@@ -25,7 +25,7 @@ class OptimalControlProblem():
2525"""Description of a finite horizon, optimal control problem.
2626
2727 The `OptimalControlProblem` class holds all of the information required to
28- specifyand optimal control problem: the system dynamics, cost function,
28+ specifyan optimal control problem: the system dynamics, cost function,
2929 and constraints. As much as possible, the information used to specify an
3030 optimal control problem matches the notation and terminology of the SciPy
3131 `optimize.minimize` module, with the hope that this makes it easier to
@@ -94,13 +94,13 @@ class OptimalControlProblem():
9494 The `_cost_function` method takes the information computes the cost of the
9595 trajectory generated by the proposed input. It does this by calling a
9696 user-defined function for the integral_cost given the current states and
97- inputs at each point along thetrajetory and then adding the value of a
97+ inputs at each point along thetrajectory and then adding the value of a
9898 user-defined terminal cost at the final pint in the trajectory.
9999
100100 The `_constraint_function` method evaluates the constraint functions along
101101 the trajectory generated by the proposed input. As in the case of the
102102 cost function, the constraints are evaluated at the state and input along
103- each point on thetrjectory . This information is compared against the
103+ each point on thetrajectory . This information is compared against the
104104 constraint upper and lower bounds. The constraint function is processed
105105 in the class initializer, so that it only needs to be computed once.
106106
@@ -567,7 +567,7 @@ def _process_initial_guess(self, initial_guess):
567567#
568568# Initially guesses from the user are passed as input vectors as a
569569# function of time, but internally we store the guess in terms of the
570- # basis coefficients. We do this by solving a least squaresprobelm to
570+ # basis coefficients. We do this by solving a least squaresproblem to
571571# find coefficients that match the input functions at the time points (as
572572# much as possible, if the problem is under-determined).
573573#
@@ -880,7 +880,7 @@ def solve_ocp(
880880 Function that returns the terminal cost given the current state
881881 and input. Called as terminal_cost(x, u).
882882
883- terminal_constraint : list of tuples, optional
883+ terminal_constraints : list of tuples, optional
884884 List of constraints that should hold at the end of the trajectory.
885885 Same format as `constraints`.
886886
@@ -914,7 +914,7 @@ def solve_ocp(
914914 res : OptimalControlResult
915915 Bundle object with the results of the optimal control problem.
916916
917- res.success: bool
917+ res.success
918918 Boolean flag indicating whether the optimization was successful.
919919
920920 res.time : array
@@ -982,7 +982,7 @@ def create_mpc_iosystem(
982982 Function that returns the terminal cost given the current state
983983 and input. Called as terminal_cost(x, u).
984984
985- terminal_constraint : list of tuples, optional
985+ terminal_constraints : list of tuples, optional
986986 List of constraints that should hold at the end of the trajectory.
987987 Same format as `constraints`.
988988
@@ -992,7 +992,7 @@ def create_mpc_iosystem(
992992 Returns
993993 -------
994994 ctrl : InputOutputSystem
995- An I/O system taking thecurrrent state of the model system and
995+ An I/O system taking thecurrent state of the model system and
996996 returning the current input to be applied that minimizes the cost
997997 function while satisfying the constraints.
998998
@@ -1039,9 +1039,9 @@ def quadratic_cost(sys, Q, R, x0=0, u0=0):
10391039 R : 2D array_like
10401040 Weighting matrix for input cost. Dimensions must match system input.
10411041 x0 : 1D array
1042- Nomimal value of the system state (for which cost should be zero).
1042+ Nominal value of the system state (for which cost should be zero).
10431043 u0 : 1D array
1044- Nomimal value of the system input (for which cost should be zero).
1044+ Nominal value of the system input (for which cost should be zero).
10451045
10461046 Returns
10471047 -------
@@ -1082,7 +1082,7 @@ def quadratic_cost(sys, Q, R, x0=0, u0=0):
10821082# As in the cost function evaluation, the main "trick" in creating a constrain
10831083# on the state or input is to properly evaluate the constraint on the stacked
10841084# state and input vector at the current time point. The constraint itself
1085- # will be called at eachpoing along the trajectory (or the endpoint) via the
1085+ # will be called at eachpoint along the trajectory (or the endpoint) via the
10861086# constrain_function() method.
10871087#
10881088# Note that these functions to not actually evaluate the constraint, they
@@ -1250,7 +1250,7 @@ def input_range_constraint(sys, lb, ub):
12501250def output_poly_constraint (sys ,A ,b ):
12511251"""Create output constraint from polytope
12521252
1253- Creates a linear constraint on the systemouput of the form A y <= b that
1253+ Creates a linear constraint on the systemoutput of the form A y <= b that
12541254 can be used as an optimal control constraint (trajectory or terminal).
12551255
12561256 Parameters