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20182026
most citedVQF: Highly Accurate IMU Orientation Estimation with Bias Estimation and Magnetic Disturbance Rejection

73 citations · 223 across the 16 of their papers we have counts for

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eess.SY2022★ 73 cited

VQF: Highly Accurate IMU Orientation Estimation with Bias Estimation and Magnetic Disturbance Rejection

Daniel Laidig, Thomas Seel

The miniaturization of inertial measurement units (IMUs) facilitates their widespread use in a growing number of application domains. Orientation estimation is a prerequisite for m…

eess.SY2021★ 21 cited

Collective Iterative Learning Control: Exploiting Diversity in Multi-Agent Systems for Reference Tracking Tasks

Michael Meindl, Fabio Molinari, Dustin Lehmann +1

Multi-agent systems (MASs) can autonomously learn to solve previously unknown tasks by means of each agent's individual intelligence as well as by collaborating and exploiting coll…

eess.SY2021★ 14 cited

Observability of the relative motion from inertial data in kinematic chains

Manon Kok, Karsten Eckhoff, Ive Weygers +1

Real-time motion tracking of kinematic chains is a key prerequisite in the control of, e.g., robotic actuators and autonomous vehicles and also has numerous biomechanical applicati…

eess.SY2020★ 5 cited

Iterative learning control in prosumer-based microgrids with hierarchical control

Lia Strenge, Xiaohan Jing, Ruth Boersma +5

Power systems are subject to fundamental changes due to the increasing infeed of renewable energy sources. Taking the accompanying decentralization of power generation into account…

eess.SY2020★ 17 cited

Sparse Magnetometer-free Inertial Motion Tracking -- A Condition for Observability in Double Hinge Joint Systems

Karsten Eckhoff, Manon Kok, Sergio Lucia +1

Inertial measurement units are commonly used in a growing number of application fields to track or capture motions of kinematic chains, such as human limbs, exoskeletons or robotic…

eess.SY2020★ 6 cited

Overcoming Output Constraints in Iterative Learning Control Systems by Reference Adaptation

Michael Meindl, Fabio Molinari, Jörg Raisch +1

Iterative Learning Control (ILC) schemes can guarantee properties such as asymptotic stability and monotonic error convergence, but do not, in general, ensure adherence to output c…

eess.SY2020★ 19 cited

Magnetometer-free inertial motion tracking of arbitrary joints with range of motion constraints

Dustin Lehmann, Daniel Laidig, Raphael Deimel +1

In motion tracking of connected multi-body systems Inertial Measurement Units (IMUs) are used in a wide variety of applications, since they provide a low-cost easy-to-use method fo…

eess.SY2019★ 10 cited

Hierarchical Event-triggered Learning for Cyclically Excited Systems with Application to Wireless Sensor Networks

Jonas Beuchert, Friedrich Solowjow, Jörg Raisch +2

Communication load is a limiting factor in many real-time systems. Event-triggered state estimation and event-triggered learning methods reduce network communication by sending inf…

eess.SY2019★ 1 cited

Distributed Bio-inspired Humanoid Posture Control

Vittorio Lippi, Fabio Molinari, Thomas Seel

This paper presents an innovative distributed bio-inspired posture control strategy for a humanoid, employing a balance control system DEC (Disturbance Estimation and Compensation)…

eess.SY2019

Joint axis estimation for fast and slow movements using weighted gyroscope and acceleration constraints

Fredrik Olsson, Thomas Seel, Dustin Lehmann +1

Sensor-to-segment calibration is a crucial step in inertial motion tracking. When two segments are connected by a hinge joint, for example in human knee and finger joints as well a…

eess.SY2018★ 12 cited

Design of a Networked Controller for a Two-Wheeled Inverted Pendulum Robot

Zenit Music, Fabio Molinari, Sebastian Gallenmüller +6

The topic of this paper is to use an intuitive model-based approach to design a networked controller for a recent benchmark scenario. The benchmark problem is to remotely control a…