Fast and embedded solvers for nonlinear optimal control and nonlinear model predictive control

HILO-MPC is a Python toolbox for easy, flexible and fast development of machine-learning-supported optimal control and estimation problems

An open source model predictive control package for Julia

An optimization framework that links CasADi, Ipopt, ACADOS and biorbd for Optimal Control Problem

Implementation of Kalman Filter, Extended Kalman Filter and Moving Horizon Estimation to the stirred tank mixing process.

Nonlinear Model Predictive Control (NMPC) based on CVXPY and JAX in Python

path-planning|control|sensor-fusion algorithmic components for mobile robotics

Source code of paper "Self-tuning moving horizon estimation of nonlinear systems via physics-informed machine learning Koopman modeling".

Rotor's dynamics calibration tests combining XBot2 rt plugins and a simple approach to linear MHE.

Nonlinear state estimation in Python: extended Kalman filter and moving-horizon estimator with CasADi automatic-differentiation Jacobians, validated by 100-trial Monte-Carlo simulation, statistical consistency checks, and parameter-sensitivity analysis.