$\newcommand{\deriv}[2][]{\dfrac{\partial #1}{\partial #2}} \newcommand{\tderiv}[2][]{\tfrac{\partial #1}{\partial #2}} \newcommand{\fderiv}[2][]{\dfrac{d #1}{d #2}} \newcommand{\derivII}[3][]{\dfrac{\partial^2 #1}{\partial #2 \partial #3}} \newcommand{\tderivII}[3][]{\tfrac{\partial^2 #1}{\partial #2 \partial #3}} \newcommand{\derivIII}[4][]{\dfrac{\partial^3 #1}{\partial #2 \partial #3 \partial #4}} \newcommand{\tderivIII}[4][]{\tfrac{\partial^3 #1}{\partial #2 \partial #3 \partial #4}} \newcommand{\derivIV}[5][]{\dfrac{\partial^4 #1}{\partial #2 \partial #3 \partial #4 \partial #5}} \newcommand{\half}{\tfrac{1}{2}} % Macro for 1/2 \newcommand{\ud}{\mathrm{d}} \newcommand{\op}{\circ} \newcommand{\dq}{\dot{q}} \newcommand{\ddq}{\ddot{q}} \newcommand{\dt}{\Delta t}$

# Trep: Dynamic Simulation and Optimal Control¶

Release: v1.0.1 May 04, 2015

Trep is a Python module for modeling rigid body mechanical systems in generalized coordinates. It provides tools for calculating continuous and discrete dynamics (based on a midpoint variational integrator), and the first and second derivatives of both. Tools for trajectory optimization and other basic optimal control methods are also available.

You can find detailed installation instructions on our website. Many examples are included with the source code (browse online).

The API Reference has detailed documentation for each part of trep. We have also put together a detailed Tutorial that gives an idea of the capabilities and organization of trep by stepping through several example problems.

If you have any questions or suggestions, please head over to our project page.

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