# do-mpc/do-mpc

Model predictive control python toolbox

Repository: https://github.com/do-mpc/do-mpc
Canonical: https://ross.abutalabs.com/products/do-mpc
Homepage: https://www.do-mpc.com/
Language: Python
License: LGPL-3.0
License Family: copyleft
Topics: mhe-es
Last push: 2025-10-31T09:44:17+00:00

## Health v2 (maintenance only)
Score: 61/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 49, release rhythm 54, longevity 100
- inputs: {"age_days": 4123, "days_push": 306, "days_rel": 306, "gap_med": 0, "n_releases_24m": 4}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1443, forks 222 (observed 2026-08-28T04:04:44.893134+00:00)

## What it is
do-mpc is an open-source Python toolbox for robust model predictive control (MPC) and moving horizon estimation (MHE) of nonlinear systems. It provides modular simulation, estimation, and control components with support for uncertainty handling, DAEs, and time discretization via orthogonal collocation.

## Use cases
- design nonlinear model predictive controllers in Python
- perform moving horizon state and parameter estimation
- simulate nonlinear systems with differential algebraic equations
- implement robust multi-stage MPC under uncertainty
- solve economic MPC problems for process control
- prototype control and estimation strategies for research

## When to choose
- you need nonlinear or economic MPC in Python
- you want robust MPC with uncertainty handling
- you need joint state and parameter estimation with MHE
- you work in process control, robotics, or energy systems research

## When to avoid
- you need simple PID or linear control without optimization
- you require real-time hard guarantees on embedded hardware
- you want a GUI-based control design tool

## Facets
- artifact type: library
- maturity: active
- function: simulation, math, machine-learning
- domain: simulation
- platform: python, cross-platform
- tags: model-predictive-control, moving-horizon-estimation, control-theory, nonlinear-systems, casadi, optimization, automation, engineering, algorithms

## Member repositories
- do-mpc/do-mpc (main) score 61

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:44.893134+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 2026-08-30T04:36:20.588382+00:00, confidence not recorded.
  - readme: https://github.com/do-mpc/do-mpc (fetched 2026-08-28T04:04:44.893134+00:00, sha d4311fa835d1)
- Data as of 2026-08-30T08:39:29.467469+00:00.