overlesno.blogg.se - Model predictive control toolbox

It is written entirely in C++ and has a strong focus on highly efficient code that can be run online (in the loop) on robots or other actuated hardware. The Control Toolbox is specifically designed for these tasks. Sooner or later, one is confronted with questions of efficient implementation, computing derivative information, formulating cost functions and constraints or running controllers in model-predictive control fashion. What is the CT?Ī common tasks for researchers and practitioners in both the control and the robotics communities is to model systems, implement equations of motion and design model-based controllers, estimators, planning algorithms, etc. 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.

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, estimation, 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.