A Motion Cueing Algorithm Based on Model Predictive Control Using Terminal Conditions in Urban Driving Scenario

Abstract

The motion cueing algorithm (MCA) is in charge of the real vehicle motion feeling regeneration for the driver of the simulation-based motion platform (SBMP) with respect to its limitations. The model predictive control (MPC) has been newly employed in developing MCAs to calculate the optimal input signals for delivering the best motion feeling to the SBMP's drivers while respecting the boundaries of the platform. The stability of the MCA based on MPC has become one of the main issues for some scenarios, such as an urban driving scenario, which involves sudden decelerations/accelerations (stop and start moving), sharp and large turn, and slalom movement. The urban driving scenario destabilizes the current MCA based on MPC and leads the undesired motion fluctuations, which create an unpleasant motion artifact for the SBMP drivers. Therefore, the displacement of the SBMP should be penalized conservatively to respect the workspace boundaries for all driving scenarios. This will make the motion conservative and can cause some motion-feeling error. In this article, the concept of terminal conditions (weights and states) are employed for the first time to design and develop a new generation of MCA based on MPC to enhance the performance of the model for different scenarios, such as the heavy-traffic scenario in the urban areas. Also, the stability of the MPC by considering the terminal conditions is investigated in the MCA domain. Then, the MCA based on MPC by considering terminal conditions is developed using the MATLAB software with a presentation of the urban motion scenario. The outcomes demonstrate the effectiveness of the designed model with a common MCA based on MPC without the consideration of the terminal conditions.