small tweaks to comments, docstrings, unit tests

murrayrm · murrayrm · commit 574cf6982241 · 2023-03-18T10:33:23.000-07:00
diff --git a/control/iosys.py b/control/iosys.py
@@ -2727,12 +2727,16 @@ def interconnect(
         System name (used for specifying signals). If unspecified, a generic
         name <sys[id]> is generated with a unique integer id.
 
-    check_unused : bool
+    check_unused : bool, optional
         If True, check for unused sub-system signals.  This check is
         not done if connections is False, and neither input nor output
         mappings are specified.
 
-    ignore_inputs : list of input-spec
+    add_unused : bool, optional
+        If True, subsystem signals that are not connected to other components
+        are added as inputs and outputs of the interconnected system.
+
+    ignore_inputs : list of input-spec, optional
         A list of sub-system inputs known not to be connected.  This is
         *only* used in checking for unused signals, and does not
         disable use of the input.
@@ -2742,7 +2746,7 @@ def interconnect(
         signals from all sub-systems with that base name are
         considered ignored.
 
-    ignore_outputs : list of output-spec
+    ignore_outputs : list of output-spec, optional
         A list of sub-system outputs known not to be connected.  This
         is *only* used in checking for unused signals, and does not
         disable use of the output.
@@ -2752,7 +2756,7 @@ def interconnect(
         outputs from all sub-systems with that base name are
         considered ignored.
 
-    warn_duplicate : None, True, or False
+    warn_duplicate : None, True, or False, optional
         Control how warnings are generated if duplicate objects or names are
         detected.  In `None` (default), then warnings are generated for
         systems that have non-generic names.  If `False`, warnings are not
diff --git a/control/optimal.py b/control/optimal.py
@@ -104,14 +104,14 @@ class OptimalControlProblem():
 
     Notes
     -----
-    To describe an optimal control problem we need an input/output system, a
-    time horizon, a cost function, and (optionally) a set of constraints on
-    the state and/or input, either along the trajectory and at the terminal
-    time.  This class sets up an optimization over the inputs at each point in
-    time, using the integral and terminal costs as well as the trajectory and
-    terminal constraints.  The `compute_trajectory` method sets up an
-    optimization problem that can be solved using
-    :func:`scipy.optimize.minimize`.
+    To describe an optimal control problem we need an input/output system,
+    a set of time points over a a fixed horizon, a cost function, and
+    (optionally) a set of constraints on the state and/or input, either
+    along the trajectory and at the terminal time.  This class sets up an
+    optimization over the inputs at each point in time, using the integral
+    and terminal costs as well as the trajectory and terminal constraints.
+    The `compute_trajectory` method sets up an optimization problem that
+    can be solved using :func:`scipy.optimize.minimize`.
 
     The `_cost_function` method takes the information computes the cost of the
     trajectory generated by the proposed input.  It does this by calling a
diff --git a/control/tests/statefbk_test.py b/control/tests/statefbk_test.py
@@ -512,7 +512,7 @@ def test_lqr_discrete(self):
             K, S, E = ct.dlqr(csys, Q, R)
 
     @pytest.mark.parametrize(
-        'nstates, noutputs, ninputs, nintegrators, type',
+        'nstates, noutputs, ninputs, nintegrators, type_',
         [(2,      0,        1,       0,            None),
          (2,      1,        1,       0,            None),
          (4,      0,        2,       0,            None),
@@ -525,7 +525,7 @@ def test_lqr_discrete(self):
          (4,      3,        2,       2,            'nonlinear'),
         ])
     def test_statefbk_iosys(
-            self, nstates, ninputs, noutputs, nintegrators, type):
+            self, nstates, ninputs, noutputs, nintegrators, type_):
         # Create the system to be controlled (and estimator)
         # TODO: make sure it is controllable?
         if noutputs == 0:
@@ -571,14 +571,14 @@ def test_statefbk_iosys(
         # Create an I/O system for the controller
         ctrl, clsys = ct.create_statefbk_iosystem(
             sys, K, integral_action=C_int, estimator=est,
-            controller_type=type, name=type)
+            controller_type=type_, name=type_)
 
         # Make sure the name got set correctly
-        if type is not None:
-            assert ctrl.name == type
+        if type_ is not None:
+            assert ctrl.name == type_
 
         # If we used a nonlinear controller, linearize it for testing
-        if type == 'nonlinear':
+        if type_ == 'nonlinear':
             clsys = clsys.linearize(0, 0)
 
         # Make sure the linear system elements are correct
