Metadata-Version: 2.1
Name: do_mpc
Version: 4.1.0
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)
        
        **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 IOT chair of the TU Berlin 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
