Prepositioning of a Land Vehicle Simulation-Based Motion Platform Using Fuzzy Logic and Neural Network

Abstract

The motion cueing algorithms (MCAs) is the method that reproduces the motion sensation of the real vehicle for the users of simulation-based motion platforms (SBMPs). Classical MCA is the most common type of MCA due to its simplicity, easy tunning procedure, and higher speed. The fixed neutral position of the SBMP restricts the available workspace and makes the motions of the platform conservative. Then, the high frequency motion inputs cannot be reproduced precisely using SBMP and it increases the motion sensation error between the SBMP and real vehicle driver. The main objective of this study is to enlarge the reachable workspace of the SBMP adaptively using the new prepositioning technique based on fuzzy logic and artificial neural network (ANN). Prepositioning of the SBMPs to an off-centre point can increase the available linear space of the SBMPs based on different driving scenarios to decrease the motion sensation error. The fuzzy logic-based units are working based on the current position of the SBMP and the predicted motion signals. Therefore, the ANN is employed to forecast the motion signal of the SBMP on the finite history of input. The prepositioning of the SBMP using fuzzy logic and ANN is established for the first time in this research with consideration of the road information and vehicle motion information. The proposed method is performed and validated using MATLAB/Simulink and the outcomes show that using the new prepositioning method will lead to better driving sensation with less wrong motion sensation errors compared with the non and previous prepositioning methods.