Library conventions

The python-control library uses a set of standard conventions for the way that different types of standard information used by the library.

LTI system representation

Linear time invariant (LTI) systems are represented in python-control in state space, transfer function, or frequency response data (FRD) form. Most functions in the toolbox will operate on any of these data types and functions for converting between between compatible types is provided.

State space systems

The StateSpace class is used to represent state-space realizations of linear time-invariant (LTI) systems:

\frac{dx}{dt} &= A x + B u \\
y &= C x + D u

where u is the input, y is the output, and x is the state.

To create a state space system, use the StateSpace constructor:

sys = StateSpace(A, B, C, D)

State space systems can be manipulated using standard arithmetic operations as well as the feedback(), parallel(), and series() function. A full list of functions can be found in Function reference.

Transfer functions

The TransferFunction class is used to represent input/output transfer functions

G(s) = \frac{\text{num}(s)}{\text{den}(s)}
     = \frac{a_0 s^n + a_1 s^{n-1} + \cdots + a_n}
            {b_0 s^m + b_1 s^{m-1} + \cdots + b_m},

where n is generally greater than or equal to m (for a proper transfer function).

To create a transfer function, use the TransferFunction constructor:

sys = TransferFunction(num, den)

Transfer functions can be manipulated using standard arithmetic operations as well as the feedback(), parallel(), and series() function. A full list of functions can be found in Function reference.

FRD (frequency response data) systems

The FRD class is used to represent systems in frequency response data form.

The main data members are omega and fresp, where omega is a 1D array with the frequency points of the response, and fresp is a 3D array, with the first dimension corresponding to the output index of the FRD, the second dimension corresponding to the input index, and the 3rd dimension corresponding to the frequency points in omega.

FRD systems have a somewhat more limited set of functions that are available, although all of the standard algebraic manipulations can be performed.

Discrete time systems

By default, all systems are considered to be continuous time systems. A discrete time system is created by specifying the ‘time base’ dt. The time base argument can be given when a system is constructed:

  • dt = None: no timebase specified
  • dt = 0: continuous time system
  • dt > 0: discrete time system with sampling period ‘dt’
  • dt = True: discrete time with unspecified sampling period

Only the StateSpace and TransferFunction classes allow explicit representation of discrete time systems.

Systems must have the same time base in order to be combined. For continuous time systems, the sample_system() function or the StateSpace.sample() and TransferFunction.sample() methods can be used to create a discrete time system from a continuous time system. See Utility functions and conversions.

Conversion between representations

LTI systems can be converted between representations either by calling the constructor for the desired data type using the original system as the sole argument or using the explicit conversion functions ss2tf() and tf2ss().

Time series data

This is a convention for function arguments and return values that represent time series: sequences of values that change over time. It is used throughout the library, for example in the functions forced_response(), step_response(), impulse_response(), and initial_response().


This convention is different from the convention used in the library scipy.signal. 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.


  • Arguments can be arrays, matrices, or nested lists.
  • Return values are arrays (not matrices).

The time vector is either 1D, or 2D with shape (1, 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. When there is only one row, a 1D object is accepted or returned, which adds convenience for SISO systems:

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)]]

Same 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.

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

X0 = [[x1]

As all simulation functions return arrays, plotting is convenient:

t, y = step(sys)
plot(t, y)

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

t, y, x = lsim(sys, u, t)
plot(t, y[0], label='y_0')
plot(t, y[1], label='y_1')

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

ft = D * U

Package configuration

The python-control library can be customized to allow for different plotting conventions. The currently configurable options allow the units for Bode plots to be set as dB for gain, degrees for phase and Hertz for frequency (MATLAB conventions) or the gain can be given in magnitude units (powers of 10), corresponding to the conventions used in Feedback Systems.

Variables that can be configured, along with their default values:
  • bode_dB (False): Bode plot magnitude plotted in dB (otherwise powers of 10)
  • bode_deg (True): Bode plot phase plotted in degrees (otherwise radians)
  • bode_Hz (False): Bode plot frequency plotted in Hertz (otherwise rad/sec)
  • bode_number_of_samples (None): Number of frequency points in Bode plots
  • bode_feature_periphery_decade (1.0): How many decades to include in the frequency range on both sides of features (poles, zeros).

Functions that can be used to set standard configurations:

use_fbs_defaults() Use Astrom and Murray compatible settings
use_matlab_defaults() Use MATLAB compatible configuration settings