Optimal tracking benchmark and optimized filter evaluation methods for motion drive algorithms in driving simulators

Motion drive algorithms (MDA) provide a tradeoff between simulation of motion cues and using the motion states of the simulator optimally. Although many proposed motion drive algorithms claim to be optimal in the sense of motion cue tracking, several do not take into account the necessary motion required to achieve this control.;This thesis will outline a method that will provide the best possible motion displacement (e.g. minimum required displacement) for perfect motion cueing. This method uses a Linear Optimal Tracking Control (LOTC) based on prior knowledge of the input maneuver and can be used as a benchmark to compare other motion drive algorithms in terms of displacement and motion efficiency.;The thesis will also outline a method to evaluate the coefficients used in the low and high-pass filters in a classical version of the motion drive algorithm based on a MATLAB optimization routine to control various quantitative states.