Next Article in Journal
Motorcycle Structural Fatigue Monitoring Using Smart Wheels
Next Article in Special Issue
Musculoskeletal Driver Model for the Steering Feedback Controller
Previous Article in Journal
Automated Multi-Level Dynamic System Topology Design Synthesis
Open AccessArticle

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

1
Rimac Automobili d.o.o., 10431 Sveta Nedelja, Croatia
2
Department of Cognitive Robotics, Delft University of Technology, Mekelweg 2, 2628CD Delft, The Netherlands
3
Siemens Digital Industries Software nv, Interleuvenlaan 68, B-3001 Leuven, Belgium
*
Author to whom correspondence should be addressed.
Received: 18 November 2020 / Revised: 26 November 2020 / Accepted: 27 November 2020 / Published: 2 December 2020
(This article belongs to the Special Issue Dynamics and Control of Automated Vehicles)
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. View Full-Text
Keywords: driving simulator; motion-cueing algorithm; model predictive control; nonlinear; actuator constraints driving simulator; motion-cueing algorithm; model predictive control; nonlinear; actuator constraints
Show Figures

Graphical abstract

MDPI and ACS Style

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. https://0-doi-org.brum.beds.ac.uk/10.3390/vehicles2040036

AMA Style

Khusro YR, Zheng Y, Grottoli M, Shyrokau B. MPC-Based Motion-Cueing Algorithm for a 6-DOF Driving Simulator with Actuator Constraints. Vehicles. 2020; 2(4):625-647. https://0-doi-org.brum.beds.ac.uk/10.3390/vehicles2040036

Chicago/Turabian Style

Khusro, Yash R.; Zheng, Yanggu; Grottoli, Marco; Shyrokau, Barys. 2020. "MPC-Based Motion-Cueing Algorithm for a 6-DOF Driving Simulator with Actuator Constraints" Vehicles 2, no. 4: 625-647. https://0-doi-org.brum.beds.ac.uk/10.3390/vehicles2040036

Find Other Styles

Article Access Map by Country/Region

1
Back to TopTop