Modelling and simulation of a motion cueing algorithm using prediction and computational intelligence techniques

The main research question of this PhD research is how to regenerate the realistic driving/flying motion sensation for SBMP user through developing a novel MCA by considering the nonlinearities such as the sensation shape following factor, dexterity, active and passive joints’ physical and dynamical limitations as well as inverse kinematics and dynamics model of the SBMPs using nonlinear model predictive control (MPC) based on prediction and computational intelligence techniques, such as evolutionary algorithms and neural network (NN)-based techniques, to predict the vehicle motion signals. It should be noted that the above-mentioned factors are ignored in the existing studies on MCAs, and this ignorance causes false motion cues and inefficient platform workspace usage.