@@ -897,7 +897,8 @@ def __init__(
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def solve_ocp (
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sys , horizon , X0 , cost , trajectory_constraints = None , terminal_cost = None ,
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terminal_constraints = [], initial_guess = None , basis = None , squeeze = None ,
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- transpose = None , return_states = True , log = False , ** kwargs ):
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+ transpose = None , return_states = True , print_summary = True , log = False ,
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+ ** kwargs ):
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"""Compute the solution to an optimal control problem
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@@ -951,6 +952,9 @@ def solve_ocp(
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log : bool, optional
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If `True`, turn on logging messages (using Python logging module).
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+ print_summary : bool, optional
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+ If `True` (default), print a short summary of the computation.
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+
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return_states : bool, optional
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If True, return the values of the state at each time (default = True).
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@@ -1017,7 +1021,8 @@ def solve_ocp(
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# Solve for the optimal input from the current state
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return ocp .compute_trajectory (
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- X0 , squeeze = squeeze , transpose = transpose , return_states = return_states )
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+ X0 , squeeze = squeeze , transpose = transpose , print_summary = print_summary ,
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+ return_states = return_states )
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# Create a model predictive controller for an optimal control problem
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