Adaptive motion washout filter design by using self-tuning fuzzy control

Hwang, Thong-Shing; Yeh, Shen-Kai; Lin, Jr-Ruei; Su, Wen-Pin

doi:10.1109/aim.2009.5229912

Cited by 8 publications

(5 citation statements)

References 6 publications

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“…As the classical WF cannot consider the physical limitations of the MSP in Cartesian space, it should be tuned based on the worst-case scenario to respect the workspace boundaries of the MSP. Also, the existing adaptive WFs [24,26] only consider the physical boundaries of the MSP in Cartesian space, while the dynamical and physical boundaries of the active and passive joints are the main reason for the workspace limitations of the MSP. The consideration of the MSP in Cartesian space still can lead to poor usage of the available workspace, and it can lead to wrong motion cues.…”

Section: Discussionmentioning

confidence: 99%

“…To validate the proposed adaptive fuzzy logic-based WF, the classical WF introduced by Conrad and Schmidt [13] and adaptive WF developed by Hwang et al [24] and improved by Asadi et al [26] are designed and implemented using MATLAB software. Also, the inverse kinematics model of the Hexapod MSP is modelled to find the active joints' accelerations, velocities and positions as well as the angular positions of the passive joints.…”

Section: A Simmechanic Modellingmentioning

confidence: 99%

“…Fuzzy logic is one of the best techniques for systems with stochastic behaviour and changeable inputs because it is able to transform deterministic plans to stochastic plans [22,23]. Hwang et al [24] employed fuzzy logic to change the fixed parameters of WFs for the generation of accurate motion signals. Asadi et al [25,26] employed the fuzzy logic controller to fluctuate the cut-off frequencies of the classical WF based on the motion sensation error between the real vehicle driver and the MSP driver, and the translational and rotational displacement of the MSP.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Washout Filter Based on Fuzzy Logic for a Motion Simulation Platform With Consideration of Joints’ Limitations

Qazani

Asadi

Bellmann

et al. 2020

IEEE Trans. Veh. Technol.

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Motion simulation platforms (MSPs) are widely used to generate driving/flying motion sensations for the users. The MSPs have a restricted workspace area due to the dynamical and physical restrictions of the Motion Platforms active joints as well as the physical limitations of its passive joints. The motion cueing algorithm (MCA) is the reproduction of the motion signal including linear accelerations and angular velocities. It aims to simultaneously respect the MSP's workspace limitations and make the same motion feeling for the user as a real vehicle. The Classical washout filter (WF) is a wellknown type of MCA. The classical WF is easy to set-up, offers a low computational burden and high functionality but has some major drawbacks such as fixed WF parameters tuned according to worstcase scenarios and no consideration of the human vestibular system. As a result, adaptive WFs were developed to consider the human vestibular system and enhance the efficiency of the method using time-varying filters. The existing adaptive WFs only cogitate the boundaries of the end-effector in the Cartesian coordinate space as a substitute for the active and passive joints limitations, which is MSP's main limiting factor. This conservative assumption reduces the available workspace area of the MSP and increases the motion sensation error for the MSPs user. In this study, a fuzzy logic-based WF is developed, to consider the dynamical and physical boundaries of the active joints as well as the physical boundaries of the passive joints. A genetic algorithm is used to select the membership functions' values of the active and passive joints' boundaries. The model is designed using MATLAB /Simulink and the outcomes demonstrate the efficiency of the proposed method versus existing adaptive WFs.

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Section: Discussionmentioning

confidence: 99%

Section: A Simmechanic Modellingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adaptive Washout Filter Based on Fuzzy Logic for a Motion Simulation Platform With Consideration of Joints’ Limitations

Qazani

Asadi

Bellmann

et al. 2020

IEEE Trans. Veh. Technol.

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“…The tuning of the MCA is complicated due to the intransparent parameters of the MCA. Most tuning techniques were introduced for the classical algorithm to solve the intransparent problem [5,6]. Moreover, the numerous parameters of the MCAs and fitting characteristics of human motion perception are also complex tuning tasks.…”

Section: Introduction *mentioning

confidence: 99%

Auto-Tuning Parameters of the Offline Optimal Motion Cueing Algorithm with Mean-Variance Mapping Optimization

2022

JST: SSAD

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A motion cueing algorithm (MCA) not only maintains the simulator within its physical limits but also generates such movements of the driving simulator that the necessary motion cues of drivers on the realistic vehicle are equivalently reproduced. The offline optimal MCA focuses on finding the best combination of the translational acceleration and tilt angles of the motion platform to maintain drivers’ motion perception. However, the best combination depends on the MCA’s parameters, tuned mainly by trial and error with experts in the loop. Moreover, for different amplitude input signals, the parameters are accordingly modified. This manual tuning procedure is so time-consuming that the generic optimization, named Mean-Variance mapping optimization, was proposed to search the suitable parameters for the optimal algorithm. This tuning method uses the specific cost function of constraint conditions such as workspace limits, avoiding false cues, and improving motion fidelity to achieve the best parameters for optimal MCA with the particular input signals.

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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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Adaptive motion washout filter design by using self-tuning fuzzy control

Cited by 8 publications

References 6 publications

Adaptive Washout Filter Based on Fuzzy Logic for a Motion Simulation Platform With Consideration of Joints’ Limitations

Adaptive Washout Filter Based on Fuzzy Logic for a Motion Simulation Platform With Consideration of Joints’ Limitations

Auto-Tuning Parameters of the Offline Optimal Motion Cueing Algorithm with Mean-Variance Mapping Optimization

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

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