Sensation Error Research Articles

Investigation of vibration's effect on driver in optimal motion cueing algorithm.

The increased sensation error between the surroundings and the driver is a major problem in driving simulators, resulting in unrealistic motion cues. Intelligent control schemes have to be developed to provide realistic motion cues to the driver. The driver's body model incorporates the effects of vibrations on the driver's health, comfort, perception, and motion sickness, and most of the current research on motion cueing has not considered these factors. This article proposes a novel optimal motion cueing algorithm that utilizes the driver's body model in conjunction with the driver's perception model to minimize the sensation error. Moreover, this article employs H∞ control in place of the linear quadratic regulator to optimize the quadratic cost function of sensation error. As compared to state of the art, we achieve decreased sensation error in terms of small root-mean-square difference (70%, 61%, and 84% decrease in case of longitudinal acceleration, lateral acceleration, and yaw velocity, respectively) and improved coefficient of cross-correlation (3% and 1% increase in case of longitudinal and lateral acceleration, respectively).

A Novel Decoupled Model Predictive Control-Based Motion Cueing Algorithm for Driving Simulators

The motion cueing algorithm (MCA) is used to reproduce the driving/flying motion sensation of the real land or air vehicles for the users of motion simulator. Highly accurate MCAs are required for the motion simulators to create realistic sensation for the simulator users, otherwise, they might cause motion sickness and user discomfort. Model predictive control (MPC) is a popular technique in development of the high-fidelity MCAs as it is able to consider the motion sensation as well as the workspace constraints of the simulator platform. Tilt coordination in MCA is in charge of creating sustained linear acceleration feeling through tilting the platform cabin and exploiting gravitational acceleration under human motion threshold, to not allow the user to perceive this as a rotational motion. However, the existing MPC-based MCAs have not considered the rate limit to constrain the tilt motion under human threshold which causes violations of the rotational sensation threshold during generating sustained acceleration feeling. Instead, some researchers increase the penalty weights of rotational motions in order to slow down rotational motions. Using this strategy slows down all the simulator rotational motions not only the ones coming from tilt coordination, to generate sustained acceleration feeling, but also from rotational channel (due to real vehicle rotation) which causes motion sensation error and consequently motion sickness. Therefore, as the existing MPC-based MCAs cannot differentiate the rotations of tilt coordination channel (due to sustained acceleration feeling generation), from rotational channel (due to vehicle rotational motions), they are not able to produce accurate rotational motion sensations because of insufficient rotational motions. In this research, a novel decoupled MPC-based MCA is developed to systematically address the issues related to the existing MPC-based MCAs, in terms of inaccurate motion generation, by redesigning the MPC-based MCA using a series of vestibular system-based MPC models and considering tilt rate limit in tilt coordination. The simulation study using MATLAB software is used to verify and validate the proposed MCA compared to the existing MCAs. The proposed decoupled MPC-based MCA is able to generate accurate motion cues compared to those of the existing MCAs.

Adaptive Motion Cueing Algorithm Using Optimized Fuzzy Control System for Motion Simulators

The Motion Cueing Algorithm (MCA) is the main unit of motion simulators responsible for transforming the vehicle motions to generate driving motion sensation for the simulator users within the motion simulator's physical workspace through washout filters. In this study, we design and provide a new framework by developing a set of novel washout filters using optimized fuzzy control systems to solve the drawbacks associated with the existing optimal MCAs of the motion simulators. These drawbacks include constant washout filter parameters, inefficient simulator movement in the workspace, lack of considering the simulator's physical constraints and driver sensation-related factors, and sub-optimal settings which causes false motion cues and subsequently simulator sickness. As a pioneering framework, the fuzzy logic controllers generate restitution signals for the optimal washout filters based on the motion sensation error between the real vehicle and simulator divers as well as the platform's position in the workspace aiming to correct the filtered signals, reduce error, generate realistic motions, and increase motion fidelity online. The GA is applied to optimize the fuzzy logic membership functions and control rules. The optimization procedure takes into account a number of important factors that are normally ignored in the current MCAs including the sensed motion error between the drivers of simulator and real car; the platform physical constraints; the motion threshold limiter effects; and signal shape following. The new adaptive MCA is designed, implemented and tested using the MATLAB/Simulink. The simulation results confirm the effectiveness of the developed MCA in maximizing motion fidelity and the reference sensation shape following, and enhancing the workspace usage of the simulator.

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

The vehicle motion signals is not able to be replicated using the simulation-based motion platform (SBMP) because of the workspace boundaries. The workspace limitations are determined based on the displacement, velocity and acceleration limitations of the actuators. The motion cueing algorithms (MCAs) are introduced to reproduce the motion sensation for the driver of the SBMP same as the real vehicle's driver while keeping the SBMP inside the actuators' limitations. The optimal MCA was developed to decrease the human motion sensation error between the real vehicle's driver and the SBMP's user based on the human vestibular system model using the linear quadratic regulator method. However, the inverse acceleration kinematics model of the SBMPs are not considered in developing optimal MCA to control the displacement, velocity and acceleration of actuators. The lack of inverse acceleration kinematics consideration inside the optimal MCA causes the poor consumption of the SBMP's workspace, as the optimal MCA only considers the boundaries of the SBMP in the Cartesian coordinate system. In this paper, the new optimal MCA based on the inverse acceleration kinematic solution of the SBMP is designed and developed to regenerate the more realistic motion sensation. The validation of the new optimal MCA is performed using Simulink/MATLAB. The outcomes demonstrate that using the new optimal MCA will reach a better motion sensation with less false motion signals in contrast with the existing optimal MCAs.

Adaptive Motion Cueing Algorithm Based on Fuzzy Logic Using Online Dexterity and Direction Monitoring

Motion cueing algorithm (MCA) is used to reproduce the realistic driving motion feeling for the user of the virtual vehicle using linear acceleration and angular velocity motion signals while respecting the physical constrains of the simulation-based motion platform. Classical washout filter is the most common and commercial type of the MCA because of easy tuning, short processing time, and simplicity. The classical washout filter is a conservative method in using workspace because of the worst-case scenario tuning technique. Also, the existing adaptive washout filter based on time-varying cut-off frequency does not consider the dexterity of the motion platform as considering this factor leads to use the high acceleration areas of the platform workspace. In this article, an adaptive washout filter is designed and developed based on time-varying cut-off frequency using dexterity and direction monitoring factors to use the available high acceleration areas of the platform for better generation of the high-frequency part of the motion signal. The proposed adaptive washout filter is composed of three fuzzy logic units for each mode including longitudinal, lateral, yaw, and heave and each unit has two inputs including motion sensation error between the real vehicle driver and user of the motion platform and the weight of the end-effector which is a parameter based on dexterity and current position of the end-effector. Each fuzzy logic unit has also one output which is the selected cut-off frequency of the related washout filter. The proposed adaptive washout filter uses the high dexterous areas of the workspace because it considers the dexterity of the motion platform in calculation of the end-effector weight as the second input of the fuzzy logic units. The proposed adaptive washout filter is validated in MATLAB/Simulink programming language and the experimental results indicate that the proposed adaptive washout filter performed better than existing adaptive and classical washout filters in terms of motion sensation error between the driver of real vehicle and the user of motion platform as well as usage of the platform workspace.

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

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.

Prepositioning of a Land Vehicle Simulation-Based Motion Platform Using Fuzzy Logic and Neural Network

The motion cueing algorithms (MCAs) is the method that reproduces the motion sensation of the real vehicle for the users of simulation-based motion platforms (SBMPs). Classical MCA is the most common type of MCA due to its simplicity, easy tunning procedure, and higher speed. The fixed neutral position of the SBMP restricts the available workspace and makes the motions of the platform conservative. Then, the high frequency motion inputs cannot be reproduced precisely using SBMP and it increases the motion sensation error between the SBMP and real vehicle driver. The main objective of this study is to enlarge the reachable workspace of the SBMP adaptively using the new prepositioning technique based on fuzzy logic and artificial neural network (ANN). Prepositioning of the SBMPs to an off-centre point can increase the available linear space of the SBMPs based on different driving scenarios to decrease the motion sensation error. The fuzzy logic-based units are working based on the current position of the SBMP and the predicted motion signals. Therefore, the ANN is employed to forecast the motion signal of the SBMP on the finite history of input. The prepositioning of the SBMP using fuzzy logic and ANN is established for the first time in this research with consideration of the road information and vehicle motion information. The proposed method is performed and validated using MATLAB/Simulink and the outcomes show that using the new prepositioning method will lead to better driving sensation with less wrong motion sensation errors compared with the non and previous prepositioning methods.

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

The motion cueing algorithm is the procedure used to regenerate vehicle motion cues by transforming translational and rotational motions of a simulated vehicle into the simulator motion such that high fidelity motions can be generated through a washout filter. Classical washout filters are widely being used in different motion simulators because of their low computational load, simplicity, and functionality. However, they have a number of disadvantages that make them unreliable in some cases. One of the main disadvantages is its parameter selecting procedure, which is based on trial-and-error and the worst case driving scenario. In addition, the washout filter parameters are kept constant during different driving scenarios, causing inflexibility of the filter design. This causes conservative platform workspace usage and as a result false motion cues. Furthermore, the mathematical model of human motion sensation plays no role in designing of the classical washout filter. Ignorance of the online information about physical limitations of the platform and the simulator driver motion sensation factors are other main drawbacks of classical washout filters. The aim of this research is to provide a fuzzy logic-based washout filter that considers the motion sensation error between the real vehicle and simulator drivers as well as the distance of the simulator platform from its physical boundaries to correct false motion cues and decrease human sensation errors. The proposed fuzzy-based washout filter also seeks to increase motion fidelity and enable the simulator to use the platform workspace more efficiently. The simulation results show the efficiency of the proposed fuzzy-based washout filter in reducing the human sensation error and enhancing efficiency of the simulator platform workspace usage.

基于相关法飞行时间三维感知的误差机理研究

基于相关法飞行时间三维感知的误差机理研究

A genetic algorithm–based nonlinear scaling method for optimal motion cueing algorithm in driving simulator

A motion cueing algorithm plays an important role in generating motion cues in driving simulators. The motion cueing algorithm is used to transform the linear acceleration and angular velocity of a vehicle into the translational and rotational motions of a simulator within its physical limitation through washout filters. Indeed, scaling and limiting should be used along within the washout filter to decrease the amplitude of the translational and rotational motion signals uniformly across all frequencies through the motion cueing algorithm. This is to decrease the effects of the workspace limitations in the simulator motion reproduction and improve the realism of movement sensation. A nonlinear scaling method based on the genetic algorithm for the motion cueing algorithm is developed in this study. The aim is to accurately produce motions with a high degree of fidelity and use the platform more efficiently without violating its physical limitations. To successfully achieve this aim, a third-order polynomial scaling method based on the genetic algorithm is formulated, tuned, and implemented for the linear quadratic regulator–based optimal motion cueing algorithm. A number of factors, which include the sensation error between the real and simulator drivers, the simulator’s physical limitations, and the sensation signal shape-following criteria, are considered in optimizing the proposed nonlinear scaling method. The results show that the proposed method not only is able to overcome problems pertaining to selecting nonlinear scaling parameters based on trial-and-error and inefficient usage of the platform workspace, but also to reduce the sensation error between the simulator and real drivers, while satisfying the constraints imposed by the platform boundaries.

Optimizing Model Predictive Control horizons using Genetic Algorithm for Motion Cueing Algorithm

Driving simulators are effective tools for producing the feeling of driving a real car through generation of a similar environment and motion cues. The main problem of motion simulators is their limited workspace which does not allow them to produce the exact motions of a real vehicle, hence they need a Motion Cueing Algorithm (MCA). A high-fidelity motion simulator can be used for vehicle prototyping and testing as well as driver/pilot training to enhance transportation safety. Using motion simulators with the capability of replacing realistic motions for these purposes is less risky for drivers and more time and cost-effective. Due to workspace limitations, washout filters have been designed to bring motion simulators back to a neutral position; however, the problem of violation of platform constraints is still an issue. Recently Model Predictive Control (MPC) has become popular in driving simulators. The primary advantage of this control method is respecting constraints and consideration of future dynamics. The horizon windows of future control and prediction affect the computational burden and the output performance. As these horizons are chosen manually by the designer, they are sub-optimal and in some cases too wide or narrow. In this paper, a novel method based on Genetic Algorithm (GA) is employed to achieve the best control and prediction horizons considering minimization of several terms such as sensation error, displacement and the computational burden. This new method is proposed to eliminate the MPC-MCA drawbacks such as time-consuming empirical guessing by iterative trial-and-error for the initial control and prediction horizons as selecting the initial control and prediction horizons based on trial-and-error can lead to large sensation error, low motion fidelity, inefficient platform usage as well as the computational burden. Therefore, this method provides a new framework for tuning not only the MPC-MCA optimally but also all the MPC-based applications while minimizing the desired cost function and computational load. The simulation results show the effectiveness of the proposed method in terms of output performance improvement and the computational burden.

Robust Optimal Motion Cueing Algorithm Based on the Linear Quadratic Regulator Method and a Genetic Algorithm

The aim of this paper is to design and develop an optimal motion cueing algorithm (MCA) based on the genetic algorithm (GA) that can generate high-fidelity motions within the motion simulator’s physical limitations. Both, angular velocity and linear acceleration are adopted as the inputs to the MCA for producing the higher order optimal washout filter. The linear quadratic regulator (LQR) method is used to constrain the human perception error between the real and simulated driving tasks. To develop the optimal MCA, the latest mathematical models of the vestibular system and simulator motion are taken into account. A reference frame with the center of rotation at the driver’s head to eliminate false motion cues caused by rotation of the simulator to the translational motion of the driver’s head as well as to reduce the workspace displacement is employed. To improve the developed LQR-based optimal MCA, a new strategy based on optimal control theory and the GA is devised. The objective is to reproduce a signal that can follow closely the reference signal and avoid false motion cues by adjusting the parameters from the obtained LQR-based optimal washout filter. This is achieved by taking a series of factors into account, which include the vestibular sensation error between the real and simulated cases, the main dynamic limitations, the human threshold limiter in tilt coordination, the cross correlation coefficient, and the human sensation error fluctuation. It is worth pointing out that other related investigations in the literature normally do not consider the effects of these factors. The proposed optimized MCA based on the GA is implemented using the MATLAB/Simulink software. The results show the effectiveness of the proposed GA-based method in enhancing human sensation, maximizing the reference shape tracking, and reducing the workspace usage.

Incorporating Human Perception With the Motion Washout Filter Using Fuzzy Logic Control

Nowadays, classical washout filters are extensively used in commercial motion simulators. Even though there are several advantages for classical washout filters, such as short processing time, simplicity and ease of adjustment, they have several shortcomings. The main disadvantage is the fixed scheme and parameters of the classical washout filter cause inflexibility of the structure and thus the resulting simulator fails to suit all circumstances. Moreover, it is a conservative approach and the platform cannot be fully exploited. The aim of this research is to present a fuzzy logic approach and take the human perception error into account in the classical motion cueing algorithm, in order to improve both the physical limits of restitution and realistic human sensations. The fuzzy compensator signal is applied to adjust the filtered signals on the longitudinal and rotational channels online, as well as the tilt coordination to minimize the vestibular sensation error below the human perception threshold. The results indicate that the proposed fuzzy logic controllers significantly minimize the drawbacks of having fixed parameters and conservativeness in the classical washout filter. In addition, the performance of motion cueing algorithm and human perception for most occasions is improved.

Optimisation of nonlinear motion cueing algorithm based on genetic algorithm

Motion cueing algorithms (MCAs) are playing a significant role in driving simulators, aiming to deliver the most accurate human sensation to the simulator drivers compared with a real vehicle driver, without exceeding the physical limitations of the simulator. This paper provides the optimisation design of an MCA for a vehicle simulator, in order to find the most suitable washout algorithm parameters, while respecting all motion platform physical limitations, and minimising human perception error between real and simulator driver. One of the main limitations of the classical washout filters is that it is attuned by the worst-case scenario tuning method. This is based on trial and error, and is effected by driving and programmers experience, making this the most significant obstacle to full motion platform utilisation. This leads to inflexibility of the structure, production of false cues and makes the resulting simulator fail to suit all circumstances. In addition, the classical method does not take minimisation of human perception error and physical constraints into account. Production of motion cues and the impact of different parameters of classical washout filters on motion cues remain inaccessible for designers for this reason. The aim of this paper is to provide an optimisation method for tuning the MCA parameters, based on nonlinear filtering and genetic algorithms. This is done by taking vestibular sensation error into account between real and simulated cases, as well as main dynamic limitations, tilt coordination and correlation coefficient. Three additional compensatory linear blocks are integrated into the MCA, to be tuned in order to modify the performance of the filters successfully. The proposed optimised MCA is implemented in MATLAB/Simulink software packages. The results generated using the proposed method show increased performance in terms of human sensation, reference shape tracking and exploiting the platform more efficiently without reaching the motion limitations.

Influence of a new discrete‐time LQR‐based motion cueing on driving simulator

SUMMARYThis study proposes a method and an experimental validation to analyze dynamics response of the simulator's cabin and platform with respect to the type of the control used in the hexapod driving simulator. In this article, two different forms of motion platform tracking control are performed as a classical motion cueing algorithm and a discrete‐time linear quadratic regulator (LQR) motion cueing algorithm. For each situation, vehicle dynamics and motion platform level data are registered from the driving simulation software. In addition, the natural frequencies of the roll accelerations are obtained in real‐time by using FFT. The data are denoised by using wavelet 1D transformation. The results show that by using discrete‐time LQR algorithm, the roll acceleration amplitudes that correspond to the natural frequencies and the total roll jerk have decreased at the motion platform level. Also, the natural frequencies have increased reasonably by using the discrete LQR motion cueing (1.5–2.2 Hz) compared with using the classical algorithm (0.4–1.5 Hz) at the motion platform, which is an indicator of motion sickness incidence avoidance. The literature shows that lateral motion (roll, yaw, etc.) in the frequency range of 0.1–0.5 Hz induces motion sickness. Furthermore, using discrete‐time LQR motion cueing algorithm has decreased the sensation error (motion platform–vehicle (cabin) levels) two times in terms of total roll jerk. In conclusion, discrete‐time LQR motion cueing has reduced the simulator sickness more than the classical motion cueing algorithm depending on sensory cue conflict theory. Copyright © 2013 John Wiley & Sons, Ltd.

Towards Washout Filter Concepts for Motion Simulators on the Base of a Stewart Platform

AbstractIn this paper we present a pneumatically driven Stewart platform as a basis for motion simulators. Motion platforms, that simulate perceived situations in aeroplanes, cars or ships, have workspace constraints in every degree of freedom. Therefore it is necessary to adapt the accelerations and angular rates in order to stay within the physical restrictions.For realizing a flight or ride simulator on the basis of the Stewart platform, “ Washout Filters” are used to change the signals of the open source software FlightGearand to minimize the sensation error between simulator and aircraft. Different filter concepts are implemented and evaluated.The platform presented in this document is a parallel kinematic robot, driven by fluidic muscles. The pneumatically actuated muscles are only able to produce tensile forces. Therefore a spiral spring from a passenger car is used to apply the compressive forces and torques. (© 2011 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)

An Optimal Washout Filter Design with Fuzzy Compensation for a Motion Platform

AbstractThe realistic motion of the vehicular simulator is produced by the motion cueing algorithm. An optimal washout filter, taking into account the limitation of the simulator's workspace, is applied here to minimize human's perception error in order to provide realistic behavior. The filtering algorithm compares the human's perception of driving simulator with that driving actual vehicle obtained based on the human vestibular model. The cost function accounting for both the pilot's sensation error and the range of platform motion is minimized to achieve more realistic motion and efficient usage of the limited workspace. To compensate the drawback of fixed parameters in optimal washout filter, the fuzzy control rules of motion perception are applied to online adjust the filtered signals. The simulation results verify the efficient utilization of platform workspace and less sensation error compared with that obtained by the classical washout filter.

Senseless Maneuver Optimal Washout Filter Design with Human Vestibular Based (HVB) for VR-based Motion Simulator

In this paper, we propose a new approach “HVB senseless maneuver optimal washout filter” which is based on human vestibular system, senseless maneuver and motion platform limitation for designing washout filter such that a cost function constraining the pilot sensation error (between simulator and vehicle) is minimized. This approach can curtail over strong feelings of pilot reception and increase efficiency of platform workspace for task running. Finally, the experimental results confirm the effectiveness of our algorithm hereby proposed. Moreover, the results show that a better performance can be attained.

Fast design of the QP-based optimal trajectory for a motion simulator

The main difficulty in realizing a motion simulator comes from the constraints on its workspace. The so-called washout filter prevents a simulator from being driven to go off its pre-determined boundaries and generate excessive torques. By noting that the existing washout filters are conservative and more aggressive motions may be accommodated, this paper presents a novel approach that fully exploits the simulator workspace and thereby reproduces the real-world sensations with high fidelity. The washout filter converts the real-world input trajectory as a realizable one that satisfies the spatial and dynamic constraints while minimizing the sensation error and fidelity between the motions experienced in the real world and on the motion simulator. The control objective is to reduce the computational burdens by using the QP algorithm. The proposed approach formulates the task of designing a washout filter as a quadratic programming (QP). The direct approach to the solution of the QP often results in a computational burden that amounts toO(N3) flops andO(N2) storage space (N=104 ∼ 105, typically). By judiciously exploiting the Toeplitz structures of the underlying matrices, an orders-of-magnitude faster algorithm is obtained to reduce the computational burdens toO(Nlog2N) flops andO(N) storage space. The extensive simulation studies on the Eclipse-II motion simulator at Seoul National University assure that the QP-based fast algorithm outperforms the existing ones in reproducing the real-world sensations.

Human Vestibular System Based Optimal Washout Filter Design on Motion Platform Constraint

In this paper, we propose a new approach which is based on human vestibular system and motion platform limitation for designing washout filter such that a cost function constraining the pilot sensation error (between simulator and vehicle) is minimized. Finally, the experimental results confirm the effectiveness of our algorithm hereby proposed. Moreover, the results show that a better performance can be attained