Update README.md

README.md

@@ -1,22 +1,19 @@
-![do_mpc](documentation/source/static/dompc_var_02_rtd_blue.svg)
+<img align="left" src="documentation/source/static/dompc_var_02_rtd_blue.svg">
 
+# do-mpc: Robust optimal control toolbox
 ![Documentation Status](https://readthedocs.org/projects/do-mpc/badge/?version=latest)
 [![Build Status](https://travis-ci.org/do-mpc/do-mpc.svg?branch=master)](https://travis-ci.org/do-mpc/do-mpc)
 
-## Introduction
+**do-mpc** proposes a new, modularized implementation for optimization based 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.
 
-**do-mpc** proposes a new, modularized implementation for optimization based model predictive control (MPC) and moving horizon estimation (MHE).
-The goal of this software project is to offer a simple to use and efficient platform,
-which allows users to define and test their problems very fast and trouble-free.
-In most cases, such implementations are highly complex and cumbersome,
-requiring considerable coding effort that only produces hard-coded solutions for each individual test case.
-With **do-mpc** we propose a generalized approach:
-The **do-mpc** model class is configured to represent the investigated system and is at the core of the **do-mpc** simulator, MHE and MPC.
-These modules can be easily configured and work independently or in conjunction.
+In summary, **do-mpc** offers the following features:
 
-A core feature of **do-mpc** is the simple framework for the implementation of a state-of-the art **robust nonlinear model predictive control** approach called multi-stage NMPC, which is based on the description of the uncertainty as a scenario tree.
+* nonlinear and economic model predictive control
+* 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 at the DYN chair of the TU Dortmund by Sergio Lucia and Alexandru Tatulea.
+The **do-mpc** software is Python based and works therefore on any OS with a Python 3.x distribution. **do-mpc** has been developed at the DYN chair of the TU Dortmund by Sergio Lucia and Alexandru Tatulea. 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://do-mpc.readthedocs.io/en/latest/installation.html).