Metadata-Version: 2.1
Name: do-mpc
Version: 4.2.0
Summary: UNKNOWN
Home-page: https://www.do-mpc.com
Author: Sergio Lucia and Felix Fiedler
Author-email: sergio.lucia@tu-berlin.de
License: GNU LESSER GENERAL PUBLIC LICENSE
Description: <img align="left" width="30%" hspace="2%" src="https://raw.githubusercontent.com/do-mpc/do-mpc/master/documentation/source/static/dompc_var_02_rtd_blue.png">
        
        # Model predictive control python toolbox
        
        [![Documentation Status](https://readthedocs.org/projects/do-mpc/badge/?version=latest)](https://www.do-mpc.com)
        [![Build Status](https://travis-ci.org/do-mpc/do-mpc.svg?branch=master)](https://travis-ci.org/do-mpc/do-mpc)
        [![PyPI version](https://badge.fury.io/py/do-mpc.svg)](https://badge.fury.io/py/do-mpc)
        [![awesome](https://img.shields.io/badge/awesome-yes-brightgreen.svg?style=flat-square)](https://github.com/do-mpc/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.
        
        In summary, **do-mpc** offers the following features:
        
        * nonlinear and economic model predictive control
        * support for differential algebraic equations (DAE)
        * time discretization with orthogonal collocation on finite elements
        * robust multi-stage model predictive control
        * moving horizon state and parameter estimation
        * modular design that can be easily extended
        
        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](https://pas.bci.tu-dortmund.de) (PAS) of the TU Dortmund by Felix Fiedler and Sergio Lucia.
        
        ## Installation instructions
        Installation instructions are given [here](https://www.do-mpc.com/en/latest/installation.html).
        
        ## Documentation
        Please visit our extensive [documentation](https://www.do-mpc.com), kindly hosted on readthedocs.
        
        ## Citing **do-mpc**
        If you use **do-mpc** for published work please cite it as:
        
        S. Lucia, A. Tatulea-Codrean, C. Schoppmeyer, and S. Engell. Rapid development of modular and sustainable nonlinear model predictive control solutions. Control Engineering Practice, 60:51-62, 2017
        
        Please remember to properly cite other software that you might be using too if you use **do-mpc** (e.g. CasADi, IPOPT, ...)
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
