@@ -50,44 +50,48 @@ class OptimalControlProblem():
5050 integral_cost : callable
5151 Function that returns the integral cost given the current state
5252 and input. Called as integral_cost(x, u).
53- trajectory_constraints : list oftuples , optional
53+ trajectory_constraints : list ofconstraints , optional
5454 List of constraints that should hold at each point in the time
55- vector. Each element of the list should consist of a tuple with
56- first element given by :meth:`~scipy.optimize.LinearConstraint` or
57- :meth:`~scipy.optimize.NonlinearConstraint` and the remaining
58- elements of the tuple are the arguments that would be passed to
59- those functions. The constraints will be applied at each time
60- point along the trajectory.
55+ vector. Each element of the list should be an object of type
56+ :class:`~scipy.optimize.LinearConstraint` with arguments `(A, lb,
57+ ub)` or :class:`~scipy.optimize.NonlinearConstraint` with arguments
58+ `(fun, lb, ub)`. The constraints will be applied at each time point
59+ along the trajectory.
6160 terminal_cost : callable, optional
6261 Function that returns the terminal cost given the current state
6362 and input. Called as terminal_cost(x, u).
64- initial_guess : 1D or 2D array_like
65- Initial inputs to use as a guess for the optimal input. The
66- inputs should either be a 2D vector of shape (ninputs, horizon)
67- or a 1D input of shape (ninputs,) that will be broadcast by
68- extension of the time axis.
6963 trajectory_method : string, optional
7064 Method to use for carrying out the optimization. Currently supported
7165 methods are 'shooting' and 'collocation' (continuous time only). The
7266 default value is 'shooting' for discrete time systems and
7367 'collocation' for continuous time systems
68+ initial_guess : (tuple of) 1D or 2D array_like
69+ Initial states and/or inputs to use as a guess for the optimal
70+ trajectory. For shooting methods, an array of inputs for each time
71+ point should be specified. For collocation methods, the initial
72+ guess is either the input vector or a tuple consisting guesses for
73+ the state and the input. Guess should either be a 2D vector of
74+ shape (ninputs, ntimepts) or a 1D input of shape (ninputs,) that
75+ will be broadcast by extension of the time axis.
7476 log : bool, optional
7577 If `True`, turn on logging messages (using Python logging module).
76- Use ``logging.basicConfig`` to enable logging output (e.g., to a file).
77- kwargs : dict, optional
78- Additional parameters (passed to :func:`scipy.optimal.minimize`).
78+ Use :py:func:`logging.basicConfig` to enable logging output
79+ (e.g., to a file).
7980
8081 Returns
8182 -------
8283 ocp : OptimalControlProblem
8384 Optimal control problem object, to be used in computing optimal
8485 controllers.
8586
86- Additional parameters
87- ---------------------
87+ Other Parameters
88+ ----------------
8889 basis : BasisFamily, optional
8990 Use the given set of basis functions for the inputs instead of
9091 setting the value of the input at each point in the timepts vector.
92+ terminal_constraints : list of constraints, optional
93+ List of constraints that should hold at the terminal point in time,
94+ in the same form as `trajectory_constraints`.
9195 solve_ivp_method : str, optional
9296 Set the method used by :func:`scipy.integrate.solve_ivp`.
9397 solve_ivp_kwargs : str, optional
@@ -182,20 +186,6 @@ def __init__(
182186if kwargs :
183187raise TypeError ("unrecognized keyword(s): " ,str (kwargs ))
184188
185- # Process trajectory constraints
186- def _process_constraints (constraint_list ,name ):
187- if isinstance (constraint_list ,tuple ):
188- constraint_list = [constraint_list ]
189- elif not isinstance (constraint_list ,list ):
190- raise TypeError (f"{ name } )
191-
192- # Make sure that we recognize all of the constraint types
193- for ctype ,fun ,lb ,ub in constraint_list :
194- if not ctype in [opt .LinearConstraint ,opt .NonlinearConstraint ]:
195- raise TypeError (f"unknown{ name } { ctype } )
196-
197- return constraint_list
198-
199189self .trajectory_constraints = _process_constraints (
200190trajectory_constraints ,"trajectory" )
201191self .terminal_constraints = _process_constraints (
@@ -1017,9 +1007,6 @@ def solve_ocp(
10171007 If True, assume that 2D input arrays are transposed from the standard
10181008 format. Used to convert MATLAB-style inputs to our format.
10191009
1020- kwargs : dict, optional
1021- Additional parameters (passed to :func:`scipy.optimal.minimize`).
1022-
10231010 Returns
10241011 -------
10251012 res : OptimalControlResult
@@ -1456,3 +1443,45 @@ def _evaluate_output_range_constraint(x, u):
14561443
14571444# Return a nonlinear constraint object based on the polynomial
14581445return (opt .NonlinearConstraint ,_evaluate_output_range_constraint ,lb ,ub )
1446+
1447+ #
1448+ # Utility functions
1449+ #
1450+
1451+ #
1452+ # Process trajectory constraints
1453+ #
1454+ # Constraints were originally specified as a tuple with the type of
1455+ # constraint followed by the arguments. However, they are now specified
1456+ # directly as SciPy constraint objects.
1457+ #
1458+ # The _process_constraints() function will covert everything to a consistent
1459+ # internal representation (currently a tuple with the constraint type as the
1460+ # first element.
1461+ #
1462+ def _process_constraints (clist ,name ):
1463+ if isinstance (
1464+ clist , (tuple ,opt .LinearConstraint ,opt .NonlinearConstraint )):
1465+ clist = [clist ]
1466+ elif not isinstance (clist ,list ):
1467+ raise TypeError (f"{ name } )
1468+
1469+ # Process individual list elements
1470+ constraint_list = []
1471+ for constraint in clist :
1472+ if isinstance (constraint ,tuple ):
1473+ # Original style of constraint
1474+ ctype ,fun ,lb ,ub = constraint
1475+ if not ctype in [opt .LinearConstraint ,opt .NonlinearConstraint ]:
1476+ raise TypeError (f"unknown{ name } { ctype } )
1477+ constraint_list .append (constraint )
1478+ elif isinstance (constraint ,opt .LinearConstraint ):
1479+ constraint_list .append (
1480+ (opt .LinearConstraint ,constraint .A ,
1481+ constraint .lb ,constraint .ub ))
1482+ elif isinstance (constraint ,opt .NonlinearConstraint ):
1483+ constraint_list .append (
1484+ (opt .NonlinearConstraint ,constraint .fun ,
1485+ constraint .lb ,constraint .ub ))
1486+
1487+ return constraint_list