# Introduction¶

Welcome to the Python Control Systems Toolbox (python-control) User’s Manual. This manual contains information on using the python-control package, including documentation for all functions in the package and examples illustrating their use.

## Overview of the toolbox¶

The python-control package is a set of python classes and functions that implement common operations for the analysis and design of feedback control systems. The initial goal is to implement all of the functionality required to work through the examples in the textbook Feedback Systems by Astrom and Murray. A MATLAB compatibility module is available that provides many of the common functions corresponding to commands available in the MATLAB Control Systems Toolbox.

## Some differences from MATLAB¶

The python-control package makes use of NumPy and SciPy. A list of general differences between NumPy and MATLAB can be found here.

In terms of the python-control package more specifically, here are some thing to keep in mind:

You must include commas in vectors. So [1 2 3] must be [1, 2, 3].

Functions that return multiple arguments use tuples.

You cannot use braces for collections; use tuples instead.

Time series data have time as the final index (see Time series data).

## Installation¶

The python-control package can be installed using conda or pip. The package requires NumPy and SciPy, and the plotting routines require Matplotlib. In addition, some routines require the Slycot library in order to implement more advanced features (including some MIMO functionality).

For users with the Anaconda distribution of Python, the following command can be used:

```
conda install -c conda-forge control slycot
```

This installs slycot and python-control from conda-forge, including the openblas package. NumPy, SciPy, and Matplotlib will also be installed if they are not already present.

Note

Mixing packages from conda-forge and the default conda channel can sometimes cause problems with dependencies, so it is usually best to instally NumPy, SciPy, and Matplotlib from conda-forge as well.)

To install using pip:

```
pip install slycot # optional
pip install control
```

Note

If you install Slycot using pip you’ll need a development environment (e.g., Python development files, C and Fortran compilers). Pip installation can be particularly complicated for Windows.

Many parts of python-control will work without slycot, but some functionality is limited or absent, and installation of slycot is recommended. Users can check to insure that slycot is installed correctly by running the command:

```
python -c "import slycot"
```

and verifying that no error message appears. More information on the Slycot package can be obtained from the Slycot project page.

Alternatively, to install from source, first download the source and unpack it. To install in your home directory, use:

```
pip install .
```

## Getting started¶

There are two different ways to use the package. For the default interface described in Function reference, simply import the control package as follows:

```
>>> import control as ct
```

If you want to have a MATLAB-like environment, use the MATLAB compatibility module:

```
>>> from control.matlab import *
```