from the documentation (and without the issues are the solution only working for a specific number of time points). |
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This problem is addressed in PR #799 by switching to using collocation as the default method for solving optimal control problems. This is much more numerically stable and provides good solutions to the example problem from the documentation (and without the issues are the solution only working for a specific number of time points).