Khusro, Y.R.; Zheng, Y.; Grottoli, M.; Shyrokau, B. MPC-Based Motion-Cueing Algorithm for a 6-DOF Driving Simulator with Actuator Constraints. Vehicles2020, 2, 625-647.
Khusro, Y.R.; Zheng, Y.; Grottoli, M.; Shyrokau, B. MPC-Based Motion-Cueing Algorithm for a 6-DOF Driving Simulator with Actuator Constraints. Vehicles 2020, 2, 625-647.
Khusro, Y.R.; Zheng, Y.; Grottoli, M.; Shyrokau, B. MPC-Based Motion-Cueing Algorithm for a 6-DOF Driving Simulator with Actuator Constraints. Vehicles2020, 2, 625-647.
Khusro, Y.R.; Zheng, Y.; Grottoli, M.; Shyrokau, B. MPC-Based Motion-Cueing Algorithm for a 6-DOF Driving Simulator with Actuator Constraints. Vehicles 2020, 2, 625-647.
Abstract
Driving simulators are widely used for understanding human-machine interaction, driver behavior and in driver training. The effectiveness of simulators in these 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 non-linear 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 utilize maximum workspace. Further, adaptive weights-based tuning is used to smoothen 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 utilization.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
