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Comparison of a Filter‐ and a Model Predictive Control Based Motion Cueing Algorithm
Author(s) -
Ellensohn Felix,
Schwienbacher Markus,
Venrooij Joost,
Rixen Daniel
Publication year - 2017
Publication title -
pamm
Language(s) - English
Resource type - Journals
ISSN - 1617-7061
DOI - 10.1002/pamm.201710361
Subject(s) - workspace , model predictive control , motion (physics) , computer science , motion simulator , control theory (sociology) , filter (signal processing) , simulation , motion control , motion capture , actuator , algorithm , artificial intelligence , control (management) , computer vision , robot
Motion Cueing Algorithms (MCA) include control strategies to take into account the motion‐based driving simulator's restrictions concerning workspace limits and dynamic boundaries. A typical 6‐DoF simulator consists of a motion system which exhibits three translational and rotational degrees of freedom. Its actuators are capable of realizing accelerations, velocities and positions in a limited range. Based on these facts MCAs aim to generating realistic simulations of the driving motion (such as a driving manoeuvre) in order to immerse persons in virtual environments provided by the simulator. Filter‐based, classical MCAs belong to the most applied algorithms and mainly consist of linear transfer functions. Whereas, Model Predictive Control (MPC) algorithms rest upon a reduced model of the technical system's dynamics and, optionally, a model of the human motion perception system. An optimization problem subject to the restrictions of the motion system predicts the control variables over a time horizon. This paper addresses the strengths and weaknesses of the two stated approaches with focus on Motion Cueing errors. These errors describe discrepancies between the motion that is to be simulated (e.g. a driving manoeuvre) and the motion that is eventually provided by the combination of the MCA and the motion simulator. On basis of the results, it gives an outlook why optimization based algorithms have a higher potential to improve driving simulation. (© 2017 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)