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MPC-based motion cueing algorithm with short prediction horizon using exponential weighting

conference contribution
posted on 2016-01-01, 00:00 authored by Arash Mohammadi, Houshyar AsadiHoushyar Asadi, Shady MohamedShady Mohamed, Kyle Nelson, Saeid Nahavandi
A motion simulator is an effective tool for training
a driver in a safe environment by mimicking motion similar
to the real world. To give a realistic feeling of driving and
avoid motion sickness, an accurate motion cueing algorithm is
required to restrict the platform within the allowed workspace
range while regenerating an appropriate motion feeling for the
simulator driver. Recently, employing Model Predictive Control
(MPC) in the motion cueing algorithm has become popular. In
this control method, by predicting future dynamics, an input is
optimized to minimize a cost function over a prediction horizon
while respecting the constraints. Reducing the prediction horizon
is desirable to minimize the computational burden; however it
draws the system toward instability. In this research, applying a
nonuniform weighting method is proposed to stabilize the motion
cueing algorithm using MPC with short prediction horizon and
optimized weighting adjustment. Simulation results show the
effectiveness of the proposed method.

History

Event

Systems, Man, and Cybernetics. IEEE International Conference (2016 : Budapest, Hungary)

Pagination

1 - 6

Publisher

IEEE

Location

Budpest, Hungary

Place of publication

Piscataway, N.J.

Start date

2016-10-09

End date

2016-10-12

ISBN-13

9781509018970

Language

eng

Publication classification

E Conference publication; E1 Full written paper - refereed

Copyright notice

2016, IEEE

Title of proceedings

SMC 2016 : Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics