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 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.

Effects of seat back height and posture on 3D vibration transmission to pelvis, trunk and head

“…Participants were instructed to sit in a car mock-up, mounted on top of a six-degrees-of-freedom motion platform (Khusro, Zheng, Grottoli and Shyrokau, 2020). The mock-up consists of the cockpit of a Toyota Yaris and participants were seated in the modified passenger's seat (see Figure 1).…”

Model Predictive Control Based Washout Algorithm Design for Flight Simulator Upset Prevention and Recovery Training

“…There are two important parts, semicircular canals and otoliths, for motion cueing application. It must be noted that these organs exhibit strongly damped high-pass behavior and cannot sense information at velocities below the threshold [25]. Their dynamic models can be represented as a combination of linear transfer functions and nonlinear motion thresholds.…”

“…Considering both computational time and real-time performance, nonlinearities due to thresholds are generally not included and only linear transfer functions are used to model the human vestibular system. The model parameters related to the linear transfer functions were specified by different researchers to determine which cues are important and should be presented [25].…”

False cue influence on motion cue quality for 10 motion cueing algorithms

“…[10][11][12] These algorithms show substantial improvements in motion sensation fidelity and are able to fill better the available workspace, but still, have some limitations in terms of computational efficiency and avoidance of false cues. Khusro et al 13 proposed a nonlinear MPC solution that uses the Stewart platform's nonlinear kinematics into the MPC algorithm to improve cueing fidelity and maximize workspace. The author also employed the adaptive weights tuning to smooth the platform's movement toward its physical limits.…”

“…The author also employed the adaptive weights tuning to smooth the platform's movement toward its physical limits. 13 Katliar et al 14,15 developed an MPC-based MCA for a Cable-Robot-based motion simulator that includes motion platform actuation. Their key conclusion was that an MPC-based MCA with a complex model can be executed in real time with the right software and numerical approaches.…”