Chalak Qazani, Mohamad Reza, Asadi, Houshyar, Bellmann, Tobias, Mohamed, Shady, Lim, Chee Peng, and Nahavandi, Saeid (2020) Adaptive Washout Filter Based on Fuzzy Logic for a Motion Simulation Platform With Consideration of Joints’ Limitations. IEEE Transactions on Vehicular Technology, 69 (11). pp. 12547-12558.

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Abstract

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 well-known 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 worst-case 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.

Item ID:	86750
Item Type:	Article (Research - C1)
ISSN:	1939-9359
Copyright Information:	© 2020 IEEE
Date Deposited:	19 Aug 2025 02:28
FoR Codes:	46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461199 Machine learning not elsewhere classified @ 20% 40 ENGINEERING > 4007 Control engineering, mechatronics and robotics > 400705 Control engineering @ 50% 40 ENGINEERING > 4007 Control engineering, mechatronics and robotics > 400706 Field robotics @ 30%
SEO Codes:	28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280110 Expanding knowledge in engineering @ 100%
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