# Shunichi09/PythonLinearNonlinearControl

PythonLinearNonLinearControl is a library implementing the linear and nonlinear control theories in python.

Repository: https://github.com/Shunichi09/PythonLinearNonlinearControl
Canonical: https://ross.abutalabs.com/products/pythonlinearnonlinearcontrol
Language: Python
License Family: other
Topics: optimal-control, model-predictive-control, linear-control, model-based-rl, mpc, nmpc, ilqr, mppi, cem, ddp, reinforcement-learning, nonlinear-control
Last push: 2021-08-23T12:57:01+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 2817, "days_push": 1836, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1041, forks 190 (observed 2026-08-28T04:03:20.537404+00:00)

## What it is
A Python library implementing linear and nonlinear control theory algorithms such as MPC, iLQR, DDP, MPPI, and CEM using only NumPy and SciPy. It ships reference implementations with example environments like cartpole and two-wheeled tracking for studying model-based control and model-based reinforcement learning.

## Use cases
- implement model predictive control in python
- compare mpc ilqr ddp and mppi controllers
- learn nonlinear control theory with runnable examples
- prototype model-based reinforcement learning controllers
- simulate cartpole with a python control algorithm
- extend a control algorithm with custom dynamics and costs

## When to choose
- you want pure-python reference implementations of classic and sampling-based control algorithms
- you need a lightweight codebase built only on numpy and scipy that is easy to modify
- you are studying or teaching optimal control and model-based RL methods
- you want to test controllers on simple environments like cartpole or a two-wheeled robot

## When to avoid
- you need real-time or hard real-time control performance in compiled code
- you require a maintained production library with active releases and support
- you need GPU acceleration or large-scale robotics stacks like ROS integration
- you want a no-code or GUI control design tool

## Facets
- artifact type: library
- maturity: maintenance
- function: simulation, machine-learning, reinforcement-learning, math, developer-tools
- domain: robotics, reinforcement-learning, simulation, education
- platform: python, cross-platform
- tags: model-predictive-control, mpc, nmpc, ilqr, ddp, mppi, cem, optimal-control, control-theory, model-based-rl, cartpole, scipy, numpy, algorithms

## Member repositories
- Shunichi09/PythonLinearNonlinearControl (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:20.537404+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:02:52.432337+00:00, confidence not recorded.
  - readme: https://github.com/Shunichi09/PythonLinearNonlinearControl (fetched 2026-08-28T04:03:20.537404+00:00, sha 8aea6382044b)
  - registry_pypi: https://pypi.org/pypi/pythonlinearnonlinearcontrol/json (fetched 2026-08-29T13:04:08.761124+00:00, sha 8e7673223c58)
- Data as of 2026-08-30T08:39:29.467469+00:00.