Adaptive Motion Cueing Algorithm Using Optimized Fuzzy Control System for Motion Simulators

The Motion Cueing Algorithm (MCA) is the main unit of motion simulators responsible for transforming the vehicle motions to generate driving motion sensation for the simulator users within the motion simulator's physical workspace through washout filters. In this study, we design and provide a new framework by developing a set of novel washout filters using optimized fuzzy control systems to solve the drawbacks associated with the existing optimal MCAs of the motion simulators. These drawbacks include constant washout filter parameters, inefficient simulator movement in the workspace, lack of considering the simulator's physical constraints and driver sensation-related factors, and sub-optimal settings which causes false motion cues and subsequently simulator sickness. As a pioneering framework, the fuzzy logic controllers generate restitution signals for the optimal washout filters based on the motion sensation error between the real vehicle and simulator divers as well as the platform's position in the workspace aiming to correct the filtered signals, reduce error, generate realistic motions, and increase motion fidelity online. The GA is applied to optimize the fuzzy logic membership functions and control rules. The optimization procedure takes into account a number of important factors that are normally ignored in the current MCAs including the sensed motion error between the drivers of simulator and real car; the platform physical constraints; the motion threshold limiter effects; and signal shape following. The new adaptive MCA is designed, implemented and tested using the MATLAB/Simulink. The simulation results confirm the effectiveness of the developed MCA in maximizing motion fidelity and the reference sensation shape following, and enhancing the workspace usage of the simulator.