control
Python Control Systems Library
Decision gist · record as of 2026-08-14
Yes. The package is actively maintained, has no known vulnerabilities, installs with low friction, and is permissively licensed. It is the standard Python library for control systems work in academia and industry. Install it if you need to analyze or design linear feedback control systems.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- Optional slycot dependency requires a C and Fortran compiler if installed via pip.
- Low friction installation with a pure Python wheel.
License · maintenance · safety
BSD-3-Clause (permissive) — Released under BSD-3-Clause (permissive). No restrictions on commercial use, modification, or redistribution provided the license text accompanies the code.
last release 2025-07-05 (405 days) · last repo commit 2026-08-14 · 2,066 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 282,815 downloads/mo, #8,084 on PyPI
Alternatives
Verify before relying
pip install control
import control as ct
sys = ct.TransferFunction([2], [2, 2])
ct.step_response(sys)- Whether slycot binaries are available for your platform via conda-forge or pip
- Performance characteristics for large-scale systems or real-time applications
What it is and what it does
The Python Control Systems Library provides a comprehensive toolkit for modeling, analyzing, and designing linear feedback control systems. It handles both state-space and frequency-domain representations, supports interconnections like series, parallel, and feedback configurations, and offers classical and modern control design methods including eigenvalue placement, linear quadratic regulators, and Kalman filtering.
The package is built on numpy, scipy, and matplotlib, making it suitable for interactive analysis in Jupyter notebooks, Google Colab, and production scripts. Core functionality works without optional dependencies, though the optional slycot wrapper extends capabilities for advanced control design tasks. It is actively maintained and widely used in academic and industrial control engineering.
Use it for
- Analyze stability and frequency response of linear systems using Bode, Nyquist, and Nichols plots
- Design state-feedback controllers via eigenvalue placement or linear quadratic regulator methods
- Simulate time-domain responses (step, impulse, initial condition) for system validation
- Assess system properties like reachability, observability, and stability margins
- Prototype and tune PID controllers using root locus and interactive design tools
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
The package is actively maintained, has no known vulnerabilities, installs with low friction, and is permissively licensed. It is the standard Python library for control systems work in academia and industry. Install it if you need to analyze or design linear feedback control systems.
Install
control on PyPI
Before you install
Low friction installation with a pure Python wheel. Actively maintained with recent releases; last commit 2026-08-14. Requires numpy, scipy, and matplotlib as runtime dependencies. Optional slycot dependency adds FORTRAN-based functionality but is not required for core features.
Requires Python 3.10 or later. Optional slycot dependency requires a C and Fortran compiler if installed via pip.
License in practice
Released under BSD-3-Clause (permissive). No restrictions on commercial use, modification, or redistribution provided the license text accompanies the code.
Quickstart
pip install control
import control as ct
sys = ct.TransferFunction([2], [2, 2])
ct.step_response(sys)Verify before relying
- Whether slycot binaries are available for your platform via conda-forge or pip
- Performance characteristics for large-scale systems or real-time applications
Package facts
| License | BSD-3-Clause permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagesnumpyscipymatplotlib |
| Maintenance | Actively maintained 405 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 282,815 / month, #8,084 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersIntended Audience :: Science/ResearchOperating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: POSIXOperating System :: UnixProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Topic :: Scientific/EngineeringTopic :: Software Development |
Evidence: control-0.10.2-py3-none-any.whl
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