Feasibility Analysis For Constrained Model Predictive Control Based Motion Cueing Algorithm
| Affiliation auteurs | !!!! Error affiliation !!!! |
| Titre | Feasibility Analysis For Constrained Model Predictive Control Based Motion Cueing Algorithm |
| Type de publication | Conference Paper |
| Year of Publication | 2019 |
| Auteurs | Rengifo C, Chardonnet J-R, Mohellebi H, Paillot D, Kemeny A |
| Editor | Howard A, Althoefer K, Arai F, Arrichiello F, Caputo B, Castellanos J, Hauser K, Isler V, Kim J, Liu H, Oh P, Santos V, Scaramuzza D, Ude A, Voyles R, Yamane K, Okamura A |
| Conference Name | 2019 INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA) |
| Publisher | Bosch; DJI; Kinova; Mercedes Benz; Samsung; Argo AI; Clearpath Robot; Element AI; Fetch Robot; Huawei; iRobot; KUKA; Quanser; SICK; Toyota Res Inst; Uber; Waymo; Zhejiang Lab; Amazon; Applanix; Cloudminds; Honda Res Inst; MathWorks; Ouster |
| Conference Location | 345 E 47TH ST, NEW YORK, NY 10017 USA |
| ISBN Number | 978-1-5386-6026-3 |
| Résumé | 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. |