Increasing Motion Fidelity in Driving Simulators Using a Fuzzy-Based Washout Filter

Asadi, Houshyar; Lim, Chee Peng; Mohamed, Shady; Nahavandi, Darius; Nahavandi, Saeid

doi:10.1109/tiv.2019.2904388

Cited by 51 publications

(24 citation statements)

References 34 publications

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“…b) Adaptive Filter: In the adaptive filter algorithm, the parameters that make up the washout algorithm can selftune. The work in [47] presents a fuzzy logic-based motion cuing classical filter that accounts for both physical boundaries [45] of the platform and error between reference motion and actual motion when outputting the filter gains. This work develops an algorithm that not only provides a better use of space but also reduces the human perception of motion error, having a performance similar to the algorithms that use model predictive control, later explained.…”

Section: F Motion Cueing Algorithmmentioning

confidence: 99%

A Review of Driving Simulation Technology and Applications

Bruck

Haycock

Emadi

2021

IEEE Open J. Veh. Technol.

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Driving simulation has become a very useful tool for vehicle design and research in industry and educational institutes. This paper provides a review of driving simulator components, including the vehicle dynamics model, the motion system, and the virtual environment, and how they interact with the human perceptual system in order to create the illusion of the driving. In addition, a sample of current state-of-the-art vehicle simulators and algorithms are described. Finally, current applications are discussed, such as driver-centered studies, chassis and powertrain design, and autonomous systems development.

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Section: F Motion Cueing Algorithmmentioning

confidence: 99%

A Review of Driving Simulation Technology and Applications

Bruck

Haycock

Emadi

2021

IEEE Open J. Veh. Technol.

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“…The second generation of the MCA is called adaptive MCA that varies the fixed parameters of the classical MCA [12]. Recently, different types of adaptive MCAs using the fuzzy logic control [13][14][15] are developed while considering the human vestibular system aiming to deliver the realistic motion feeling [16][17][18]. The linear quadratic regulator technique by considering of human vestibular to decrease the human feeling error is employed in forming of optimal MCA [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

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

Qazani

Asadi

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.

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“…The movement signals of the real vehicle cannot be directly implemented into the SBMP because of the limited workspace area. The approach to reproduce the real vehicle motion signals (including linear acceleration and angular velocity), generating the same real vehicle movement feeling for the SBMP user, is called motion cueing algorithm (MCA) including classical [11,12], adaptive [13][14][15][16][17][18], optimal [19][20][21][22][23] and model predictive control [24][25][26][27][28]. The classical MCAs are very popular in industries and research centres because of its simplicity, easy tunning, low computational load and safety.…”

Section: Introductionmentioning

confidence: 99%

“…Their model suffered from high computational loads since the direct inverse kinematics problem cannot be solved analytically, and the prediction model increases the size of the matrices that should be solved in real-time. The fuzzy logic controller is employed to generate the motion signal taking into account the Cartesian workspace limitations and human motion sensation errors by Asadi et al [13,16]. Considering the workspace limits of SBMP in the Cartesian system instead of the joints' limitations can reduce the efficiency of the method because of the conservative use of the workspace area.…”

Section: Introductionmentioning

confidence: 99%

“…All the previous studies on adaptive MCAs [13][14][15][16][17][18] considered the limitations of the SBMP in the Cartesian system instead of the joints' limitations which lead to the conservative use of the workspace area. Considering the active and passive joints' positions in adaptive MCA leads to the extensive use of the mechanisms workspace and improves the fidelity for the driver of the SBMP.…”

Section: Introductionmentioning

confidence: 99%

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

Qazani

Asadi

Bellmann

et al. 2020

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

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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.

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Increasing Motion Fidelity in Driving Simulators Using a Fuzzy-Based Washout Filter

Cited by 51 publications

References 34 publications

A Review of Driving Simulation Technology and Applications

A Review of Driving Simulation Technology and Applications

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

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

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