Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 18 November 2020 / Approved: 19 November 2020 / Online: 19 November 2020 (08:02:14 CET)

How to cite: Khusro, Y.R.; Zheng, Y.; Grottoli, M.; Shyrokau, B. MPC-Based Motion Cueing Algorithm for a 6 DOF Driving Simulator with Actuator Constraints. Preprints 2020, 2020110495 (doi: 10.20944/preprints202011.0495.v1). Khusro, Y.R.; Zheng, Y.; Grottoli, M.; Shyrokau, B. MPC-Based Motion Cueing Algorithm for a 6 DOF Driving Simulator with Actuator Constraints. Preprints 2020, 2020110495 (doi: 10.20944/preprints202011.0495.v1).

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.

Subject Areas

driving simulator; motion cueing algorithm; model predictive control; nonlinear actuator constraints

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.