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

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

Development and Optimization of Motion Cueing for Flight Simulation of Maritime Helicopter Operations

Maritime helicopter operations such as helicopter ship deck landings are highly demanding for both the pilot and the helicopter. In the absent of potential safety risks during an offshore mission a maritime simulation environment offers a benefit for pilot training and development of new systems and procedures. Such a maritime simulation environment requires a specific optimized motion system. In the recent past years new methods have been developed to tune the motion system in an easier, faster and objective way. Unfortunately, these methods are not sufficiently validated because however suitable validation methods are still missing. This master thesis presents a methodology to implement a Classical Washout Algorithm (CWA) in the simulator. To develop suitable motion parameter sets for a helicopter ship deck landing procedure the fitness function is used as a novel optimization method. The validation is conducted with piloted simulator flight test trials that are new developed. Additionally, an unpiloted validation is carried out with the Objective Motion Cueing Test (OMCT). The results of both validation methods are compared to each other

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

Intelligent Systems Approach for Automated Identification of Individual Control Behavior of a Human Operator

Results have been obtained using conventional techniques to model the generic human operator?s control behavior, however little research has been done to identify an individual based on control behavior. The hypothesis investigated is that different operators exhibit different control behavior when performing a given control task. Two enhancements to existing human operator models, which allow personalization of the modeled control behavior, are presented. One enhancement accounts for the testing control signals, which are introduced by an operator for more accurate control of the system and/or to adjust the control strategy. This uses the Artificial Neural Network which can be fine-tuned to model the testing control. Another enhancement takes the form of an equiripple filter which conditions the control system power spectrum. A novel automated parameter identification technique was developed to facilitate the identification process of the parameters of the selected models. This utilizes a Genetic Algorithm based optimization engine called the Bit-Climbing Algorithm. Enhancements were validated using experimental data obtained from three different sources: the Manual Control Laboratory software experiments, Unmanned Aerial Vehicle simulation, and NASA Langley Research Center Visual Motion Simulator studies. This manuscript also addresses applying human operator models to evaluate the effectiveness of motion feedback when simulating actual pilot control behavior in a flight simulator

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

Real Coded Mixed Integer Genetic Algorithm for Geometry Optimization of Flight Simulator Mechanism Based on Rotary Stewart Platform

Featured Application Low-cost flight simulators with electric rotary actuators and optimized geometry for flight simulation. Designing the motion platform for the flight simulator is closely coupled with the particular aircraft's flight envelope. While in training, the pilot on the motion platform has to experience the same feeling as in the aircraft. That means that flight simulators need to simulate all flight cases and forces acting upon the pilot during flight. Among many existing mechanisms, parallel mechanisms based on the Stewart platform are suitable because they have six degrees of freedom. In this paper, a real coded mixed integer genetic algorithm (RCMIGA) is applied for geometry optimization of the Stewart platform with rotary actuators (6-RUS) to design a mechanism with appropriate physical limitations of workspace and motion performances. The chosen algorithm proved that it can find the best global solution with all imposed constraints. At the same time, the obtained geometry can be manufactured because integer solutions can be mapped to available discrete values. Geometry is defined with a minimum number of parameters that fully define the mechanism with all constraints. These geometric parameters are then optimized to obtain custom-tailored geometry for aircraft flight simulation