do-mpc/do-mpc

Health v2 · maintenance only

Adoption not part of the score

1443 stars · 222 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

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

library · maturity active

simulation math machine-learning simulation python cross-platform model-predictive-control moving-horizon-estimation control-theory nonlinear-systems casadi optimization automation engineering algorithms

Member repositories

For agents

markdown · JSON · MCP: product_card(name="do-mpc/do-mpc")