%0 Generic %1 giftthaler2018control %A Giftthaler, Markus %A Neunert, Michael %A Stäuble, Markus %A Buchli, Jonas %D 2018 %K 2018 arxiv c++ control-theory cpp dynamics library opensource robotics %T The Control Toolbox - An Open-Source C++ Library for Robotics, Optimal and Model Predictive Control %U http://arxiv.org/abs/1801.04290 %X We introduce the Control Toolbox (CT), an open-source C++ library for efficient modelling, control, estimation, trajectory optimization and model predictive control. The CT is applicable to a broad class of dynamic systems, but features additional modelling tools specially designed for robotics. This paper outlines its general concept, its major building blocks and highlights selected application examples. The CT was designed for intuitive modelling of systems governed by ordinary differential- or difference equations. It supports rapid prototyping of cost functions and constraints and provides common interfaces for different optimal control solvers. To date, we support Single Shooting, the iterative Linear-Quadratic Regulator, Gauss-Newton Multiple Shooting and classical Direct Multiple Shooting. We provide interfaces to different NLP and linear-quadratic solvers, such as IPOPT, SNOPT, HPIPM, or a custom Riccati solver. The CT was designed with performance for online control in mind and allows to solve large-scale optimal control problems highly efficiently. Some of the key features enabling fast run-time performance are full support for Automatic Differentiation, derivative code generation and thorough multi-threading. For robotics problems, the we offer an interface to a fully auto-differentiable rigid-body dynamics modelling engine. In combination with derivative code generation, this allows for an unprecedented performance in solving optimal control problems for complex articulated robotic systems.

@misc{giftthaler2018control, abstract = {We introduce the Control Toolbox (CT), an open-source C++ library for efficient modelling, control, estimation, trajectory optimization and model predictive control. The CT is applicable to a broad class of dynamic systems, but features additional modelling tools specially designed for robotics. This paper outlines its general concept, its major building blocks and highlights selected application examples. The CT was designed for intuitive modelling of systems governed by ordinary differential- or difference equations. It supports rapid prototyping of cost functions and constraints and provides common interfaces for different optimal control solvers. To date, we support Single Shooting, the iterative Linear-Quadratic Regulator, Gauss-Newton Multiple Shooting and classical Direct Multiple Shooting. We provide interfaces to different NLP and linear-quadratic solvers, such as IPOPT, SNOPT, HPIPM, or a custom Riccati solver. The CT was designed with performance for online control in mind and allows to solve large-scale optimal control problems highly efficiently. Some of the key features enabling fast run-time performance are full support for Automatic Differentiation, derivative code generation and thorough multi-threading. For robotics problems, the we offer an interface to a fully auto-differentiable rigid-body dynamics modelling engine. In combination with derivative code generation, this allows for an unprecedented performance in solving optimal control problems for complex articulated robotic systems.}, added-at = {2018-03-15T15:30:33.000+0100}, author = {Giftthaler, Markus and Neunert, Michael and Stäuble, Markus and Buchli, Jonas}, biburl = {https://www.bibsonomy.org/bibtex/2f0fe40c7af6426b00ce06eee09842f75/achakraborty}, description = {[1801.04290] The Control Toolbox - An Open-Source C++ Library for Robotics, Optimal and Model Predictive Control}, interhash = {4c47e372aef13e55bdc83dff38018332}, intrahash = {f0fe40c7af6426b00ce06eee09842f75}, keywords = {2018 arxiv c++ control-theory cpp dynamics library opensource robotics}, note = {cite arxiv:1801.04290}, timestamp = {2018-03-15T15:30:33.000+0100}, title = {The Control Toolbox - An Open-Source C++ Library for Robotics, Optimal and Model Predictive Control}, url = {http://arxiv.org/abs/1801.04290}, year = 2018 }