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

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 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.

Comparison of gait phase detection using traditional machine learning and deep learning techniques

“…For future work and directions, we suggest to implementing and asses these methods on simalution platforms [44] and robotics [45], [46]. Furthermore, they can be paired with haptic feedback [47] and emotion recognition methodologies [48], body position [49],…”

A novel structure of vehicle motion cueing algorithm for a pneumatic motion simulation platform

“…To reproduce real vehicle motion perceptions, motion cueing algorithm (MCA) is usually used to process the translational accelerations and angular velocities of vehicles for obtaining the translational and angular displacements of MSPs [16]. After the early research about the classical MCA [17], many kinds of MCA are proposed subsequently, such as robust MCA [18], model predictive control based MCA [19], adaptive MCA [20] and optimal MCA [21] - [23]. These motion cueing algorithms (MCAs) have a similar structure as shown in Fig.…”

“…In the early study of the MCA [17], low pass filter is used to get low frequency information for the MSPs. Moreover, the low pass filter is applied in many subsequent studies of the MCA [20] [26]. But the gains of the low pass filter is usually gotten by trial-and-error method.…”

A Prediction of Time Series Driving Motion Scenarios Using LSTM and ESN

“…As a result, the motion cueing algorithm (MCA) [21,22] is devised to re-generate the motion sensations for the simulator user to experience the same sensation as if in a vehicle, within the limited working area. MCA is divided to classical [23], adaptive [24][25][26][27][28][29], optimal [30][31][32][33] and model predictive [34][35][36][37][38][39][40][41] methods. In this regard, false motion cues lead to motion sickness in simulator users, which is the main disadvantage of the MCA.…”