MPC-Based Motion-Cueing Algorithm for a 6-DOF Driving Simulator with Actuator Constraints

Abstract

Driving simulators are widely used for understanding human–machine interaction, driver behavior and in driver training. The effectiveness of simulators in this process depends largely on their ability to generate realistic motion cues. Though the conventional filter-based motion-cueing strategies have provided reasonable results, these methods suffer from poor workspace management. To address this issue, linear MPC-based strategies have been applied in the past. However, since the kinematics of the motion platform itself is nonlinear and the required motion varies with the driving conditions, this approach tends to produce sub-optimal results. This paper presents a nonlinear MPC-based algorithm which incorporates the nonlinear kinematics of the Stewart platform within the MPC algorithm in order to increase the cueing fidelity and use maximum workspace. Furthermore, adaptive weights-based tuning is used to smooth the movement of the platform towards its physical limits. Full-track simulations were carried out and performance indicators were defined to objectively compare the response of the proposed algorithm with classical washout filter and linear MPC-based algorithms. The results indicate a better reference tracking with lower root mean square error and higher shape correlation for the proposed algorithm. Lastly, the effect of the adaptive weights-based tuning was also observed in the form of smoother actuator movements and better workspace use.

Optimal Trajectory Planning for Mitigated Motion Sickness: Simulator Study Assessment

“…Apparatus 1) Driving Simulator: Delft Advanced Vehicle Simulator (DAVSi, Figure 3) is used for assessing the effectiveness of optimal trajectories obtained from the proposed algorithm, in the mitigation of motion sickness via human-in-the-loop experiments. DAVSi is a 6-DoF motion platform driving simulator [30], capable of generating acceleration up to 1 g in all directions and can simulate motions in the wide frequency range up to 10 Hz. The half-car Toyota Yaris mock-up with a controllable interface via CAN (levers, buttons, air-con, etc.)…”

Curve Tilting With Nonlinear Model Predictive Control for Enhancing Motion Comfort

“…The communication operates at 20 Hz, identical to the controller's sampling frequency, while the vehicle dynamics are updated at 1 kHz. Also included in the HIL setup is the Delft Advanced Driving Simulator (DAVSi) [31], which mainly consists of a mock-up of the front half of a Toyota Yaris and a hexapod motion platform driven by six linear motors. The experiment runs in hard real-time mode, where if the turnaround time of the controller exceeds the sampling time, the simulation is terminated immediately.…”

The Impact of Physical Motion Cues on Driver Braking Performance: A Clinical Study Using Driving Simulator and Eye Tracker

“…To enhance simulator fidelity, efforts have been made in this research field to provide a more realistic sense of presence [ 14 ]. Thus, advanced artificial intelligence (AI) techniques, including deep neural network (DNN) [ 15 ], fuzzy logic [ 16 , 17 , 18 ], or genetic algorithm [ 19 ], have been exploited to optimize platform motion cueing in a high degrees-of-freedom (DOF) in the roll, pitch, and yaw axis. However, other studies underlined the high-cost issue of such developed software and hardware of motion platforms and intended to reduce simulators’ cost by decreasing the freedom to 3-DOF [ 20 ], 2-DOF [ 21 ], or even to completely static simulators [ 22 , 23 , 24 , 25 , 26 ].…”