6533b7defe1ef96bd12765b1
RESEARCH PRODUCT
Applying particle swarm optimization to the motion-cueing-algorithm tuning problem
Inmaculada ComaMarcos FernándezCristina PortalésSergio Casassubject
050210 logistics & transportationOptimization problemComputer science0502 economics and business05 social sciences0202 electrical engineering electronic engineering information engineeringParticle swarm optimization020201 artificial intelligence & image processing02 engineering and technologyMulti-swarm optimizationAlgorithmMotion (physics)description
The MCA tuning problem consists in finding the best values for the parameters/coefficients of Motion Cueing Algorithms (MCA). MCA are used to control the movements of robotic motion platforms employed to generate inertial cues in vehicle simulators. This problem is traditionally approached with a manual pilot-in-the-loop subjective tuning, based on the opinion of several pilots/drivers. Instead, this paper proposes applying Particle Swarm Optimization (PSO) to solve this problem, using simulated motion platforms and objective indicators rather than subjective opinions. Results show that PSO-based tuning can provide a suitable solution for this complex optimization problem.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2017-07-15 | Proceedings of the Genetic and Evolutionary Computation Conference Companion |