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

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.

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Cited by in corpus (15)

• Whole-Body Nonlinear Model Predictive Control Through Contacts for Quadrupeds
• LQR-Assisted Whole-Body Control of a Wheeled Bipedal Robot with Kinematic Loops
• MATMPC - A MATLAB Based Toolbox for Real-time Nonlinear Model Predictive Control
• Model Hierarchy Predictive Control of Robotic Systems
• Automatic Differentiation of Rigid Body Dynamics for Optimal Control and Estimation
• PyPose: A Library for Robot Learning with Physics-based Optimization
• TriFinger: An Open-Source Robot for Learning Dexterity
• A Projection Approach to Equality Constrained Iterative Linear Quadratic Optimal Control
• GRiD: GPU-Accelerated Rigid Body Dynamics with Analytical Gradients
• Chronos and CRS: Design of a miniature car-like robot and a software framework for single and multi-agent robotics and control
• Follow the Gradient: Crossing the Reality Gap using Differentiable Physics (RealityGrad)
• Optimal Control for Structurally Sparse Systems using Graphical Inference
• Robust Footstep Planning and LQR Control for Dynamic Quadrupedal Locomotion
• The Adaptive Dynamic Programming Toolbox
• Collision Avoidance with Stochastic Model Predictive Control for Systems with a Twofold Uncertainty Structure