The Control Toolbox - An Open-Source C++ Library for Robotics, Optimal and Model Predictive Control

TL;DR

The Control Toolbox is an open-source C++ library that facilitates modeling, control, and optimization of dynamic systems, especially for robotics, supporting various solvers and enabling efficient real-time control.

Contribution

It introduces a modular, user-friendly C++ library with extensive features for modeling, control, and optimization tailored for robotic applications and large-scale problems.

Findings

Supports multiple optimal control solvers including Shooting and LQ regulators.

Enables fast runtime performance with automatic differentiation and multi-threading.

Provides interfaces for modeling, control, and estimation in robotics.

Abstract

We introduce the Control Toolbox (CT), an open-source C++ library for efficient modeling, control, estimation, trajectory optimization and Model Predictive Control. The CT is applicable to a broad class of dynamic systems but features interfaces to modeling tools specifically designed for robotic applications. This paper outlines the general concept of the toolbox, its main building blocks, and highlights selected application examples. The library contains several tools to design and evaluate controllers, model dynamical systems and solve optimal control problems. The CT was designed for intuitive modeling of systems governed by ordinary differential or difference equations. It supports rapid prototyping of cost functions and constraints and provides standard 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 general purpose NLP solvers and Riccati-based linear-quadratic optimal control solvers. The CT was designed to solve large-scale optimal control and estimation problems efficiently and allows for online control of dynamic systems. Some of the key features to enable fast run-time performance are full compatibility with Automatic Differentiation, derivative code generation, and multi-threading. Still, the CT is designed as a modular framework whose building blocks can also be used for other control and estimation applications such as inverse dynamics control, extended Kalman filters or kinematic planning.