Systems Theory and Automatic Control



Robot Model Library

    We developed a software library that provides the dynamic model of a KUKA Lightweight Robot IV (LWR IV) in two forms. The first one calculates the matrices and vectors of the inverse dynamics model required by many model-based motion controller. The second form computes a simulation model (and the respective Jacobian matrix) to be used by SUNDIALS, an implicit solver of ordinary differential equation.
    The models are computed in pure C. Additionally, a Python interface is included for easy prototyping.

    You can download the software here.

    Consider citing our paper:
    V. Bargsten, P. Zometa, and R. Findeisen. "Modeling, parameter identification and model-based control of a robotic manipulator." In: Control Applications (CCA), IEEE International Conference on. pp. 134-139. IEEE. 2013.
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Optimal Parameter Identification



Optimal trajectories for the identification of the parameters of a dynamic model of the KUKA Lightweight Robot IV.

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3-D Simulation of Dynamics



Simulation of a trajectory tracking motion controller (also see video below).
    Controller: feedback linearization.
    Trajectory: a circle in a vertical plane.
    Hardware: a computer based on a Intel Xeon processor.
    Software: The controller was implemented in Python, using the dynamic model we developed. SUNDIALS is used as implicit ODE solver. The 3-D simulation is based on Blender. All the development was made using free software.

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Model-based motion controller: trajectory tracking



Implementation of a model-based trajectory tracking controller. The motion controller linearizes the highly nonlinear dynamics via feedback linearization. We additionally use a linear controller to stabilize the system. Each joint behaves as a critically damped mass-spring-damper system, which is clearly seen after the disturbances introduced by hand.
    Controller: feedback linearization.
    Trajectory: a circle in a vertical plane.
    Hardware: KUKA LWR IV together with computer based on a Intel Xeon processor.
    Software: The controller was implemented in Python, using the dynamic model we developed to linearize the system dynamics.

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Path-following MPC

More information about this topic can be found here

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