6533b7dcfe1ef96bd12733db

RESEARCH PRODUCT

Feasibility Analysis For Constrained Model Predictive Control Based Motion Cueing Algorithm

Carolina RengifoJean-rémy ChardonnetDamien PaillotAndras KemenyHakim Mohellebi

subject

0209 industrial biotechnology021103 operations researchComputer scienceDriving simulationControl (management)0211 other engineering and technologiesStability (learning theory)Driving simulator02 engineering and technologyModélisation et simulation [Informatique]Motion controlOptimal control[INFO.INFO-MO]Computer Science [cs]/Modeling and SimulationAutomatique / Robotique [Sciences de l'ingénieur]Motion (physics)[SPI.AUTO]Engineering Sciences [physics]/AutomaticModel predictive controlAcceleration020901 industrial engineering & automationMotion Cueing AlgorithmAlgorithmModel Predictive Control

description

International audience; This paper deals with motion control for an 8-degree-of-freedom (DOF) high performance driving simulator. We formulate a constrained optimal control that defines the dynamical behavior of the system. Furthermore, the paper brings together various methodologies for addressing feasibility issues arising in implicit model predictive control-based motion cueing algorithms.The implementation of different techniques is described and discussed subsequently. Several simulations are carried out in the simulator platform. It is observed that the only technique that can provide ensured closed-loop stability by assuring feasibility over all prediction horizons is a braking law that basically saturates the control inputs in the constrained form.

yearjournalcountryeditionlanguage
2019-05-20
10.1109/icra.2019.8794129https://hdl.handle.net/10985/16315