The modeling and control of motion perception algorithms for stewart platform-based simulators

In this thesis, the focus is on how simulators have evolved to prioritize simulating the sensation of acceleration, utilizing the limitations of human perception, rather than attempting to perfectly replicate real vehicle movement. This evolution involves the mathematical modeling of human perception and the application of these findings to engineering practices. Simulators are specifically designed to create the best possible sensation of acceleration within limited motion spaces. They take advantage of the fact that human perception is less sensitive to certain acceleration values and has difficulty differentiating between tilt and forward acceleration beyond a certain degree. These perceptual phenomena form the foundation of motion cueing algorithms, which have been refined over the past 50 years using various control theory approaches to achieve optimal results. This thesis compares the Classical Washout Algorithm with the Optimal Motion Cueing Algorithm, which is developed using a Linear Quadratic Regulator approach, through detailed simulations conducted in Matlab.