diff --git a/CITATION.cff b/CITATION.cff
index 9d40ccc..38f6d91 100644
--- a/CITATION.cff
+++ b/CITATION.cff
@@ -14,5 +14,5 @@ authors:
     orcid: "https://orcid.org/0000-0002-1987-9268"
     affiliation: "McGill University"
 title: "decargroup/dkpy"
-version: v0.1.12
+version: v0.1.9
 url: "https://github.com/decargroup/dkpy"
diff --git a/README.rst b/README.rst
index 677ff7b..40bf832 100644
--- a/README.rst
+++ b/README.rst
@@ -141,6 +141,6 @@ If you use this software in your research, please cite it as below or see
         url={https://github.com/decargroup/dkpy},
         publisher={Zenodo},
         author={Steven Dahdah and Timothy Everett Adams and James Richard Forbes},
-        version = {{v0.1.12}},
+        version = {{v0.1.9}},
         year={2025},
     }
diff --git a/doc/_static/example_8/8_magnitude_uncertainty_weight.png b/doc/_static/example_8/8_magnitude_uncertainty_weight.png
new file mode 100644
index 0000000..e3d1718
Binary files /dev/null and b/doc/_static/example_8/8_magnitude_uncertainty_weight.png differ
diff --git a/doc/_static/example_8/8_sval_actuator.png b/doc/_static/example_8/8_sval_actuator.png
new file mode 100644
index 0000000..118170b
Binary files /dev/null and b/doc/_static/example_8/8_sval_actuator.png differ
diff --git a/doc/_static/example_8/8_sval_residual_max.png b/doc/_static/example_8/8_sval_residual_max.png
new file mode 100644
index 0000000..a7b44df
Binary files /dev/null and b/doc/_static/example_8/8_sval_residual_max.png differ
diff --git a/doc/conf.py b/doc/conf.py
index df5b6ff..7348137 100644
--- a/doc/conf.py
+++ b/doc/conf.py
@@ -7,10 +7,10 @@
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
 
 project = "dkpy"
-copyright = "2024, Steven Dahdah, Timothy Everett Adams and James Richard Forbes"
+copyright = "2025, Steven Dahdah, Timothy Everett Adams and James Richard Forbes"
 author = "Steven Dahdah, Timothy Everett Adams and James Richard Forbes"
-version = "0.1.12"
-release = "0.1.12"
+version = "0.1.9"
+release = "0.1.9"
 
 # -- General configuration ---------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
diff --git a/doc/examples.rst b/doc/examples.rst
index e3ef9bc..a1326a7 100644
--- a/doc/examples.rst
+++ b/doc/examples.rst
@@ -178,13 +178,14 @@ particular, the response of the system states and actuator inputs are shown.
 
 .. image:: _static/example_6/6_plot_inputs.png
 
-Multi-Model Uncertainty Characterization
-----------------------------------------
+Multi-Model Uncertainty Characterization: Academic Model
+--------------------------------------------------------
 
-In this example, an unstructured uncertainty set is characterized from a set
-of nominal and off-nominal frequency responses. The off-nominal models are
-generated from a nominal model by randomly perturbing the parameters within a
-known bound.
+In this example, an unstructured uncertainty set is characterized from a set of
+nominal and off-nominal frequency responses. This is an academic example as the
+uncertain model in this problem is arbitrarily selected for the purpose of
+demonstration. The off-nominal models are generated from a nominal model by
+randomly perturbing the parameters within a known bound.
 
 .. literalinclude:: ../examples/7_uncertainty_characterization.py
    :language: python
@@ -240,7 +241,35 @@ set. The optimal weight frequency responses and fitted weights are shown below.
 
 .. image:: _static/example_7/7_uncertainty_weight.png
 
+Multi-Model Uncertainty Characterization: Aircraft Actuator Model
+-----------------------------------------------------------------
 
+In this example, an unstructured uncertainty set is characterized from a set of
+nominal and off-nominal frequency responses. In this example, the uncertainty
+of an aircraft actuator model is characterized using an actuator model from an
+example given in Section 14.1 of [M04]_. 
+
+.. literalinclude:: ../examples/8_aircraft_actuator_uncertainty_characterization.py
+   :language: python
+
+The off-nominal frequency response data is generated by sampling valid
+perturbations from the uncertainty model provided in the problem statement.
+
+.. image:: _static/example_8/8_sval_actuator.png
+
+Three different candidate uncertainty models are evaluated: additive
+uncertainty, multiplicative input uncertainty, and inverse multiplicative
+input uncertainty. The multiplicative input uncertainty is selected as it
+yields the smallest residual singular values.
+
+.. image:: _static/example_8/8_sval_residual_max.png
+
+The left uncertainty weight is constrained to be scalar whereas the right uncertainty
+weight is constrained to the identity matrix. Given that the right uncertainty
+weight is the identity matrix, a fit does not need to be performed for this weight
+as it will be neglected in the generalized plant.
+
+.. image:: _static/example_8/8_magnitude_uncertainty_weight.png
 
 
 
diff --git a/examples/6_dk_iteration_non_square_perturbation.py b/examples/6_dk_iteration_non_square_perturbation.py
index e5877d2..385c152 100644
--- a/examples/6_dk_iteration_non_square_perturbation.py
+++ b/examples/6_dk_iteration_non_square_perturbation.py
@@ -57,7 +57,7 @@ def example_dk_iter_list_order_aircraft():
     # DK-iteration controller synthesis
     dk_iter = dkpy.DkIterListOrder(
         controller_synthesis=dkpy.HinfSynLmi(
-            lmi_strictness=5e-7,  # Have to play with tolerances.
+            lmi_strictness=5e-7,
             solver_params=dict(
                 solver="MOSEK",
                 eps=5e-8,
@@ -74,7 +74,7 @@ def example_dk_iter_list_order_aircraft():
             ),
         ),
         d_scale_fit=dkpy.DScaleFitSlicot(),
-        fit_orders=[4, 4],
+        fit_orders=[4, 4, 4],
     )
     # Alternative MATLAB block structure description
     # block_structure = np.array([[n_u_delta, n_y_delta], [n_w, n_z]])
diff --git a/examples/7_uncertainty_characterization.py b/examples/7_uncertainty_characterization.py
index 0154fa1..eec2817 100644
--- a/examples/7_uncertainty_characterization.py
+++ b/examples/7_uncertainty_characterization.py
@@ -8,33 +8,12 @@
 def example_uncertainty_characterization():
     """Multi-model uncertainty characterization from frequency response data."""
 
-    # Generate example data
+    # Load example data
     eg = dkpy.example_multimodel_uncertainty()
     response_nom = eg["complex_response_nominal"]
     response_offnom_list = eg["complex_response_offnominal_list"]
     omega = eg["omega"]
 
-    # Plot: Magnitude response of nominal and off-nominal systems
-    fig, _ = dkpy.plot_magnitude_response_uncertain_model_set(
-        response_nom,
-        response_offnom_list,
-        omega,
-    )
-
-    # Plot: Phase response of nominal and off-nominal systems
-    fig, _ = dkpy.plot_phase_response_uncertain_model_set(
-        response_nom,
-        response_offnom_list,
-        omega,
-    )
-
-    # Plot: Singular value response of nominal and off-nominal systems
-    fig, ax = dkpy.plot_singular_value_response_uncertain_model_set(
-        response_nom,
-        response_offnom_list,
-        omega,
-    )
-
     # Uncertainty models
     uncertainty_models = {
         "additive",
@@ -44,41 +23,58 @@ def example_uncertainty_characterization():
         "inverse_multiplicative_input",
         "inverse_multiplicative_output",
     }
+
+    # Compute uncertainty residual frequency response
     response_residuals_dict = dkpy.compute_uncertainty_residual_response(
         response_nom,
         response_offnom_list,
         uncertainty_models,
     )
 
-    # Plot: Singular value response of uncerainty residuals
-    figure_dict = dkpy.plot_singular_value_response_residual(
-        response_residuals_dict, omega
-    )
-
-    # Plot: Comparison of singular value response of uncerainty residuals for each
-    # uncertainty model
-    fig, _ = dkpy.plot_singular_value_response_residual_comparison(
-        response_residuals_dict, omega
-    )
-
     # Compute uncertainty weight frequency response
     response_weight_left, response_weight_right = (
-        dkpy.compute_optimal_uncertainty_weight_response(
+        dkpy.compute_uncertainty_weight_response(
             response_residuals_dict["inverse_additive"], "diagonal", "diagonal"
         )
     )
 
     # Fit overbounding stable and minimum-phase uncertainty weight system
-    weight_left = dkpy.fit_overbounding_uncertainty_weight(
-        response_weight_left, omega, [4, 5]
+    weight_left = dkpy.fit_uncertainty_weight(response_weight_left, omega, [4, 5])
+    weight_right = dkpy.fit_uncertainty_weight(response_weight_right, omega, [3, 5])
+
+    # Plot: Magnitude response of nominal and off-nominal systems
+    dkpy.plot_magnitude_response_uncertain_model_set(
+        response_nom,
+        response_offnom_list,
+        omega,
+    )
+
+    # Plot: Phase response of nominal and off-nominal systems
+    dkpy.plot_phase_response_uncertain_model_set(
+        response_nom,
+        response_offnom_list,
+        omega,
     )
-    weight_right = dkpy.fit_overbounding_uncertainty_weight(
-        response_weight_right, omega, [3, 5]
+
+    # Plot: Singular value response of nominal and off-nominal systems
+    dkpy.plot_singular_value_response_uncertain_model_set(
+        response_nom,
+        response_offnom_list,
+        omega,
+    )
+
+    # Plot: Singular value response of uncerainty residuals
+    dkpy.plot_singular_value_response_residual(response_residuals_dict, omega)
+
+    # Plot: Comparison of singular value response of uncerainty residuals for each
+    # uncertainty model
+    dkpy.plot_singular_value_response_residual_comparison(
+        response_residuals_dict, omega
     )
 
     # Plot: Magnitude response of uncertainty weight frequency response and overbounding
     # fit
-    fig, _ = dkpy.plot_magnitude_response_uncertainty_weight(
+    dkpy.plot_magnitude_response_uncertainty_weight(
         response_weight_left,
         response_weight_right,
         omega,
diff --git a/examples/8_aircraft_actuator_uncertainty_characterization.py b/examples/8_aircraft_actuator_uncertainty_characterization.py
new file mode 100644
index 0000000..058b226
--- /dev/null
+++ b/examples/8_aircraft_actuator_uncertainty_characterization.py
@@ -0,0 +1,64 @@
+"""Multi-model aircraft actuator uncertainty characterization from frequency response
+data.
+
+"""
+
+from matplotlib import pyplot as plt
+
+import dkpy
+
+
+def example_aircraft_uncertainty_characterization():
+    """Multi-model aircraft actuator uncertainty characterization from frequency
+    response data.
+    """
+    # Load example data
+    eg = dkpy.example_aircraft_actuator_uncertainty()
+    response_actuator = eg["response_actuator_nominal"]
+    response_actuator_offnom_list = eg["response_actuator_offnominal"]
+    omega = eg["omega"]
+
+    # Compute the residual response for different uncertainty models
+    response_residual = dkpy.compute_uncertainty_residual_response(
+        response_actuator,
+        response_actuator_offnom_list,
+        {"additive", "multiplicative_input", "inverse_multiplicative_input"},
+    )
+
+    # Compute the optimal uncertainty weights with a given structure
+    response_weight_left, response_weight_right = (
+        dkpy.compute_uncertainty_weight_response(
+            response_residual["multiplicative_input"],
+            "scalar",
+            "identity",
+        )
+    )
+
+    # Fit an overbounding LTI system to the optimal uncertainty weight response
+    weight_left = dkpy.fit_uncertainty_weight(response_weight_left, omega, 1)
+
+    # Plot: Singular value response of nominal and off-nominal systems
+    dkpy.plot_singular_value_response_uncertain_model_set(
+        response_actuator, response_actuator_offnom_list, omega, hz=True
+    )
+
+    # Plot: Comparison of singular value response of uncerainty residuals for each
+    # uncertainty model
+    dkpy.plot_singular_value_response_residual_comparison(
+        response_residual, omega, hz=True
+    )
+
+    # Plot: Magnitude response of uncertainty weight frequency response and overbounding
+    # fit
+    dkpy.plot_magnitude_response_uncertainty_weight(
+        response_weight_left,
+        response_weight_right,
+        omega,
+        weight_left=weight_left,
+        hz=True,
+    )
+    plt.show()
+
+
+if __name__ == "__main__":
+    example_aircraft_uncertainty_characterization()
diff --git a/pyproject.toml b/pyproject.toml
index 44b8b2b..bee7ade 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "dkpy"
-version = "0.1.12"
+version = "0.1.9"
 dependencies = [
     "numpy>=1.21.0",
     "scipy>=1.7.0",
diff --git a/src/dkpy/uncertainty_characterization.py b/src/dkpy/uncertainty_characterization.py
index b5b2a9d..f9787cd 100644
--- a/src/dkpy/uncertainty_characterization.py
+++ b/src/dkpy/uncertainty_characterization.py
@@ -2,8 +2,8 @@
 
 __all__ = [
     "compute_uncertainty_residual_response",
-    "compute_optimal_uncertainty_weight_response",
-    "fit_overbounding_uncertainty_weight",
+    "compute_uncertainty_weight_response",
+    "fit_uncertainty_weight",
     "plot_magnitude_response_uncertain_model_set",
     "plot_phase_response_uncertain_model_set",
     "plot_singular_value_response_uncertain_model_set",
@@ -23,6 +23,7 @@
 from typing import List, Optional, Union, Tuple, Dict, Callable, Set, Any
 from matplotlib.figure import Figure
 from matplotlib.axes import Axes
+from matplotlib.legend import Legend
 
 from . import utilities
 
@@ -304,13 +305,6 @@ def _compute_uncertainty_residual_multiplicative_input_freq(
 
     num_inputs = complex_response_nom_freq.shape[1]
     num_outputs = complex_response_nom_freq.shape[0]
-    if num_inputs < num_outputs:
-        warnings.warn(
-            "Multipliative input uncertainty models cannot include all possible "
-            "off-nominal systems when the number of inputs is less than the number of "
-            "outputs. This may result in an error if an uncertainty residual cannot "
-            "be found for a given nominal and off-nominal frequency response matrix."
-        )
 
     A = complex_response_nom_freq
     B = complex_response_offnom_freq - complex_response_nom_freq
@@ -365,13 +359,7 @@ def _compute_uncertainty_residual_multiplicative_output_freq(
 
     num_inputs = complex_response_nom_freq.shape[1]
     num_outputs = complex_response_nom_freq.shape[0]
-    if num_inputs > num_outputs:
-        warnings.warn(
-            "Multipliative output uncertainty models cannot include all possible "
-            "off-nominal systems when the number of outputs is less than the number of "
-            "inputs. This may result in an error if an uncertainty residual cannot "
-            "be found for a given nominal and off-nominal frequency response matrix."
-        )
+
     A = complex_response_nom_freq.T
     B = complex_response_offnom_freq.T - complex_response_nom_freq.T
     X, residues_lstsq, _, _ = scipy.linalg.lstsq(A, B)
@@ -425,13 +413,6 @@ def _compute_uncertainty_residual_inverse_additive_freq(
 
     num_inputs = complex_response_nom_freq.shape[1]
     num_outputs = complex_response_nom_freq.shape[0]
-    if num_inputs != num_outputs:
-        warnings.warn(
-            "Inverse additive uncertainty models cannot include all possible "
-            "off-nominal systems when the number of inputs is not equal to the number "
-            "of outputs. This may result in an error if an uncertainty residual cannot "
-            "be found for a given nominal and off-nominal frequency response matrix."
-        )
 
     A1 = complex_response_offnom_freq
     B1 = complex_response_offnom_freq - complex_response_nom_freq
@@ -492,14 +473,6 @@ def _compute_uncertainty_residual_inverse_multiplicative_input_freq(
 
     num_inputs = complex_response_nom_freq.shape[1]
     num_outputs = complex_response_nom_freq.shape[0]
-    if num_inputs < num_outputs:
-        warnings.warn(
-            "Inverse multipliative input uncertainty models cannot include all "
-            "possible off-nominal systems when the number of inputs is less than the "
-            "number of outputs. This may result in an error if an uncertainty residual "
-            "cannot be found for a given nominal and off-nominal frequency response "
-            "matrix."
-        )
 
     A = complex_response_offnom_freq
     B = complex_response_offnom_freq - complex_response_nom_freq
@@ -554,14 +527,7 @@ def _compute_uncertainty_residual_inverse_multiplicative_output_freq(
 
     num_inputs = complex_response_nom_freq.shape[1]
     num_outputs = complex_response_nom_freq.shape[0]
-    if num_inputs > num_outputs:
-        warnings.warn(
-            "Inverse multipliative output uncertainty models cannot include all "
-            "possible off-nominal systems when the number of outputs is less than the "
-            "number of inputs. This may result in an error if an uncertainty residual "
-            "cannot be found for a given nominal and off-nominal frequency response "
-            "matrix."
-        )
+
     A = complex_response_offnom_freq.T
     B = complex_response_offnom_freq.T - complex_response_nom_freq.T
     X, residues_lstsq, _, _ = scipy.linalg.lstsq(A, B)
@@ -583,7 +549,7 @@ def _compute_uncertainty_residual_inverse_multiplicative_output_freq(
             )
 
 
-def compute_optimal_uncertainty_weight_response(
+def compute_uncertainty_weight_response(
     complex_response_residual_list: Union[np.ndarray, control.FrequencyResponseList],
     weight_left_structure: str,
     weight_right_structure: str,
@@ -591,6 +557,9 @@ def compute_optimal_uncertainty_weight_response(
 ) -> Tuple[np.ndarray, np.ndarray]:
     """Compute the optimal uncertainty weight frequency response.
 
+    The algorithm is based on a modified semidefinite program formulation presented in
+    [#uncertainty_characterization]_.
+
     Parameters
     ----------
     complex_response_residual_list : Union[np.ndarray, control.FrequencyResponseList]
@@ -635,12 +604,19 @@ def compute_optimal_uncertainty_weight_response(
     ...     uncertainty_models,
     ... )
     >>> complex_response_weight_left, complex_response_weight_right = (
-    ...     dkpy.compute_optimal_uncertainty_weight_response(
+    ...     dkpy.compute_uncertainty_weight_response(
     ...         complex_response_residual_dict["multiplicative_input"],
     ...         "diagonal",
     ...         "diagonal",
     ...     )
     ... )
+
+    References
+    ----------
+    .. [#uncertainty_characterization] G. J. Balas, A. K. Packard, and P. J. Seiler,
+    “Uncertain Model Set Calculation from Frequency Domain Data,” Springer eBooks,
+    pp. 89–105, Jan. 2009, doi: https://doi.org/10.1007/978-1-4419-0895-7_6.
+
     """
 
     # Convert frequency response data to expected type
@@ -800,7 +776,7 @@ def _compute_optimal_weight_freq(
     return complex_response_weight_left_freq, complex_response_weight_right_freq
 
 
-def fit_overbounding_uncertainty_weight(
+def fit_uncertainty_weight(
     complex_response_uncertainty_weight: Union[
         np.ndarray, control.FrequencyResponseData
     ],
@@ -812,8 +788,7 @@ def fit_overbounding_uncertainty_weight(
     max_iter_bisection: int = 500,
     num_spec_constr: int = 500,
 ) -> control.StateSpace:
-    """
-    Fit an overbounding stable and minimum-phase state-space uncertainty weight to
+    """Fit an overbounding stable and minimum-phase state-space uncertainty weight to
     frequency response data.
 
     Parameters
@@ -867,16 +842,16 @@ def fit_overbounding_uncertainty_weight(
     ...     uncertainty_models,
     ... )
     >>> complex_response_weight_left, complex_response_weight_right = (
-    ...     dkpy.compute_optimal_uncertainty_weight_response(
+    ...     dkpy.compute_uncertainty_weight_response(
     ...         complex_response_residual_dict["multiplicative_input"],
     ...         "diagonal",
     ...         "diagonal",
     ...     )
     ... )
-    >>> weight_left = dkpy.fit_overbounding_uncertainty_weight(
+    >>> weight_left = dkpy.fit_uncertainty_weight(
     ...     complex_response_weight_left, omega, [4, 5]
     ... )
-    >>> weight_right = dkpy.fit_overbounding_uncertainty_weight(
+    >>> weight_right = dkpy.fit_uncertainty_weight(
     ...     complex_response_weight_right, omega, [3, 5]
     ... )
 
@@ -951,10 +926,26 @@ def fit_overbounding_uncertainty_weight(
 
 
 def _convert_frequency_response_data_to_array(
-    frequency_response: Union[
-        np.ndarray, np.typing.ArrayLike, control.FrequencyResponseData
-    ],
+    frequency_response: Union[np.typing.ArrayLike, control.FrequencyResponseData],
 ) -> np.ndarray:
+    """Convert frequency response data into the expected form.
+
+    Parameters
+    ----------
+    frequency_response : Union[np.typing.ArrayLike, control.FrequencyResponseData],
+        Frequency response data in arbitrary form.
+
+    Returns
+    -------
+    np.ndarray
+        Frequency response data in expected array form.
+
+    Raises
+    ------
+    ValueError
+        The dimensions of the `frequency_response` input are incompatible with the
+        expected form.
+    """
     if isinstance(frequency_response, control.FrequencyResponseData):
         complex_response = np.array(frequency_response.complex, dtype=complex)
 
@@ -980,18 +971,34 @@ def _convert_frequency_response_data_to_array(
 
 
 def _convert_frequency_response_list_to_array(
-    frequency_response_list: Union[
-        np.ndarray, np.typing.ArrayLike, control.FrequencyResponseList
-    ],
+    frequency_response_list: Union[np.typing.ArrayLike, control.FrequencyResponseList],
 ) -> np.ndarray:
+    """Convert list of frequency response data into the expected form.
+
+    Parameters
+    ----------
+    frequency_response_list : Union[np.typing.ArrayLike, control.FrequencyResponseData],
+        Frequency response data in arbitrary form.
+
+    Returns
+    -------
+    np.ndarray
+        Frequency response data list in expected array form.
+
+    Raises
+    ------
+    ValueError
+        The dimensions of the `frequency_response_list` input are incompatible with the
+        expected form.
+    """
     if isinstance(frequency_response_list, control.FrequencyResponseList):
         complex_response_list = []
         for frequency_response in frequency_response_list:
             complex_response = np.array(frequency_response.complex, dtype=complex)
 
-            # SISO `FrequencyResponseData` objects return 1D arrays for frequency response
-            # data and 3D arrays for MIMO systems. `dkpy` uses a 3D array in all cases, so
-            # the 1D array is converted into a 3D array.
+            # SISO `FrequencyResponseData` objects return 1D arrays for frequency
+            # response data and 3D arrays for MIMO systems. `dkpy` uses a 3D array in
+            # all cases, so the 1D array is converted into a 3D array.
             if complex_response.ndim == 1:
                 complex_response = complex_response[None, None, :]
 
@@ -1014,8 +1021,8 @@ def _convert_frequency_response_list_to_array(
 
 
 def plot_magnitude_response_uncertain_model_set(
-    complex_response_nom: np.ndarray,
-    complex_response_offnom_list: np.ndarray,
+    complex_response_nom: Union[np.ndarray, control.FrequencyResponseData],
+    complex_response_offnom_list: Union[np.ndarray, control.FrequencyResponseList],
     omega: np.ndarray,
     db: bool = True,
     hz: bool = False,
@@ -1023,14 +1030,14 @@ def plot_magnitude_response_uncertain_model_set(
     plot_nom_kw: Dict[str, Any] = {},
     plot_offnom_kw: Dict[str, Any] = {},
     subplot_kw: Dict[str, Any] = {},
-) -> Tuple[Figure, Union[Axes, np.ndarray]]:
+) -> Tuple[Figure, Union[Axes, np.ndarray], Legend]:
     """Plot magnitude response of nominal model and set of off-nominal models.
 
     Parameters
     ----------
-    complex_response_nom : np.ndarray
+    complex_response_nom : Union[np.ndarray, control.FrequencyResponseData]
         Frequency response matrices over a grid of frequencies of the nominal system.
-    complex_response_offnom_list : np.ndarray
+    complex_response_offnom_list : Union[np.ndarray, control.FrequencyResponseList]
         Frequency response matrices over a grid of frequencies of the set of off-nominal
         systems.
     omega : np.ndarray
@@ -1054,12 +1061,26 @@ def plot_magnitude_response_uncertain_model_set(
         Keyword arguments for the subplot. See [#subplot_kw]_ for more information on
         the subplot keywords.
 
+    Returns
+    -------
+    Tuple[Figure, Union[Axes, np.ndarray], Legend]
+        Matplotlib Figure object, Axes object (or np.ndarray of Axes objects), and
+        Legend object.
+
     References
     ----------
     .. [#plot_kw] https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html
     .. [#subplot_kw] https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html
     """
 
+    # Convert frequency response data to expected type
+    complex_response_nom = _convert_frequency_response_data_to_array(
+        complex_response_nom
+    )
+    complex_response_offnom_list = _convert_frequency_response_list_to_array(
+        complex_response_offnom_list
+    )
+
     # System paramters
     num_offnom = complex_response_offnom_list.shape[0]
     num_outputs = complex_response_offnom_list.shape[2]
@@ -1116,19 +1137,19 @@ def plot_magnitude_response_uncertain_model_set(
         ax[-1, idx_input].set_xlabel("$f$ (Hz)" if hz else r"$\omega$ (rad/s)")
     handles, labels = ax[0, 0].get_legend_handles_labels()
     legend_dict = dict(zip(labels, handles))
-    fig.legend(
+    legend = fig.legend(
         labels=legend_dict.keys(),
         handles=legend_dict.values(),
         loc="outside lower center",
         ncol=2,
     )
 
-    return fig, ax
+    return fig, ax, legend
 
 
 def plot_phase_response_uncertain_model_set(
-    complex_response_nom: np.ndarray,
-    complex_response_offnom_list: np.ndarray,
+    complex_response_nom: Union[np.ndarray, control.FrequencyResponseData],
+    complex_response_offnom_list: Union[np.ndarray, control.FrequencyResponseList],
     omega: np.ndarray,
     deg: bool = True,
     hz: bool = False,
@@ -1136,14 +1157,14 @@ def plot_phase_response_uncertain_model_set(
     plot_nom_kw: Dict[str, Any] = {},
     plot_offnom_kw: Dict[str, Any] = {},
     subplot_kw: Dict[str, Any] = {},
-) -> Tuple[Figure, Union[Axes, np.ndarray]]:
+) -> Tuple[Figure, Union[Axes, np.ndarray], Legend]:
     """Plot phase response of nominal model and set of off-nominal models.
 
     Parameters
     ----------
-    complex_response_nom : np.ndarray
+    complex_response_nom : Union[np.ndarray, control.FrequencyResponseData]
         Frequency response matrices over a grid of frequencies of the nominal system.
-    complex_response_offnom_list : np.ndarray
+    complex_response_offnom_list : Union[np.ndarray, control.FrequencyResponseList]
         Frequency response matrices over a grid of frequencies of the set of off-nominal
         systems.
     omega : np.ndarray
@@ -1167,12 +1188,26 @@ def plot_phase_response_uncertain_model_set(
         Keyword arguments for the subplot. See [#subplot_kw]_ for more information on
         the subplot keywords.
 
+    Returns
+    -------
+    Tuple[Figure, Union[Axes, np.ndarray], Legend]
+        Matplotlib Figure object, Axes object (or np.ndarray of Axes objects), and
+        Legend object.
+
     References
     ----------
     .. [#plot_kw] https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html
     .. [#subplot_kw] https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html
     """
 
+    # Convert frequency response data to expected type
+    complex_response_nom = _convert_frequency_response_data_to_array(
+        complex_response_nom
+    )
+    complex_response_offnom_list = _convert_frequency_response_list_to_array(
+        complex_response_offnom_list
+    )
+
     # System paramters
     num_offnom = complex_response_offnom_list.shape[0]
     num_outputs = complex_response_offnom_list.shape[2]
@@ -1229,19 +1264,19 @@ def plot_phase_response_uncertain_model_set(
         ax[-1, idx_input].set_xlabel("$f$ (Hz)" if hz else r"$\omega$ (rad/s)")
     handles, labels = ax[0, 0].get_legend_handles_labels()
     legend_dict = dict(zip(labels, handles))
-    fig.legend(
+    legend = fig.legend(
         labels=legend_dict.keys(),
         handles=legend_dict.values(),
         loc="outside lower center",
         ncol=2,
     )
 
-    return fig, ax
+    return fig, ax, legend
 
 
 def plot_singular_value_response_uncertain_model_set(
-    complex_response_nom: np.ndarray,
-    complex_response_offnom_list: np.ndarray,
+    complex_response_nom: Union[np.ndarray, control.FrequencyResponseData],
+    complex_response_offnom_list: Union[np.ndarray, control.FrequencyResponseList],
     omega: np.ndarray,
     db: bool = True,
     hz: bool = False,
@@ -1249,14 +1284,14 @@ def plot_singular_value_response_uncertain_model_set(
     plot_nom_kw: Dict[str, Any] = {},
     plot_offnom_kw: Dict[str, Any] = {},
     subplot_kw: Dict[str, Any] = {},
-) -> Tuple[Figure, Union[Axes, np.ndarray]]:
+) -> Tuple[Figure, Axes, Legend]:
     """Plot singular value response of nominal model and set of off-nominal models.
 
     Parameters
     ----------
-    complex_response_nom : np.ndarray
+    complex_response_nom : Union[np.ndarray, control.FrequencyResponseData]
         Frequency response matrices over a grid of frequencies of the nominal system.
-    complex_response_offnom_list : np.ndarray
+    complex_response_offnom_list : Union[np.ndarray, control.FrequencyResponseList]
         Frequency response matrices over a grid of frequencies of the set of off-nominal
         systems.
     omega : np.ndarray
@@ -1280,12 +1315,26 @@ def plot_singular_value_response_uncertain_model_set(
         Keyword arguments for the subplot. See [#subplot_kw]_ for more information on
         the subplot keywords.
 
+    Returns
+    -------
+    Tuple[Figure, Union[Axes, np.ndarray], Legend]
+        Matplotlib Figure object, Axes object (or np.ndarray of Axes objects), and
+        Legend object.
+
     References
     ----------
     .. [#plot_kw] https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html
     .. [#subplot_kw] https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html
     """
 
+    # Convert frequency response data to expected type
+    complex_response_nom = _convert_frequency_response_data_to_array(
+        complex_response_nom
+    )
+    complex_response_offnom_list = _convert_frequency_response_list_to_array(
+        complex_response_offnom_list
+    )
+
     # System paramters
     num_offnom = complex_response_offnom_list.shape[0]
 
@@ -1330,21 +1379,19 @@ def plot_singular_value_response_uncertain_model_set(
     # Plot settings
     if frequency_log_scale:
         ax.set_xscale("log")
-    ax.set_ylabel(
-        "Singular Value Magnitude (dB)" if db else " Singular Value Magnitude (-)"
-    )
+    ax.set_ylabel("Magnitude (dB)" if db else "Magnitude (-)")
     ax.grid()
     ax.set_xlabel("$f$ (Hz)" if hz else r"$\omega$ (rad/s)")
     handles, labels = ax.get_legend_handles_labels()
     legend_dict = dict(zip(labels, handles))
-    fig.legend(
+    legend = fig.legend(
         labels=legend_dict.keys(),
         handles=legend_dict.values(),
         loc="outside lower center",
         ncol=2,
     )
 
-    return fig, ax
+    return fig, ax, legend
 
 
 def plot_singular_value_response_residual(
@@ -1356,7 +1403,7 @@ def plot_singular_value_response_residual(
     plot_sval_max_kw: Dict[str, Any] = {},
     plot_sval_kw: Dict[str, Any] = {},
     subplot_kw: Dict[str, Any] = {},
-) -> Dict[str, Tuple[Figure, Union[Axes, np.ndarray]]]:
+) -> Dict[str, Tuple[Figure, Axes, Legend]]:
     """Plot the singular value response of the uncertainty residuals for different
     unstructured uncertainty models on separate figures.
 
@@ -1386,6 +1433,12 @@ def plot_singular_value_response_residual(
         Keyword arguments for the subplot. See [#subplot_kw]_ for more information on
         the subplot keywords.
 
+    Returns
+    -------
+    Tuple[Figure, Union[Axes, np.ndarray], Legend]
+        Matplotlib Figure object, Axes object (or np.ndarray of Axes objects), and
+        Legend object.
+
     References
     ----------
     .. [#plot_kw] https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html
@@ -1459,13 +1512,11 @@ def plot_singular_value_response_residual(
         # Plot settings
         if frequency_log_scale:
             ax.set_xscale("log")
-        ax.set_ylabel(
-            "Singular Value Magnitude (dB)" if db else "Singular Value Magnitude (-)"
-        )
+        ax.set_ylabel("Magnitude (dB)" if db else "Magnitude (-)")
         ax.grid()
         ax.set_xlabel("$f$ (Hz)" if hz else r"$\omega$ (rad/s)")
         handles, labels = ax.get_legend_handles_labels()
-        legend_dict = dict(zip(labels, handles))
+        legend = legend_dict = dict(zip(labels, handles))
         fig.legend(
             labels=legend_dict.keys(),
             handles=legend_dict.values(),
@@ -1473,7 +1524,7 @@ def plot_singular_value_response_residual(
             ncol=2,
         )
 
-        figure_dict[uncertainty_model_id] = (fig, ax)
+        figure_dict[uncertainty_model_id] = (fig, ax, legend)
 
     return figure_dict
 
@@ -1486,7 +1537,7 @@ def plot_singular_value_response_residual_comparison(
     frequency_log_scale: bool = True,
     plot_sval_max_kw: Dict[str, Any] = {},
     subplot_kw: Dict[str, Any] = {},
-) -> Tuple[Figure, Union[Axes, np.ndarray]]:
+) -> Tuple[Figure, Axes, Legend]:
     """Plot the maximum singular value response of the uncertainty residuals for
     different unstructured uncertainty models on the same figure for comparision of the
     various uncertainty models.
@@ -1514,6 +1565,12 @@ def plot_singular_value_response_residual_comparison(
         Keyword arguments for the subplot. See [#subplot_kw]_ for more information on
         the subplot keywords.
 
+    Returns
+    -------
+    Tuple[Figure, Union[Axes, np.ndarray], Legend]
+        Matplotlib Figure object, Axes object (or np.ndarray of Axes objects), and
+        Legend object.
+
     References
     ----------
     .. [#plot_kw] https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html
@@ -1571,21 +1628,19 @@ def plot_singular_value_response_residual_comparison(
     # Plot settings
     if frequency_log_scale:
         ax.set_xscale("log")
-    ax.set_ylabel(
-        "Singular Value Magnitude (dB)" if db else "Singular Value Magnitude (-)"
-    )
+    ax.set_ylabel("Magnitude (dB)" if db else "Magnitude (-)")
     ax.grid()
     ax.set_xlabel("$f$ (Hz)" if hz else r"$\omega$ (rad/s)")
     handles, labels = ax.get_legend_handles_labels()
     legend_dict = dict(zip(labels, handles))
-    fig.legend(
+    legend = fig.legend(
         labels=legend_dict.keys(),
         handles=legend_dict.values(),
         loc="outside lower center",
         ncol=3,
     )
 
-    return fig, ax
+    return fig, ax, legend
 
 
 def plot_magnitude_response_uncertainty_weight(
@@ -1600,7 +1655,7 @@ def plot_magnitude_response_uncertainty_weight(
     plot_response_kw: Dict[str, Any] = {},
     plot_response_fit_kw: Dict[str, Any] = {},
     subplot_kw: Dict[str, Any] = {},
-) -> Tuple[Figure, Union[Axes, np.ndarray]]:
+) -> Tuple[Figure, Union[Axes, np.ndarray], Legend]:
     """Plot the diagonal elements of the optimal left and right uncertainty weight
     frequency responses. Optionally, the fitted overbounding left and right uncertainty
     weights can also be displayed.
@@ -1638,6 +1693,12 @@ def plot_magnitude_response_uncertainty_weight(
         Keyword arguments for the subplot. See [#subplot_kw]_ for more information on
         the subplot keywords.
 
+    Returns
+    -------
+    Tuple[Figure, Union[Axes, np.ndarray], Legend]
+        Matplotlib Figure object, Axes object (or np.ndarray of Axes objects), and
+        Legend object.
+
     References
     ----------
     .. [#plot_kw] https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html
@@ -1754,11 +1815,11 @@ def plot_magnitude_response_uncertainty_weight(
                 fig.delaxes(ax_row_col)
     handles, labels = ax[0, 0].get_legend_handles_labels()
     legend_dict = dict(zip(labels, handles))
-    fig.legend(
+    legend = fig.legend(
         labels=legend_dict.keys(),
         handles=legend_dict.values(),
         loc="outside lower center",
         ncol=2,
     )
 
-    return fig, ax
+    return fig, ax, legend
diff --git a/src/dkpy/utilities.py b/src/dkpy/utilities.py
index 4495f97..23f67a2 100644
--- a/src/dkpy/utilities.py
+++ b/src/dkpy/utilities.py
@@ -4,6 +4,7 @@
     "example_scherer1997_p907",
     "example_skogestad2006_p325",
     "example_mackenroth2004_p430",
+    "example_aircraft_actuator_uncertainty",
     "example_multimodel_uncertainty",
     "_ensure_tf",
     "_tf_close_coeff",
@@ -322,6 +323,96 @@ def example_mackenroth2004_p430():
     return out
 
 
+def example_aircraft_actuator_uncertainty():
+    """Add uncertain frequency response of aircaft actuator from [M04]_, Section 14.1
+    (p. 430).
+    """
+    # Actuator model
+    A_act = np.array(
+        [[0, 1, 0, 0], [-1600, -56, 0, 0], [0, 0, 0, 1], [0, 0, -1600, -56]]
+    )
+    B_act = np.array([[0, 0], [1600, 0], [0, 0], [0, 1600]])
+    C_act = np.eye(4)
+    D_act = np.zeros((4, 2))
+    actuator = control.StateSpace(A_act, B_act, C_act, D_act, name="actuator")
+    actuator.set_inputs(["zeta_unc", "xi_unc"])
+    actuator.set_outputs(["zeta", "rate_zeta", "xi", "rate_xi"])
+
+    # Input multiplicative uncertainty weight
+    weight_unc_channel = control.TransferFunction([0.25, 0.05], [0.125, 1])
+    weight_unc = control.append(
+        weight_unc_channel,
+        weight_unc_channel,
+        name="weight_unc",
+    )
+    weight_unc.set_inputs(["zeta_c", "xi_c"])
+    weight_unc.set_outputs(weight_unc.noutputs, "y_del")
+
+    # Uncertainty summation junction
+    sum_uncertainty = control.StateSpace([], [], [], [[1, 0, 1, 0], [0, 1, 0, 1]])
+    sum_uncertainty.set_inputs(["zeta_c", "xi_c", "u_del[0]", "u_del[1]"])
+    sum_uncertainty.set_outputs(["zeta_unc", "xi_unc"])
+
+    # Generate off-nominal models with different perturbations
+    delta_block_list = []
+
+    # Constant gain perturbation
+    num_offnom_gain = 20
+    for gain_unc in np.linspace(-1, 1, num_offnom_gain):
+        delta = control.StateSpace([], [], [], [gain_unc])
+        delta_block = control.append(delta, delta)
+        delta_block.set_inputs(delta_block.ninputs, "y_del")
+        delta_block.set_outputs(delta_block.noutputs, "u_del")
+        delta_block_list.append(delta_block)
+
+    # Phase perturbation
+    a_min = 0.01
+    a_max = 10
+    num_offnom_phase = 20
+    for a in np.linspace(a_min, a_max, num_offnom_phase):
+        delta_block = control.append(
+            control.tf([1 / a, -1], [1 / a, 1]),
+            control.tf([1 / a, -1], [1 / a, 1]),
+            name="delta_block",
+        )
+        delta_block.set_inputs(delta_block.ninputs, "y_del")
+        delta_block.set_outputs(delta_block.noutputs, "u_del")
+        delta_block_list.append(delta_block)
+
+    # Off-nominal actuator list
+    actuator_offnom_list = []
+    for delta_block in delta_block_list:
+        actuator_offnom = control.interconnect(
+            syslist=[
+                actuator,
+                delta_block,
+                weight_unc,
+                sum_uncertainty,
+            ],
+            inplist=["zeta_c", "xi_c"],
+            outlist=["zeta", "rate_zeta", "xi", "rate_xi"],
+            name="closed_loop_sim",
+        )
+        actuator_offnom_list.append(actuator_offnom)
+
+    # Frequency response
+    omega_min = 0.01
+    omega_max = 100
+    num_omega = 100
+    omega = np.logspace(np.log10(omega_min), np.log10(omega_max), num_omega)
+
+    frequency_response_actuator = control.frequency_response(actuator, omega)
+    frequency_response_actuator_offnom_list = control.frequency_response(
+        actuator_offnom_list, omega
+    )
+
+    return {
+        "response_actuator_nominal": frequency_response_actuator,
+        "response_actuator_offnominal": frequency_response_actuator_offnom_list,
+        "omega": omega,
+    }
+
+
 def example_multimodel_uncertainty():
     """Generate uncertain models using parameter variation."""
 
diff --git a/tests/test_uncertainty_characterization.py b/tests/test_uncertainty_characterization.py
index 963eeb2..390053c 100644
--- a/tests/test_uncertainty_characterization.py
+++ b/tests/test_uncertainty_characterization.py
@@ -133,7 +133,7 @@ def test_compute_uncertainty_residual_multiplicative_input_freq(
         self, complex_response_nom_freq, complex_response_offnom_freq
     ):
         """Test ValueError of
-        :func:`_test_compute_uncertainty_residual_multiplicative_input_freq`.
+        :func:`_compute_uncertainty_residual_multiplicative_input_freq`.
         """
 
         with pytest.raises(ValueError):
@@ -154,7 +154,7 @@ def test_compute_uncertainty_residual_multiplicative_output_freq(
         self, complex_response_nom_freq, complex_response_offnom_freq
     ):
         """Test ValueError of
-        :func:`_test_compute_uncertainty_residual_multiplicative_output_freq`.
+        :func:`_compute_uncertainty_residual_multiplicative_output_freq`.
         """
         with pytest.raises(ValueError):
             residual_freq = dkpy.uncertainty_characterization._compute_uncertainty_residual_multiplicative_output_freq(
@@ -178,7 +178,7 @@ def test_compute_uncertainty_residual_inverse_additive_freq(
         self, complex_response_nom_freq, complex_response_offnom_freq
     ):
         """Test ValueError of
-        :func:`_test_compute_uncertainty_residual_inverse_additive_freq`.
+        :func:`_compute_uncertainty_residual_inverse_additive_freq`.
         """
         with pytest.raises(ValueError):
             residual_freq = dkpy.uncertainty_characterization._compute_uncertainty_residual_inverse_additive_freq(
@@ -198,7 +198,7 @@ def test_compute_uncertainty_residual_inverse_multiplicative_input_freq(
         self, complex_response_nom_freq, complex_response_offnom_freq
     ):
         """Test ValueError of
-        :func:`_test_compute_uncertainty_residual_inverse_multiplicative_input_freq`.
+        :func:`_compute_uncertainty_residual_inverse_multiplicative_input_freq`.
         """
         with pytest.raises(ValueError):
             residual_freq = dkpy.uncertainty_characterization._compute_uncertainty_residual_inverse_multiplicative_input_freq(
@@ -218,7 +218,7 @@ def test_compute_uncertainty_residual_inverse_multiplicative_output_freq(
         self, complex_response_nom_freq, complex_response_offnom_freq
     ):
         """Test ValueError of
-        :func:`_test_compute_uncertainty_residual_inverse_multiplicative_output_freq`.
+        :func:`_compute_uncertainty_residual_inverse_multiplicative_output_freq`.
         """
         with pytest.raises(ValueError):
             residual_freq = dkpy.uncertainty_characterization._compute_uncertainty_residual_inverse_multiplicative_output_freq(
@@ -226,8 +226,8 @@ def test_compute_uncertainty_residual_inverse_multiplicative_output_freq(
             )
 
 
-class TestComputeOptimalUncertaintyWeightResponse:
-    """Test :func:`compute_optimal_uncertainty_weight_response`."""
+class TestComputeUncertaintyWeightResponse:
+    """Test :func:`compute_uncertainty_weight_response`."""
 
     @pytest.mark.parametrize(
         "sys_nom, sys_offnom_list, omega, weight_left_structure, weight_right_structure",
@@ -589,7 +589,7 @@ class TestComputeOptimalUncertaintyWeightResponse:
             ),
         ],
     )
-    def test_compute_optimal_uncertainty_weight_response(
+    def test_compute_uncertainty_weight_response(
         self,
         ndarrays_regression,
         sys_nom,
@@ -598,7 +598,7 @@ def test_compute_optimal_uncertainty_weight_response(
         weight_left_structure,
         weight_right_structure,
     ):
-        """Regression test :func:`compute_optimal_uncertainty_weight_response`."""
+        """Regression test :func:`compute_uncertainty_weight_response`."""
         # Frequency response of systems
         frequency_response_nom = control.frequency_response(
             sys_nom, omega, squeeze=False
@@ -627,7 +627,7 @@ def test_compute_optimal_uncertainty_weight_response(
 
         # Optimal uncertainty weight
         complex_response_weight_left, complex_response_weight_right = (
-            dkpy.compute_optimal_uncertainty_weight_response(
+            dkpy.compute_uncertainty_weight_response(
                 complex_response_residual_dict["multiplicative_input"],
                 weight_left_structure,
                 weight_right_structure,
@@ -645,8 +645,8 @@ def test_compute_optimal_uncertainty_weight_response(
         )
 
 
-class TestFitOverboundingUncertaintyWeight:
-    """Test :func:`fit_overbounding_uncertainty_weight`."""
+class TestFitUncertaintyWeight:
+    """Test :func:`fit_uncertainty_weight`."""
 
     @pytest.mark.parametrize(
         "sys_nom, sys_offnom_list, omega, weight_left_structure, weight_right_structure, fit_order",
@@ -823,7 +823,7 @@ class TestFitOverboundingUncertaintyWeight:
             ),
         ],
     )
-    def test_fit_overbounding_uncertainty_weight(
+    def test_fit_uncertainty_weight(
         self,
         ndarrays_regression,
         sys_nom,
@@ -833,7 +833,7 @@ def test_fit_overbounding_uncertainty_weight(
         weight_right_structure,
         fit_order,
     ):
-        """Regression test :func:`test_fit_overbounding_uncertainty_weight`."""
+        """Regression test :func:`fit_uncertainty_weight`."""
         # Frequency response of systems
         frequency_response_nom = control.frequency_response(
             sys_nom, omega, squeeze=False
@@ -862,7 +862,7 @@ def test_fit_overbounding_uncertainty_weight(
 
         # Optimal uncertainty weight
         complex_response_weight_left, complex_response_weight_right = (
-            dkpy.compute_optimal_uncertainty_weight_response(
+            dkpy.compute_uncertainty_weight_response(
                 complex_response_residual_dict["multiplicative_input"],
                 weight_left_structure,
                 weight_right_structure,
@@ -870,10 +870,10 @@ def test_fit_overbounding_uncertainty_weight(
         )
 
         # Overbounding transfer function fit
-        weight_left_fit = dkpy.fit_overbounding_uncertainty_weight(
+        weight_left_fit = dkpy.fit_uncertainty_weight(
             complex_response_weight_left, omega, fit_order
         )
-        weight_right_fit = dkpy.fit_overbounding_uncertainty_weight(
+        weight_right_fit = dkpy.fit_uncertainty_weight(
             complex_response_weight_right, omega, fit_order
         )
 
diff --git a/tests/test_uncertainty_characterization/test_compute_optimal_uncertainty_weight_response_sys_nom0_sys_offnom_list0_omega0_diagonal_diagonal_.npz b/tests/test_uncertainty_characterization/test_compute_uncertainty_weight_response_sys_nom0_sys_offnom_list0_omega0_diagonal_diagonal_.npz
similarity index 100%
rename from tests/test_uncertainty_characterization/test_compute_optimal_uncertainty_weight_response_sys_nom0_sys_offnom_list0_omega0_diagonal_diagonal_.npz
rename to tests/test_uncertainty_characterization/test_compute_uncertainty_weight_response_sys_nom0_sys_offnom_list0_omega0_diagonal_diagonal_.npz
diff --git a/tests/test_uncertainty_characterization/test_compute_optimal_uncertainty_weight_response_sys_nom10_sys_offnom_list10_omega10_diagonal_diagonal_.npz b/tests/test_uncertainty_characterization/test_compute_uncertainty_weight_response_sys_nom10_sys_offnom_list10_omega10_diagonal_diagonal_.npz
similarity index 100%
rename from tests/test_uncertainty_characterization/test_compute_optimal_uncertainty_weight_response_sys_nom10_sys_offnom_list10_omega10_diagonal_diagonal_.npz
rename to tests/test_uncertainty_characterization/test_compute_uncertainty_weight_response_sys_nom10_sys_offnom_list10_omega10_diagonal_diagonal_.npz
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