Model predictive motion cueing for a helicopter hover task on an 8-dof serial robot simulator

Abstract

Motion cueing for helicopter hover is difficult: small simulators require considerable attenuation, rendering motion cues not useful for stabilization, and large simulators are typically not cost effective. Industrial serial robot-based simulators provide large motion capabilities at a moderate cost, but have two distinct disadvantages. First, they are highly dimensional systems with a non-convex motion space, such that efficient use of the entire space is not trivial. Second, they are typically non-stiff structures with a large mass at the end effector, resulting in oscillatory dynamical properties. We recently developed a novel Model Predictive Motion Cueing Algorithm (MPMCA) that resolves both problems effectively for pre-recorded inertial reference signals. The MPMCA requires an accurate prediction of the future course of the reference inertial signals, which is trivial for pre-recorded maneuvers, but not for real-time human-in-the-loop simulations. In this paper, we present a model-based prediction method, which predicts pilot control inputs and the subsequent helicopter inertial signals during a helicopter hover simulation in real-time. The method is tested in a human-in-the-loop experiment and compared with the Classic Washout Algorithm. The results demonstrate that the MPMCA is a promising new approach to motion cueing.