Interval Type 2 Fuzzy Adaptive Motion Drive Algorithm Design

Motion drive algorithms are a set of filters designed to simulate realistic motion and are an integral part of contemporary vehicle simulators. This paper presents the design of a novel intelligent interval type 2 fuzzy adaptive motion drive algorithm for an off-road uphill vehicle simulator. The off-road, uphill vehicle simulator is used to train and assess the driver’s behavior under varying operational and environmental conditions in mountainous terrain. The proposed algorithm is the first of its kind to be proposed for off-road uphill vehicle simulators, and it offers numerous benefits over other motion drive algorithms. The proposed algorithm enables the simulator to adapt to changes in the uphill road surface, vehicle weight distribution, and other factors that influence off-road driving in mountainous terrain. The proposed algorithm simulates driving on hilly terrain more realistically than existing algorithms, allowing drivers to learn and practice in a safe and controlled environment. Additionally, the proposed algorithm overcomes limitations present in existing algorithms. The performance of the proposed algorithm is evaluated via test drives and compared to the performance of the conventional motion drive algorithm. The results demonstrate that the proposed algorithm is more effective than the conventional motion drive algorithm for the ground vehicle simulator. The pitch and roll responses demonstrate that the proposed algorithm has enabled the driver to experience abrupt changes in terrain while maintaining the driver’s safety. The surge response demonstrated that the proposed MDA handled the acceleration and deceleration of the vehicle very effectively. In addition, the results demonstrated that the proposed algorithm resulted in a smoother drive, prevented false motion cues, and offered a more immersive and realistic driving experience.publishedVersio

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 H1 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)

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

Central Nervous System (CNS) Based Motion Control

Motion simulators are widely used in several applications ranging from research to commercial training and entertainment in order to replicate real movement situation. These motions can be sensed by human perception organ called Central Nervous System (CNS). This research presents a novel control algorithm called Central Nervous System (CNS) based control that aims to create realistic perception of vehicle simulation. CNS-based motion control was evaluated by computer simulation to classical, adaptive and optimal washout filter. In addition, comparisons of human motion perception are performed on Force Dynamics 301 simulator for longitudinal acceleration driving test of all four washout filters. The subjects were seated in the simulator. Their motion perceptions were measured through vestibulo-ocular reflex (VOR) using EyeSeeCam vHit camera and compared to the estimated VOR from CNS model. The results revealed that CNS-based motion control can crucially reduce the workspace and provide realistic motion sensation.   &nbsp

Eine Studie über State-of-the-Art Motion-Cueing-Algorithmen angewendet auf Planar Motion mit Pure Lateral Acceleration - Vergleich, Auto-Tuning und subjektive Bewertung auf einem KUKA Robocoaster Serial Ride Simulator

Driving simulators are widely used during the training of drivers/pilots, and for entertainment, as well as in the research of human behavior. Motion cueing algorithms (MCAs) are aimed at mapping the motion of moving vehicles into the limited workspace of driving simulators, while preserving the perceptual realism of the simulation. Several MCAs have been developed in the literature to improve the realism of the simulation. However, each MCA presents strengths and weaknesses when compared to the others. Most importantly, the tuning of the MCA parameters is an open issue because these parameters are not intuitive for normal simulator users. The current dissertation considers a systematic comparison of existing MCAs based on a simple maneuver with only lateral (left/right) accelerations, i.e. a trajectory along a planar S-shaped track with constant velocity. All the MCAs were implemented and compared numerically using a novel measure – the well-tuned index. In a later stage, the trajectories of the MCAs were implemented in the KUKA Robocoaster and assessed by a group of 17 test-subjects. The results of the subjective assessment were compared with the numerical metrics. Furthermore, in the thesis an auto-tuning process based on the mean-variance mapping optimization (MVMO) and numerical perception scores was developed by which one is able to automatically tune the parameters to obtain a high well-tuned index, reducing drastically the current high manual tuning times. It was observed that, for a serial robot, the circular motion of the cabin can be well-compensated by the pitch angle. Among the MCAs, the optimal tracking algorithm (ZyRo) and the model predictive control (MPC*) can simulate large amplitude input signals while keeping a high value of the well-tuned index. Furthermore, these algorithms exploit the simulator’s workspace better than the other MCAs and are easily tuned. The ZyRo algorithm produces similar results as the MPC* algorithm, but requires less computational time. The responses of all the MCAs included in this study are similar when using a same low scaling factor (k = 0.4). However, if a larger scale factor is used, the responses of the MCAs change significantly. Finally, it was concluded that a good correlation between the average subjective scores and the objective measures can be achieved. However, due to the large variability of the individual scores, further research is needed to better understand the subjective ratings of simulation rides.Fahrsimulatoren werden für die Schulung von Fahrern/Piloten, in Fahrgeschäften, sowie in der Forschung des menschlichen Verhaltens verwendet. Motion-Cueing-Algorithmen (MCAs) zielen darauf ab, die Bewegung von fahrenden Fahrzeugen in dem begrenzten Arbeitsbereich der Fahrsimulatoren abzubilden und gleichzeitig den Wahrnehmungsrealismus der Simulation zu bewahren. In der Literatur wurden mehrere MCAs entwickelt, um den Realismus der Simulation zu verbessern. Jedoch weist jeder MCA Stärken und Schwächen im Vergleich zu den anderen. Dabei ist die Abstimmung der MCA-Parameter kritisch, da die Parameter für normale Simulator-Benutzer nicht intuitiv sind. In dieser Dissertation wird ein systematischer Vergleich zwischen bestehenden MCAs anhand eines einfachen Manövers mit nur seitlichen (links / rechts) Beschleunigungen, d.h. entlang einer ebenen S-förmigen Spur mit konstanter Geschwindigkeit durchgeführt. Alle MCAs wurden implementiert und numerisch mit einem neuartigen Maß - dem „gut abgestimmten Index“ („well-tuned index“) – miteinander verglichen. In einem späteren Stadium wurden die Trajektorien der MCAs im KUKA Robocoaster implementiert und von einer Gruppe von 17 Versuchspersonen bewertet. Die Ergebnisse der subjektiven Bewertung wurden dargestellt und mit den numerischen Metriken verglichen. In der Dissertation wird auch eine neue Auto-Tuning Methode entwickelt, die die Mittel-Varianz-Mapping-Optimierung (Mean Variance Mapping Optimization = MVMO) sowie numerische Empfindungscores verwendet, mit der die Parameter automatisch abgestimmt und damit hohe Werte des Bewegungsempfindungsindex erzielt werden können. Dadurch entfallen die sehr zeitaufwändigen manuellen Abstimmungsarbeiten bei gleicher oder besserer Bewegungsqualität. Es wurde beobachtet, dass für einen seriellen Roboter die Kreisbewegung der Kabine durch den Nickwinkel gut kompensiert werden kann. Unter den MCAs konnten der optimale Tracking-Algorithmus (ZyRo) und die modellbasierte prädiktive Regelung (MPC*) große Amplituden des Eingangssignals unter Erzielung eines hohen Wert des „gut abgestimmten Index“ simulieren. Darüber hinaus nutzten diese Algorithmen den Arbeitsbereich des Simulators besser als die anderen MCAs und waren leichter abzustimmen. Der ZyRo-Algorithmus erzeugt ähnliche Ergebnisse wie der MPC*-Algorithmus, erfordert aber weniger Rechenzeit. Die Bewegungseigenschaften aller in dieser Studie enthaltenen MCAs sind bei der Verwendung des gleichen niedrigen Skalierungsfaktors (k = 0,4) ähnlich. Wenn jedoch ein größerer Skalierungsfaktor verwendet wird, ändern sich die Antworten der MCAs signifikant. Zum Schluss wurde festgestellt, dass eine gute Korrelation zwischen den durchschnittlichen subjektiven Bewertungen und den objektiven Maßnahmen erreicht werden kann. Allerdings sind aufgrund der großen Variabilität der einzelnen Bewertungen weitere Untersuchungen erforderlich, um die subjektiven Bewertungen der Fahrsimulationen besser zu verstehen

Motion Fidelity Requirements for Helicopter-Ship Operations in Maritime Rotorcraft Flight Simulators

The research presented in this paper is part of a longer-term project to develop overall fidelity requirements for simulated helicopter shipboard operations to inform and support First of Class Flight Trials. The paper reports the results of motion cueing assessment and optimisation research, conducted in a six-degree-of-freedom motion flight simulator, to develop simulator motion drive laws capable of providing high fidelity motion cueing for simulated shipboard operations. To do this, a novel objective technique, Vestibular Motion Perception Error (VMPE), has been developed. The technique was utilised to optimise the motion cues for simulated helicopter landings on a naval single-spot destroyer at different wind and sea-state conditions. New simulator motion tuning sets were derived offline and then tested experimentally to compare the objective VMPE predictions with subjective assessments from a test pilot. Results show the influence of different motion cues, airwake conditions and ship motion states on the pilot’s overall perception of self-motion, control strategy, task performance and workload. It was found that high-fidelity motion cueing becomes more desirable for the pilot at higher wind conditions and sea states, for which an ‘Optimised’ motion setting was obtained using the new technique. Moreover, the use of an ‘Optimised’ motion setting generated by the VMPE methodology resulted in reduced pilot workload, leading to improved simulated maritime helicopter operational capability. The technique provides a rational methodology for motion tuning which could be applied in training and engineering simulators

Enhancing human motion perception in model predictive motion cueing algorithm

In this research, the predictive motion cueing algorithm has been optimized for improving a human driver sensation based on the mathematical model. The Model Predictive Control cost function and the prediction and control horizons are optimized. The future trajectory is predicted by artificial intelligence and the related control actions are applied beforehand in real-time

Research and Development on Noise, Vibration, and Harshness of Road Vehicles Using Driving Simulators - A Review

Noise, vibration, and harshness (NVH) is a key aspect in the vehicle development. Reducing noise and vibration to create a comfortable environment is one of the main objectives in vehicle design. In the literature, many theoretical and experimental methods have been presented for improving the NVH performances of vehicles. However, in the great majority of situations, physical prototypes are still required as NVH is highly dependent on subjective human perception and a pure computational approach often does not suffice. In this article, driving simulators are discussed as a tool to reduce the need of physical prototypes allowing a reduction in development time while providing a deep understanding of vehicle NVH characteristics. The present article provides a review of the current development of driving simulator focused on problems, challenges, and solutions for NVH applications. Starting from the definition of the human response to noise and vibration, this article describes the different driving simulator technologies to tackle all the involved perception aspects. The different available technologies are discussed and compared as to provide design engineers with a complete picture of the current possibilities and future trends

A review on otolith models in human perception

The vestibular system, which consists of semicircular canals and otolith, are the main sensors mammals use to perceive rotational and linear motions. Identifying the most suitable and consistent mathematical model of the vestibular system is important for research related to driving perception. An appropriate vestibular model is essential for implementation of the Motion Cueing Algorithm (MCA) for motion simulation purposes, because the quality of the MCA is directly dependent on the vestibular model used. In this review, the history and development process of otolith models are presented and analyzed. The otolith organs can detect linear acceleration and transmit information about sensed applied specific forces on the human body. The main purpose of this review is to determine the appropriate otolith models that agree with theoretical analyses and experimental results as well as provide reliable estimation for the vestibular system functions. Formulating and selecting the most appropriate mathematical model of the vestibular system is important to ensure successful human perception modelling and simulation when implementing the model into the MCA for motion analysis