An Optimal Motion Cueing Algorithm Using the Inverse Kinematic Solution of the Hexapod Simulation Platform

The vehicle motion signals is not able to be replicated using the simulation-based motion platform (SBMP) because of the workspace boundaries. The workspace limitations are determined based on the displacement, velocity and acceleration limitations of the actuators. The motion cueing algorithms (MCAs) are introduced to reproduce the motion sensation for the driver of the SBMP same as the real vehicle's driver while keeping the SBMP inside the actuators' limitations. The optimal MCA was developed to decrease the human motion sensation error between the real vehicle's driver and the SBMP's user based on the human vestibular system model using the linear quadratic regulator method. However, the inverse acceleration kinematics model of the SBMPs are not considered in developing optimal MCA to control the displacement, velocity and acceleration of actuators. The lack of inverse acceleration kinematics consideration inside the optimal MCA causes the poor consumption of the SBMP's workspace, as the optimal MCA only considers the boundaries of the SBMP in the Cartesian coordinate system. In this paper, the new optimal MCA based on the inverse acceleration kinematic solution of the SBMP is designed and developed to regenerate the more realistic motion sensation. The validation of the new optimal MCA is performed using Simulink/MATLAB. The outcomes demonstrate that using the new optimal MCA will reach a better motion sensation with less false motion signals in contrast with the existing optimal MCAs.