moving-horizon-estimation

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 and moving horizon estimation 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".

MATLAB benchmark for coupled nonlinear aeroelastic simulation and control of a Pazy-wing aircraft, with scheduled ROMs, LQR, nMHE, nMPC, actuator dynamics, and native acceleration.

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.

NeuralMPCX is a Python library for building and deploying Model Predictive Controllers with linear, nonlinear, and neural dynamics. The software interfaces CasADi and IPOPT to solve constrained optimal control problems with recurrent neural networks (RNN, LSTM) and state-space systems.

Distributed MPC + MHE for quaternion-hexacopter and fixed-wing UAV clusters — GNSS-denied resilience, 210-run Monte-Carlo study, interactive 3D replay. The per-agent real-time-iteration NMPC controller and MHE estimator used for the in-the-loop and timing results are an extension of my MSc thesis.

Header-only C++17 library and benchmark of 86 state estimators and observers: Kalman, adaptive, robust, particle, moving-horizon, smoothers and attitude, all behind one model-agnostic interface. No heap, no exceptions, float32-ready, Cortex-M7 verified. ~9,000 benchmark runs, 958-page report.

State estimation with Kalman filters (KF/EKF/UKF) and Moving Horizon Estimation on a thermal lab kit - course project report and MATLAB code