A Model Predictive Control-based Motion Cueing Algorithm using an optimized Nonlinear Scaling for Driving Simulators

Nahavandi

2021

IEEE Systems Journal

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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 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 urban driving scenario which involves sudden decelerations/accelerations (stop and start moving), sharp and large turn, slalom movement. The urban driving scenario destabilises the current MCA based on MPC and leads undesired motion fluctuations which creates unpleasant motion artefact for the SBMP drivers. Therefore, the displacement of the SBMP should be penalised 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 paper, 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 urban areas. Also, the stability of the MPC by considering of the terminal conditions is investigated in MCA domain. Then, the MCA based on MPC by considering of terminal conditions is developed using MATLAB software with presentation of urban motion scenario. The outcomes demonstrate the effectiveness of the designed model with common MCA based on MPC without consideration of the terminal conditions.

Section: Resultsmentioning

confidence: 99%

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

Nahavandi

2021

IEEE Systems Journal

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“…The SimMechanic model increases the capability of the model by producing more realistic results. It should be mentioned that the same scale factor is employed for three MCAs (1/5 and 1/2 for translational and rotational motion signal, respectively) to reach a fair evaluation of them [34,35].…”

Section: Resultsmentioning

confidence: 99%

A New Fuzzy Logic Based Adaptive Motion Cueing Algorithm Using Parallel Simulation-Based Motion Platform

2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

Bellmann

et al. 2020

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Parallel manipulators are recently used in most motion simulation laboratories as they can easily generate six degrees of freedom motion. Recently, fuzzy logic-based adaptive motion cueing algorithms (MCAs) have been employed to reproduce the motion signals. The usage of fuzzy logicbased adaptive MCA reduces the movement sensation error between the real vehicle and the simulation-based motion platform (SBMP) user considering the end-effector limitations in Cartesian space.. In this paper, a new fuzzy logic-based adaptive MCA is introduced to generate motion signals based on the joints' limitations and the movement sensation error between the real vehicle and the SBMP user. Considering the parallel SBMP, joint limits enhance the ability of the introduced adaptive motion cueing algorithm to generate more accurate movement feelings with high fidelity. The simulation results prove that the proposed adaptive motion cueing algorithm can effectively use the large workspace of the parallel SBMP whilst reducing the motion sensation error.

“…It should be noted that the 0.2 scaling factor is employed for the translational motion signal in order to respect the SBMP's limitations. To directly compare simulation results, the same scale factors are used for three investigated methods [31,32].…”

Section: D)mentioning

confidence: 99%

An Optimal Motion Cueing Algorithm Using the Inverse Kinematic Solution of the Hexapod Simulation Platform

IEEE Trans. Intell. Veh.

Nahavandi

2022

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