4. Input/Output Response and Plotting

The Python Control Systems Toolbox contains a number of functions for computing and plotting input/output responses in the time and frequency domain, root locus diagrams, and other standard charts used in control system analysis, for example:

bode_plot(sys)
nyquist_plot([sys1, sys2])
phase_plane_plot(sys, limits)
pole_zero_plot(sys)
root_locus_plot(sys)

While plotting functions can be called directly, the standard pattern used in the toolbox is to provide a function that performs the basic computation or analysis (e.g., computation of the time or frequency response) and returns an object representing the output data. A separate plotting function, typically ending in _plot, is then used to plot the data, resulting in the following standard pattern:

response = ct.nyquist_response([sys1, sys2])
count = ct.response.count          # number of encirclements of -1
cplt = ct.nyquist_plot(response)   # Nyquist plot

Plotting commands return a ControlPlot object that provides access to the individual lines in the generated plot using cplt.lines, allowing various aspects of the plot to be modified to suit specific needs.

The plotting function is also available via the plot() method of the analysis object, allowing the following type of calls:

step_response(sys).plot()
frequency_response(sys).plot()
nyquist_response(sys).plot()
pp.streamlines(sys, limits).plot()
root_locus_map(sys).plot()

The remainder of this chapter provides additional documentation on how these response and plotting functions can be customized.

4.1. Time Response Data

Time responses are used to provide information on the behavior of a system in response to a standard input (such as a step function or impulse function), the initial state with no input, a custom function of time, or any combination of the above. Time responses are useful for evaluating system performance of either linear or nonlinear systems, in continuous or discrete time. The time response for a linear system to a standard input can be often computed exactly while the responses of nonlinear systems or linear systems with arbitrary input signals must be computed numerically.

Continuous time signals in python-control are represented by the value of the signal at a set of specified time points, with linear interpolation between the time points. The time points need not be uniformly spaced. Discrete time signals are represented by the value of the signal at a uniformly-spaced sequence of times.

LTI response functions

A number of functions are available for computing the output (and state) response of an LTI systems:

initial_response(sysdata[, timepts, ...])

Compute the initial condition response for a linear system.

step_response(sysdata[, timepts, ...])

Compute the step response for a linear system.

impulse_response(sysdata[, timepts, ...])

Compute the impulse response for a linear system.

forced_response(sysdata[, timepts, inputs, ...])

Compute the output of a linear system given the input.

Each of these functions returns a TimeResponseData object that contains the data for the time response (described in more detail in the next section).

The forced_response() system is the most general and computes the response of the system to a given input from a zero or non-zero initial condition.

For linear time invariant (LTI) systems, the impulse_response(), initial_response(), and step_response() functions will automatically compute the time vector based on the poles and zeros of the system. If a list of systems is passed, a common time vector will be computed and a list of responses will be returned in the form of a TimeResponseList object. The forced_response() function can also take a list of systems, to which a single common input is applied. The TimeResponseList object has a plot() method that will plot each of the responses in turn, using a sequence of different colors with appropriate titles and legends.

In addition, the input_output_response() function, which handles simulation of nonlinear systems and interconnected systems, can be used. For an LTI system, results are generally more accurate using the LTI simulation functions above. The input_output_response() function is described in more detail in the Interconnected I/O Systems section.

Time series data conventions

A variety of functions in the library return time series data: sequences of values that change over time. A common set of conventions is used for returning such data: columns represent different points in time, rows are different components (e.g., inputs, outputs or states). For return arguments, an array of times is given as the first returned argument, followed by one or more arrays of variable values. This convention is used throughout the library, for example in the functions forced_response(), step_response(), impulse_response(), and initial_response().

Note

The convention used by python-control is different from the convention used in the scipy.signal library. In SciPy’s convention the meaning of rows and columns is interchanged. Thus, all 2D values must be transposed when they are used with functions from scipy.signal.

The time vector is a 1D array with shape (n, ):

T = [t1,     t2,     t3,     ..., tn    ]

Input, state, and output all follow the same convention. Columns are different points in time, rows are different components:

U = [[u1(t1), u1(t2), u1(t3), ..., u1(tn)]
     [u2(t1), u2(t2), u2(t3), ..., u2(tn)]
     ...
     ...
     [ui(t1), ui(t2), ui(t3), ..., ui(tn)]]

(and similarly for X, Y). So, U[:, 2] is the system’s input at the third point in time; and U[1] or U[1, :] is the sequence of values for the system’s second input.

When there is only one row, a 1D object is accepted or returned, which adds convenience for SISO systems:

The initial conditions are either 1D, or 2D with shape (j, 1):

X0 = [[x1]
      [x2]
      ...
      ...
      [xj]]

Functions that return time responses (e.g., forced_response(), impulse_response(), input_output_response(), initial_response(), and step_response()) return a TimeResponseData object that contains the data for the time response. These data can be accessed via the time, outputs, states and inputs properties:

sys = ct.rss(4, 1, 1)
response = ct.step_response(sys)
plt.plot(response.time, response.outputs)

The dimensions of the response properties depend on the function being called and whether the system is SISO or MIMO. In addition, some time response function can return multiple “traces” (input/output pairs), such as the step_response() function applied to a MIMO system, which will compute the step response for each input/output pair. See TimeResponseData for more details.

The input, output, and state elements of the response can be accessed using signal names in place of integer offsets:

plt.plot(response.time, response.states['x[1]'])

The time response functions can also be assigned to a tuple, which extracts the time and output (and optionally the state, if the return_x keyword is used). This allows simple commands for plotting:

t, y = ct.step_response(sys)
plt.plot(t, y)

The output of a MIMO LTI system can be plotted like this:

sys = ct.rss(4, 2, 1)

timepts = np.linspace(0, 10)
u = np.sin(timepts)

t, y = ct.forced_response(sys, timepts, u)
plt.plot(t, y[0], label='y_0')
plt.plot(t, y[1], label='y_1')

For multi-trace systems generated by step_response() and impulse_response(), the input name used to generate the trace can be used to access the appropriate input output pair:

response = ct.step_response(sys)
plt.plot(response.time, response.outputs['y[1]', 'u[0]'])

The convention also works well with the state space form of linear systems. If D is the feedthrough matrix (2D array) of a linear system, and U is its input (array), then the feedthrough part of the system’s response, can be computed like this:

ft = D @ U

Finally, the to_pandas method can be used to create a pandas dataframe:

df = response.to_pandas()

The column labels for the data frame are time and the labels for the input, output, and state signals (‘u[i]’, ‘y[i]’, and ‘x[i]’ by default, but these can be changed using the inputs, outputs, and states keywords when constructing the system, as described in ss(), tf(), and other system creation functions. Note that when exporting to pandas, “rows” in the data frame correspond to time and “cols” (DataSeries) correspond to signals.

Time response plots

The input/output time response functions ( forced_response(), impulse_response(), initial_response(), input_output_response(), step_response()) return a TimeResponseData object, which contains the time, input, state, and output vectors associated with the simulation, as described above. Time response data can be plotted with the time_response_plot() function, which is also available as the TimeResponseData.plot() method. For example, the step response for a two-input, two-output can be plotted using the commands:

sys_mimo = ct.tf2ss(
    [[[1], [0.1]], [[0.2], [1]]],
    [[[1, 0.6, 1], [1, 1, 1]], [[1, 0.4, 1], [1, 2, 1]]], name="sys_mimo")
response = ct.step_response(sys_mimo)
response.plot()

which produces the following plot:

_images/timeplot-mimo_step-default.png

A number of options are available in the time_response_plot() function (and associated TimeResponseData.plot() method) to customize the appearance of input output data. For data produced by the impulse_response() and step_response() commands, the inputs are not shown. This behavior can be changed using the plot_inputs keyword. It is also possible to combine multiple lines onto a single graph, using either the overlay_signals keyword (which puts all outputs out a single graph and all inputs on a single graph) or the overlay_traces keyword, which puts different traces (e.g., corresponding to step inputs in different channels) on the same graph, with appropriate labeling via a legend on selected axes.

For example, using plot_input = True and overlay_signals = True yields the following plot:

ct.step_response(sys_mimo).plot(
  plot_inputs=True, overlay_signals=True,
  title="Step response for 2x2 MIMO system " +
  "[plot_inputs, overlay_signals]")
_images/timeplot-mimo_step-pi_cs.png

Input/output response plots created with either the forced_response() or the input_output_response() functions include the input signals by default. These can be plotted on separate axes, but also “overlaid” on the output axes (useful when the input and output signals are being compared to each other). The following plot shows the use of plot_inputs = ‘overlay’ as well as the ability to reposition the legends using the legend_map keyword:

timepts = np.linspace(0, 10, 100)
U = np.vstack([np.sin(timepts), np.cos(2*timepts)])
ct.input_output_response(sys_mimo, timepts, U).plot(
    plot_inputs='overlay',
    legend_map=np.array([['lower right'], ['lower right']]),
    title="I/O response for 2x2 MIMO system " +
    "[plot_inputs='overlay', legend_map]")
_images/timeplot-mimo_ioresp-ov_lm.png

Another option that is available is to use the transpose keyword so that instead of plotting the outputs on the top and inputs on the bottom, the inputs are plotted on the left and outputs on the right, as shown in the following figure:

U1 = np.vstack([np.sin(timepts), np.cos(2*timepts)])
resp1 = ct.input_output_response(sys_mimo, timepts, U1)

U2 = np.vstack([np.cos(2*timepts), np.sin(timepts)])
resp2 = ct.input_output_response(sys_mimo, timepts, U2)

ct.combine_time_responses(
    [resp1, resp2], trace_labels=["Scenario #1", "Scenario #2"]).plot(
        transpose=True,
        title="I/O responses for 2x2 MIMO system, multiple traces "
        "[transpose]")
_images/timeplot-mimo_ioresp-mt_tr.png

This figure also illustrates the ability to create “multi-trace” plots using the combine_time_responses() function. The line properties that are used when combining signals and traces are set by the input_props, output_props and trace_props parameters for time_response_plot().

Additional customization is possible using the input_props, output_props, and trace_props keywords to set complementary line colors and styles for various signals and traces:

cplt = ct.step_response(sys_mimo).plot(
    plot_inputs='overlay', overlay_signals=True, overlay_traces=True,
    output_props=[{'color': c} for c in ['blue', 'orange']],
    input_props=[{'color': c} for c in ['red', 'green']],
    trace_props=[{'linestyle': s} for s in ['-', '--']])
_images/timeplot-mimo_step-linestyle.png

4.2. Frequency Response Data

Linear time invariant (LTI) systems can be analyzed in terms of their frequency response and python-control provides a variety of tools for carrying out frequency response analysis. The most basic of these is the frequency_response() function, which will compute the frequency response for one or more linear systems:

sys1 = ct.tf([1], [1, 2, 1], name='sys1')
sys2 = ct.tf([1, 0.2], [1, 1, 3, 1, 1], name='sys2')
response = ct.frequency_response([sys1, sys2])

A Bode plot provide a graphical view of the response an LTI system and can be generated using the bode_plot() function:

ct.bode_plot(response, initial_phase=0)
_images/freqplot-siso_bode-default.png

Computing the response for multiple systems at the same time yields a common frequency range that covers the features of all listed systems.

Bode plots can also be created directly using the FrequencyResponseData.plot() method:

sys_mimo = ct.tf(
    [[[1], [0.1]], [[0.2], [1]]],
    [[[1, 0.6, 1], [1, 1, 1]], [[1, 0.4, 1], [1, 2, 1]]], name="sys_mimo")
ct.frequency_response(sys_mimo).plot()
_images/freqplot-mimo_bode-default.png

A variety of options are available for customizing Bode plots, for example allowing the display of the phase to be turned off or overlaying the inputs or outputs:

ct.frequency_response(sys_mimo).plot(
    plot_phase=False, overlay_inputs=True, overlay_outputs=True)
_images/freqplot-mimo_bode-magonly.png

The singular_values_response() function can be used to generate Bode plots that show the singular values of a transfer function:

ct.singular_values_response(sys_mimo).plot()
_images/freqplot-mimo_svplot-default.png

Different types of plots can also be specified for a given frequency response. For example, to plot the frequency response using a a Nichols plot, use plot_type = ‘nichols’:

response.plot(plot_type='nichols')
_images/freqplot-siso_nichols-default.png

Another response function that can be used to generate Bode plots is the gangof4_response() function, which computes the four primary sensitivity functions for a feedback control system in standard form:

proc = ct.tf([1], [1, 1, 1], name="process")
ctrl = ct.tf([100], [1, 5], name="control")
response = ct.gangof4_response(proc, ctrl)
ct.bode_plot(response)        # or response.plot()
_images/freqplot-gangof4.png

Nyquist analysis can be done using the nyquist_response() function, which evaluates an LTI system along the Nyquist contour, and the nyquist_plot() function, which generates a Nyquist plot:

sys = ct.tf([1, 0.2], [1, 1, 3, 1, 1], name='sys')
ct.nyquist_plot(sys)
_images/freqplot-nyquist-default.png

The nyquist_response() function can be used to compute the number of encirclements of the -1 point and can return the Nyquist contour that was used to generate the Nyquist curve.

By default, the Nyquist response will generate small semicircles around poles that are on the imaginary axis. In addition, portions of the Nyquist curve that are far from the origin are scaled to a maximum value, while the line style is changed to reflect the scaling, and it is possible to offset the scaled portions to separate out the portions of the Nyquist curve at \infty. A number of keyword parameters for both are available for nyquist_response() and nyquist_plot() to tune the computation of the Nyquist curve and the way the data are plotted:

sys = ct.tf([1, 0.2], [1, 0, 1]) * ct.tf([1], [1, 0])
nyqresp = ct.nyquist_response(sys)
nyqresp.plot(
    max_curve_magnitude=6, max_curve_offset=1,
    arrows=[0, 0.15, 0.3, 0.6, 0.7, 0.925],
    title='Custom Nyquist plot')
print("Encirclements =", nyqresp.count)
_images/freqplot-nyquist-custom.png

All frequency domain plotting functions will automatically compute the range of frequencies to plot based on the poles and zeros of the frequency response. Frequency points can be explicitly specified by including an array of frequencies as a second argument (after the list of systems):

sys1 = ct.tf([1], [1, 2, 1], name='sys1')
sys2 = ct.tf([1, 0.2], [1, 1, 3, 1, 1], name='sys2')
omega = np.logspace(-2, 2, 500)
ct.frequency_response([sys1, sys2], omega).plot(initial_phase=0)
_images/freqplot-siso_bode-omega.png

Alternatively, frequency ranges can be specified by passing a list of the form [wmin, wmax], where wmin and wmax are the minimum and maximum frequencies in the (log-spaced) frequency range:

response = ct.frequency_response([sys1, sys2], [1e-2, 1e2])

The number of (log-spaced) points in the frequency can be specified using the omega_num keyword parameter.

Frequency response data can also be accessed directly and plotted manually:

sys = ct.rss(4, 2, 2, strictly_proper=True)  # 2x2 MIMO system
fresp = ct.frequency_response(sys)
plt.loglog(fresp.omega, fresp.magnitude['y[1]', 'u[0]'])

Access to frequency response data is available via the attributes omega, magnitude, phase, and response, where response represents the complex value of the frequency response at each frequency. The magnitude, phase, and response arrays can be indexed using either input/output indices or signal names, with the first index corresponding to the output signal and the second input corresponding to the input signal.

4.3. Pole/Zero Data

Pole/zero maps and root locus diagrams provide insights into system response based on the locations of system poles and zeros in the complex plane. The pole_zero_map() function returns the poles and zeros and can be used to generate a pole/zero plot:

sys = ct.tf([1, 2], [1, 2, 3], name='SISO transfer function')
response = ct.pole_zero_map(sys)
ct.pole_zero_plot(response)
_images/pzmap-siso_ctime-default.png

A root locus plot shows the location of the closed loop poles of a system as a function of the loop gain:

ct.root_locus_map(sys).plot()
_images/rlocus-siso_ctime-default.png

The grid in the left hand plane shows lines of constant damping ratio as well as arcs corresponding to the frequency of the complex pole. The grid can be turned off using the grid keyword. Setting grid to False will turn off the grid but show the real and imaginary axis. To completely remove all lines except the root loci, use grid = ‘empty’.

On systems that support interactive plots, clicking on a location on the root locus diagram will mark the pole locations on all branches of the diagram and display the gain and damping ratio for the clicked point below the plot title:

_images/rlocus-siso_ctime-clicked.png

Root locus diagrams are also supported for discrete-time systems, in which case the grid is show inside the unit circle:

sysd = sys.sample(0.1)
ct.root_locus_plot(sysd)
_images/rlocus-siso_dtime-default.png

Lists of systems can also be given, in which case the root locus diagram for each system is plotted in different colors:

sys1 = ct.tf([1], [1, 2, 1], name='sys1')
sys2 = ct.tf([1, 0.2], [1, 1, 3, 1, 1], name='sys2')
ct.root_locus_plot([sys1, sys2], grid=False)
_images/rlocus-siso_multiple-nogrid.png

4.4. Customizing Control Plots

A set of common options are available to customize control plots in various ways. The following general rules apply:

  • If a plotting function is called multiple times with data that generate control plots with the same shape for the array of subplots, the new data will be overlaid with the old data, with a change in color(s) for the new data (chosen from the standard matplotlib color cycle). If not overridden, the plot title and legends will be updated to reflect all data shown on the plot.

  • If a plotting function is called and the shape for the array of subplots does not match the currently displayed plot, a new figure is created. Note that only the shape is checked, so if two different types of plotting commands that generate the same shape of subplots are called sequentially, the matplotlib.pyplot.figure() command should be used to explicitly create a new figure.

  • The ax keyword argument can be used to direct the plotting function to use a specific axes or array of axes. The value of the ax keyword must have the proper number of axes for the plot (so a plot generating a 2x2 array of subplots should be given a 2x2 array of axes for the ax keyword).

  • The color, linestyle, linewidth, and other matplotlib line property arguments can be used to override the default line properties. If these arguments are absent, the default matplotlib line properties are used and the color cycles through the default matplotlib color cycle.

    The bode_plot(), time_response_plot(), and selected other commands can also accept a matplotlib format string (e.g., ‘r–’). The format string must appear as a positional argument right after the required data argument.

    Note that line property arguments are the same for all lines generated as part of a single plotting command call, including when multiple responses are passed as a list to the plotting command. For this reason it is often easiest to call multiple plot commands in sequence, with each command setting the line properties for that system/trace.

  • The label keyword argument can be used to override the line labels that are used in generating the title and legend. If more than one line is being plotted in a given call to a plot command, the label argument value should be a list of labels, one for each line, in the order they will appear in the legend.

    For input/output plots (frequency and time responses), the labels that appear in the legend are of the form “<output name>, <input name>, <trace name>, <system name>”. The trace name is used only for multi-trace time plots (for example, step responses for MIMO systems). Common information present in all traces is removed, so that the labels appearing in the legend represent the unique characteristics of each line.

    For non-input/output plots (e.g., Nyquist plots, pole/zero plots, root locus plots), the default labels are the system name.

    If label is set to False, individual lines are still given labels, but no legend is generated in the plot. (This can also be accomplished by setting legend_map to False).

    Note: the label keyword argument is not implemented for describing function plots or phase plane plots, since these plots are primarily intended to be for a single system. Standard matplotlib commands can be used to customize these plots for displaying information for multiple systems.

  • The legend_loc, legend_map and show_legend keyword arguments can be used to customize the locations for legends. By default, a minimal number of legends are used such that lines can be uniquely identified and no legend is generated if there is only one line in the plot. Setting show_legend to False will suppress the legend and setting it to True will force the legend to be displayed even if there is only a single line in each axes. In addition, if the value of the legend_loc keyword argument is set to a string or integer, it will set the position of the legend as described in the matplotlib.legend() documentation. Finally, legend_map can be set to an array that matches the shape of the subplots, with each item being a string indicating the location of the legend for that axes (or None for no legend).

  • The rcParams keyword argument can be used to override the default matplotlib style parameters used when creating a plot. The default parameters for all control plots are given by the config.defaults['ctrlplot.rcParams'] dictionary and have the following values:

    Key

    Value

    ‘axes.labelsize’

    ‘small’

    ‘axes.titlesize’

    ‘small’

    ‘figure.titlesize’

    ‘medium’

    ‘legend.fontsize’

    ‘x-small’

    ‘xtick.labelsize’

    ‘small’

    ‘ytick.labelsize’

    ‘small’

    Only those values that should be changed from the default need to be specified in the rcParams keyword argument. To override the defaults for all control plots, update the config.defaults['ctrlplt.rcParams'] dictionary entries. For convenience, this dictionary can also be accessed as ct.rcParams.

    The default values for style parameters for control plots can be restored using reset_rcParams().

  • For multi-input, multi-output time and frequency domain plots, the sharex and sharey keyword arguments can be used to determine whether and how axis limits are shared between the individual subplots. Setting the keyword to ‘row’ will share the axes limits across all subplots in a row, ‘col’ will share across all subplots in a column, ‘all’ will share across all subplots in the figure, and False will allow independent limits for each subplot.

    For Bode plots, the share_magnitude and share_phase keyword arguments can be used to independently control axis limit sharing for the magnitude and phase portions of the plot, and share_frequency can be used instead of sharex.

  • The title keyword can be used to override the automatic creation of the plot title. The default title is a string of the form “<Type> plot for <syslist>” where <syslist> is a list of the sys names contained in the plot (which is updated if the plotting function is called multiple times). Use title = False to suppress the title completely. The title can also be updated using the set_plot_title() method for the returned control plot object.

    The plot title is only generated if ax is None.

The following code illustrates the use of some of these customization features:

P = ct.tf([0.02], [1, 0.1, 0.01])   # servomechanism
C1 = ct.tf([1, 1], [1, 0])          # unstable
L1 = P * C1
C2 = ct.tf([1, 0.05], [1, 0])       # stable
L2 = P * C2

plt.rcParams.update(ct.rcParams)
fig = plt.figure(figsize=[7, 4])
ax_mag = fig.add_subplot(2, 2, 1)
ax_phase = fig.add_subplot(2, 2, 3)
ax_nyquist = fig.add_subplot(1, 2, 2)

ct.bode_plot(
    [L1, L2], ax=[ax_mag, ax_phase],
    label=["$L_1$ (unstable)", "$L_2$ (unstable)"],
    show_legend=False)
ax_mag.set_title("Bode plot for $L_1$, $L_2$")
ax_mag.tick_params(labelbottom=False)
fig.align_labels()

ct.nyquist_plot(L1, ax=ax_nyquist, label="$L_1$ (unstable)")
ct.nyquist_plot(
    L2, ax=ax_nyquist, label="$L_2$ (stable)",
    max_curve_magnitude=22, legend_loc='upper right')
ax_nyquist.set_title("Nyquist plot for $L_1$, $L_2$")

fig.suptitle("Loop analysis for servomechanism control design")
plt.tight_layout()
_images/ctrlplot-servomech.png

As this example illustrates, python-control plotting functions and Matplotlib plotting functions can generally be intermixed. One type of plot for which this does not currently work is pole/zero plots with a continuous-time omega-damping grid (including root locus diagrams), due to the way that axes grids are implemented. As a workaround, the pole_zero_subplots() command can be used to create an array of subplots with different grid types, as illustrated in the following example:

ax_array = ct.pole_zero_subplots(2, 1, grid=[True, False])
sys1 = ct.tf([1, 2], [1, 2, 3], name='sys1')
sys2 = ct.tf([1, 0.2], [1, 1, 3, 1, 1], name='sys2')
ct.root_locus_plot([sys1, sys2], ax=ax_array[0, 0])
cplt = ct.root_locus_plot([sys1, sys2], ax=ax_array[1, 0])
cplt.set_plot_title("Root locus plots (w/ specified axes)")
cplt.figure.tight_layout()
_images/ctrlplot-pole_zero_subplots.png

Alternatively, turning off the omega-damping grid (using grid = False or grid = ‘empty’) allows use of Matplotlib layout commands.

4.5. Response and Plotting Reference

Response functions

Response functions take a system or list of systems and return a response object that can be used to retrieve information about the system (e.g., the number of encirclements for a Nyquist plot) as well as plotting (via the plot method).

describing_function_response(H, F, A[, ...])

Compute the describing function response of a system.

frequency_response(sysdata[, omega, ...])

Frequency response of an LTI system.

forced_response(sysdata[, timepts, inputs, ...])

Compute the output of a linear system given the input.

gangof4_response(P, C[, omega, ...])

Compute response of "Gang of 4" transfer functions.

impulse_response(sysdata[, timepts, ...])

Compute the impulse response for a linear system.

initial_response(sysdata[, timepts, ...])

Compute the initial condition response for a linear system.

input_output_response(sys[, timepts, ...])

Compute the output response of a system to a given input.

nyquist_response(sysdata[, omega, ...])

Nyquist response for a system.

pole_zero_map(sysdata)

Compute the pole/zero map for an LTI system.

root_locus_map(sysdata[, gains])

Compute the root locus map for an LTI system.

singular_values_response(sysdata[, omega, ...])

Singular value response for a system.

step_response(sysdata[, timepts, ...])

Compute the step response for a linear system.

Plotting functions

Plotting functions take a response or list of responses and return a ControlPlot object that can be used to retrieve information about the plot. Plotting functions can also be called with a system or list of systems, in which case the appropriate response will be first computed and then plotted.

Note that the phase_plane_plot function is part of the python-control namespace, but the individual functions for customizing phase plots are contained in the phaseplot module, which should be imported separately using import control.phaseplot as pp. The phase plane plotting functionality is described in more detail in the Phase Plane Plots section.

bode_plot(data[, omega, ax, omega_limits, ...])

Bode plot for a system.

describing_function_plot(data, *args, **kwargs)

Nyquist plot with describing function for a nonlinear system.

nichols_plot(data[, omega, grid, title, ax, ...])

Nichols plot for a system.

nyquist_plot(data[, omega, plot, ...])

Nyquist plot for a system.

phase_plane_plot(sys[, pointdata, timedata, ...])

Plot phase plane diagram.

phaseplot.circlegrid(centers, radius, num)

Generate list of points around a circle.

phaseplot.equilpoints(sys, pointdata[, ...])

Plot equilibrium points in the phase plane.

phaseplot.meshgrid(xvals, yvals)

Generate list of points forming a mesh.

phaseplot.separatrices(sys, pointdata[, ...])

Plot separatrices in the phase plane.

phaseplot.streamlines(sys, pointdata[, ...])

Plot stream lines in the phase plane.

phaseplot.vectorfield(sys, pointdata[, ...])

Plot a vector field in the phase plane.

pole_zero_plot(data[, plot, grid, title, ...])

Plot a pole/zero map for a linear system.

root_locus_plot(sysdata[, gains, grid, plot])

Root locus plot.

singular_values_plot(data[, omega, plot, ...])

Plot the singular values for a system.

time_response_plot(data, *fmt[, ax, ...])

Plot the time response of an input/output system.

Utility functions

These additional functions can be used to manipulate response data or carry out other operations in creating control plots.

phaseplot.boxgrid(xvals, yvals)

Generate list of points along the edge of box.

combine_time_responses(response_list[, ...])

Combine individual time responses into multi-trace response.

pole_zero_subplots(nrows, ncols[, grid, dt, ...])

Create axes for pole/zero plot.

reset_rcParams()

Reset rcParams to default values for control plots.

Response and plotting classes

The following classes are used in generating response data.

ControlPlot(lines[, axes, figure, legend])

Return class for control platting functions.

DescribingFunctionResponse(response, N_vals, ...)

Results of describing function analysis.

FrequencyResponseData(frdata, omega[, smooth])

Input/output model defined by frequency response data (FRD).

FrequencyResponseList([iterable])

List of FrequencyResponseData objects with plotting capability.

NyquistResponseData(count, contour, response, dt)

Nyquist response data object.

PoleZeroData(poles, zeros[, gains, loci, ...])

Pole/zero data object.

TimeResponseData(time, outputs[, states, ...])

Input/output system time response data.

TimeResponseList([iterable])

List of TimeResponseData objects with plotting capability.