A genetic algorithm–based nonlinear scaling method for optimal motion cueing algorithm in driving simulator. (August 2018)

• Record Type: Journal Article
• Authors: Asadi, Houshyar; Lim, Chee Peng; Mohammadi, Arash; Mohamed, Shady; Nahavandi, Saeid; Shanmugam, Lakshmanan
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 232:Number 8(2018)
• Page Start: 1025
• Page End: 1038
• Publication Date: 2018-08
• Subjects: Motion cueing algorithm -- nonlinear scaling -- human sensation -- washout filter -- genetic algorithm; Mechanical engineering -- Periodicals; Automatic control -- Periodicals; Systems engineering -- Periodicals; 621.3
• DOI: 10.1177/0959651818772940
• Languages: English
• ISSNs: 0959-6518

Abstract

A motion cueing algorithm plays an important role in generating motion cues in driving simulators. The motion cueing algorithm is used to transform the linear acceleration and angular velocity of a vehicle into the translational and rotational motions of a simulator within its physical limitation through washout filters. Indeed, scaling and limiting should be used along within the washout filter to decrease the amplitude of the translational and rotational motion signals uniformly across all frequencies through the motion cueing algorithm. This is to decrease the effects of the workspace limitations in the simulator motion reproduction and improve the realism of movement sensation. A nonlinear scaling method based on the genetic algorithm for the motion cueing algorithm is developed in this study. The aim is to accurately produce motions with a high degree of fidelity and use the platform more efficiently without violating its physical limitations. To successfully achieve this aim, a third-order polynomial scaling method based on the genetic algorithm is formulated, tuned, and implemented for the linear quadratic regulator–based optimal motion cueing algorithm. A number of factors, which include the sensation error between the real and simulator drivers, the simulator's physical limitations, and the sensation signal shape-following criteria, are considered in optimizing the proposed nonlinear scaling method. The results show that the proposed method not only is able to overcome problems pertaining to selecting nonlinear scaling parameters based on trial-and-error and inefficient usage of the platform workspace, but also to reduce the sensation error between the simulator and real drivers, while satisfying the constraints imposed by the platform boundaries.