DO-MPC

do-mpc is a comprehensive open-source toolbox for robust model predictive control (MPC) and moving horizon estimation (MHE). do-mpc enables the efficient formulation and solution of control and estimation problems for nonlinear systems, including tools to deal with uncertainty and time discretization. The modular structure of do-mpc contains simulation, estimation and control components that can be easily extended and combined to fit many different applications... The do-mpc software is Python based and works therefore on any OS with a Python 3.x distribution. do-mpc has been developed by Sergio Lucia and Alexandru Tatulea at the DYN chair of the TU Dortmund lead by Sebastian Engell. The development is continued at the Laboratory of Process Automation Systems (PAS) of the TU Dortmund by Felix Fiedler and Sergio Lucia.