The Python Control Systems Library (python-control) is a Python package that implements basic operations for analysis and design of feedback control systems. Features Linear input/output systems in state-space and frequency domain

The Python Control Systems Library (python-control) provides common functions for analyzing and designing feedback control systems. The initial goal for the package is to implement all of the functionality required to work through the examples in the …

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.

The Python Control Systems Library control provides common functions for analyzing and designing feedback control systems. Documentation is available in two forms: docstrings provided with the code, and the python-control users guide, available from the python-control homepage .

This Jupyter notebook contains an introduction to the basic operations in the Python Control Systems Library (python-control), a Python package for control system design. The tutorial consists of two examples:

The examples below use python-control in a Jupyter notebook environment. These notebooks demonstrate the use of modeling, analysis, and design tools using examples from textbooks ( FBS , OBC ), courses, and other online sources.

The Python Control Systems Library (python-control) provides common functions for analyzing and designing feedback control systems. The initial goal for the package is to implement all of the functionality required to work through the examples in the …

MATLAB Compatibility Module MATLAB compatibility subpackage. This subpackage contains a number of functions that emulate some of the functionality of MATLAB. The intent of these functions is to provide a simple interface to the python control systems library (python-control) for people who are familiar with the MATLAB Control Systems Toolbox (tm).

control. lqr (A, B, Q, R [, N]) [source] Linear quadratic regulator design. The lqr() function computes the optimal state feedback controller u = -K x that minimizes the quadratic cost

A common use of optimization-based control techniques is the implementation of model predictive control (MPC, also called receding horizon control). In model predictive control, a finite horizon optimal control problem is solved, generating open-loop state and control trajectories.