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However, when it comes to control and especially Numerical Optimal Control, there are not
#MODEL PREDICTIVE CONTROL TOOLBOX SOFTWARE#
In creating software tools and libraries for robotics. Software is one of the key building blocks for robotic systems and there is a great effort Robotics Lab at ETH Zurich, but is continuously extended to cover more fields of applications and algorithms. The project originated from research conducted at the Agile & Dexterous Hardware experiments, demonstrations and academic publications.Įxample hardware applications are online trajectory optimization with collisionĪvoidance \cite giftthaler2017autodiff, trajectory optimization for Quadrupeds \cite neunert:2017:ralĪnd mobile manipulators \cite giftthaler2017efficient as well as NMPC on ground robots \cite neunert2017mpcĪnd UAVs \cite neunert16hexrotor. The CT has been successfully used in a variety of different projects, including a large number of
#MODEL PREDICTIVE CONTROL TOOLBOX CODE#
Other modules are independent of the a particular modelling framework, allowing the code to be interfaced While we provide an interface to a state-of-the art Auto-Diff compatible robot modelling software, all Nonlinear model predictive control (NMPC) or numerical optimal control easily and with minimal effort. We designed the toolbox with usability in mind, allowing users to apply advanced concepts such as The CT supports Automatic Differentiation (Auto-Diff) and allows to generate derivative codeįor arbitrary scalar and vector-valued functions. A major contribution of the CT is its implementations of optimal controlĪlgorithms, spanning a range from simple LQR reference implementations to constrained model predictiveĬontrol. It is written entirely in C++ and hasĪ strong focus on highly efficient code that can be run online (in the loop) on robots or otherĪctuated hardware. The Control Toolbox is specifically designed for these tasks. Information, formulating cost functions and constraints or running controllers in model-predictive Sooner or later, one is confronted with questions of efficient implementation, computing derivative Is to model systems, implement equations of motion and design model-based controllers, estimators, What is the CT?Ī common tasks for researchers and practitioners in both the control and the robotics communities Slightly more complex optimization examples, including gait optimization for a quadruped, are availabe in ct_ros. The Control Toolbox has been used for Hardware and Simulation control tasks on flying, walking and ground robots. derivative code generation for maximum efficiency.automatic differentiation and code generation of rigid body dynamics.
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first and second order automatic differentiation of arbitrary vector-valued functions including cost functions and constraints.implementation of a basic nonlinear-programming inverse kinematics solver for fix-base robots.straight-forward interface to the state-of the art rigid body dynamics modelling tool RobCoGen.Robot Modelling, Rigid Body Kinematics and Dynamics: solve large scale optimal control problems in MPC fashion.standardized interfaces for the solvers.Classical Direct Multiple Shooting (DMS).iLQR / iLQG (iterative Linear Quadratic Optimal Control).common interfaces for optimal control solvers and nonlinear model predictive control.intuitive modelling of cost functions and constraints.Trajectory optimization, optimal control and (nonlinear) model predictive control: intuitive modelling of systems governed by ordinary differential or difference equations.The CT was designed with the following features in mind: The library contains several tools to design and evaluate controllers, model dynamical systems and solve optimal control problems. This page outlines its general concept, its major building blocks and highlights selected application examples. The CT is applicable to a broad class of dynamic systems, but features additional modelling tools specially designed for robotics. This is the ADRL Control Toolbox ('CT'), an open-source C++ library for efficient modelling, control,Įstimation, trajectory optimization and model predictive control. This is the Control Toolbox, an efficient C++ library for control, estimation, optimization and motion planning in robotics.įind the detailed documentation here. Important note (July 2021): this library is currently only scarcely maintained, - it may take a while until we respond to bugs or feature requests.