Model-based predictive motion cueing strategy for vehicle driving simulators

Motion-based driving simulators are designed to render accelerations perceived as realistic, while keeping the motion system within its physical limits. These objectives are generally contradictory, and consequently motion control strategies are difficult to customize for different simulator configurations. In this paper, a novel approach is presented for the design of motion rendering strategies, using the model-based predictive control theory. Compared to traditional cueing techniques, actuator constraints are always respected, and the use of motion workspace is maximized during simulations. Models of human motion perception can be integrated to reduce sensory conflicts. A practical implementation on a high-performance automotive driving simulator is presented.