Constrained Motion Cueing for Driving Simulators Using a Real-Time Nonlinear MPC Scheme

Abstract

This contribution presents a motion cueing algorithm (MCA) for driving simulators using nonlinear model predictive control (MPC). The goal of the MCA is to generate a realistic motion feeling while keeping the simulator within its workspace limits. The approach relies on a realtime gradient algorithm in combination with the augmented Lagrangian method in order to directly incorporate the system constraints into the optimization. Simulation results for a reference trajectory with typical driving situations demonstrate the performance as well as the computational efficiency of the approach.