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  • 1.
    Hedberg, Erik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Norrlöf, Mikael
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Moberg, Stig
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Gunnarsson, Svante
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Comparing Feedback Linearization and Jacobian Linearization for LQ Control of an Industrial Manipulator2018In: Proccedings of the 12TH IFAC SYMPOSIUM ON ROBOT CONTROL, 2018Conference paper (Refereed)
    Abstract [en]

    Feedback linearization is compared to Jacobian linearization for LQ control of atwo-link industrial manipulator. A method for obtaining equivalent nominal performance forboth control designs is introduced. An experimentally verified benchmark model with industrialrelevance is used for comparing the designs. Results do not show any conclusive advantages ofFeedback linearization.

  • 2.
    Hedberg, Erik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Norén, Johan
    ABB Robotics, Sweden.
    Norrlöf, Mikael
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering. ABB Robotics, Sweden.
    Gunnarsson, Svante
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Industrial Robot Tool Position Estimation using Inertial Measurements in a Complementary Filter and an EKF2017In: 20th IFAC World Congress, Elsevier, 2017, Vol. 50, p. 12748-12752Conference paper (Refereed)
    Abstract [en]

     In this work an Inertial Measurement Unit is used to improve tool position estimates for an ABB IRB 4600 industrial robot, starting from estimates based on motor angle forward kinematics. A Complementary Filter and an Extended Kalman Filter are investigated. The Complementary Filter is found to perform on par with the Extended Kalman Filter while having lower complexity both in the tuning process and the filtering computations.

  • 3.
    Heirich, Oliver
    et al.
    ] DLR German Aerosp Ctr, Inst Commun & Nav, Oberpfaffenhofen, Germany.
    Siebler, Benjamin
    ] DLR German Aerosp Ctr, Inst Commun & Nav, Oberpfaffenhofen, Germany.
    Hedberg, Erik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Study of Train-Side Passive Magnetic Measurements with Applications to Train Localization2017In: Journal of Sensors, ISSN 1687-725X, E-ISSN 1687-7268, Vol. 2017, article id 8073982Article in journal (Refereed)
    Abstract [en]

    Passive magnetic sensors measure the magnetic field density in three axes and are often integrated on a single chip. These low-cost sensors are widely used in car navigation as well as in battery powered navigation equipment such as smartphones as part of an electronic compass. We focus on a train localization application with multiple, exclusively onboard sensors and a track map. This approach is considered as a base technology for future railway applications such as collision avoidance systems or autonomous train driving. In this paper, we address the following question: how beneficial are passive magnetic measurements for train localization? We present and analyze measurements of two different magnetometers recorded on a regional train at regular passenger service. We show promising correlations of the measurements with the track positions and the traveled switch way. The processed data reveals that the railway environment has repeatable, location-dependent magnetic signatures. This is considered as a novel approach to train localization, as the use of these magnetic signals at first view is not obvious. The proposed methods based on passive magnetic measurements show a high potential to be integrated in new and existing train localization approaches.

  • 4.
    Johansson, Viktor
    et al.
    AstaZero, Sweden.
    Moberg, Stig
    ABB AB, Sweden.
    Hedberg, Erik
    Linköping University, Faculty of Science & Engineering. Linköping University, Department of Electrical Engineering, Automatic Control.
    Norrlöf, Mikael
    ABB AB, Sweden.
    Gunnarsson, Svante
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    A learning approach for feed-forward friction compensation2018In: Proceedings of the 12th IFAC Symposium on Robot Control, 2018Conference paper (Other academic)
    Abstract [en]

    An experimental comparison of two feed-forward based frictioncompensation methods is presented. The first method is based on theLuGre friction model, using identified friction model parameters, andthe second method is based on B-spline network, where the networkweights are learned from experiments. The methods are evaluated andcompared via experiments using a six axis industrial robot carryingout circular movements of different radii. The experiments show thatthe learning-based friction compensation gives an error reduction ofthe same magnitude as for the LuGre-based friction compensation.

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