1,720,960 research outputs found
Objective Evaluation of Flight Simulator Motion Cueing Fidelity Through a Cybernetic Approach
No full textCompared to aircraft, flight simulators are severely limited in their motion envelopes. Presenting the true aircraft motion one-to-one on flight simulators is generally impossible and it is therefore common practice that these motion stimuli are only presented in reduced and attenuated form. Because of a limited understanding of human motion perception processes and how these affect the perceived realism of the multimodal stimuli pilots are subjected to during aircraft control (most notably, visual and physical motion stimuli), the definition of requirements for flight simulator motion cueing fidelity is a problem that researchers and legislators have struggled with for years. This thesis therefore describes and evaluates an objective method for the assessment of simulator cueing motion fidelity. The proposed method is centered around an analysis of the control dynamics adopted by pilots during manual control tasks, and how they use visual and motion stimuli in their selected control strategy, using multimodal pilot models. This approach thereby allows for the objective and quantitative evaluation of flight simulator motion fidelity, by explicitly considering how degraded motion cueing fidelity affects a simulator's ability to induce real-flight manual control behavior. This thesis describes a number of experiments in which pilot manual control behavior was measured using this approach in the Cessna Citation II laboratory aircraft and the SIMONA Research Simulator at Delft University of Technology. A comparison of the collected measurements clearly shows that variations in simulator motion cueing fidelity result in changes in pilot manual control behavior. With increased motion cueing fidelity, pilots are seen to rely significantly more on the presented motion stimuli, a control strategy that also typically results in increased manual control performance. Furthermore, these experiments also show that important behavioral parameters that characterize the weighing of visual and motion information by pilots also correspond best with those measured for in-flight pilot behavior when simulator motion cues are close to those of real flight.Control & SimulationAerospace Engineerin
Quantifying loss of motor skills after cerebellar stroke: Control behavior of cerebellar patients in a preview tracking task
No full textA cerebellar stroke causes motor deficits and may complicate daily activities of patients. Their movements become uncoordinated with errors in speed and accuracy. To quantify the level of impairment, this paper proposes a preview tracking task. Such a task assesses and quantifies eye-hand coordination skills. A preview experiment on a touch-screen has been conducted with four cerebellar patients, four age-matched healthy control subjects, and a group of 26 young adults. Age-matched control subjects and young adults performed eight runs of the preview task, with the dominant hand only. Patients performed eight runs with each hand to make a distinction between the hand affected by the lesion and the unaffected hand. During the experiment, both eye- and hand movements were recorded. Results were obtained with system identification techniques. A model of the human controller in preview tasks was fitted to the estimated frequency response of the subject. The resulting control parameters revealed the tracking behaviour of the subjects. Patients show significantly higher visual time delays, neuromuscular damping ratios and lead time constants than age-matched control subjects. Consequently, patients engage in a more cautious control strategy than age-matched control subjects.Overall, the proposed task is found to be capable of detecting visuo-motor impairment in cerebellar patients. It could be used to quantify impairment as a result of neurological disorders, and monitor improvement during the revalidation phase and afterwards.Aerospace Engineerin
Mitigation of Biodynamic Feedthrough for Touchscreens on the Flight Deck
No full textBiodynamic feedthrough (BDFT) is a key issue for touchscreen operations on the future flight deck, as cockpit accelerations due to turbulence leave pilots vulnerable to erroneous touches that disrupt task performance. This research focuses on the implementation of a software-based cancellation approach to mitigate the adverse effects of BDFT in touchscreen dragging tasks. A flight-simulator experiment with 18 participants was performed to estimate models of BDFT dynamics for horizontal and vertical touch-inputs on a primary flight display. The averaged BDFT models were used to cancel BDFT in the same continuous dragging task used for model identification and a discrete point-to-point dragging task. While for the continuous task the cancellation enabled 63% mitigation in BDFT, the same cancellation was ineffective for the discrete task, due to reduced BDFT susceptibility. Overall, the results show that while model-based BDFT cancellation can be highly effective, a key technical challenge will be ensuring it is sufficiently task-adaptive.Control & Simulatio
A Cybernetic Approach to Assess the Training of Manual Control Skills
Full text linkThis paper presents a cybernetic approach to assess the training of manual control skills in simulators. The approach uses multi-channel pilot models that separate pilots’ responses to visual and motion stimuli. This allows for a quantitative analysis of pilots’ use of visual and motion cues for manual aircraft control, as well as the evolution of these control skills during training and after transfer. The cybernetic approach was applied to data from a quasi-transfer-of-training experiment performed in the SIMONA Research Simulator at Delft University of Technology. In this experiment, fully task-naive participants were trained to perform an aircraft pitch attitude tracking task in a fixed-base simulator environment. After training, participants were transferred to a motion-base simulator environment. Results indicate that the cybernetic approach is successful in revealing progressive changes in participants’ utilization of visual and motion cues – i.e., their equalization dynamics – during training and after transfer. Furthermore, the results show that convergence to a final skill-based manual control strategy requires significant training
Incremental nonlinear control of hydraulic parallel robots: An application to the SIMONA research simulator
No full textIn advanced robotic applications such as robotic locomotion, vehicle and flight simulators, and material test devices, there are higher requirements on stiffness, robustness and power ability for the mechanical structure and the actuator. Hence, it is common for such applications to use parallel manipulators and hydraulic actuators, due to their advantages in these aspects over their counterparts of serialmanipulators and electrical actuators. When high-precision motion control is required for such systems, advanced model-based controllers, including feedback linearization and adaptive control, have been proposed in state-of-the-art studies for both hydraulic and parallel mechanical systems. However, the high complexity, nonlinearity and model uncertainty of these systems raise significant challenges for their motion control accuracy.Control & Simulatio
Measuring, modelling and minimizing perceived motion incongruence: for vehicle motion simulation
No full textHumans always wanted to go faster and higher than their own legs could carry them, leading them to invent numerous types of vehicles to move fast over land, water and air. As training how to handle such vehicles and testing new developments can be dangerous and costly, vehicle motion simulators were invented. Motion-based simulators in particular, combine visual and physical motion cues to provide occupants with a feeling of being in the real vehicle. While visual cues are generally not limited in amplitude, physical cues certainly are, due to the limited simulator motion space. A motion cueing algorithm (MCA) is used to map the vehicle motions onto the simulator motion space. This mapping inherently creates mismatches between the visual and physical motion cues. Due to imperfections in the human perceptual system, not all visual/physical cueing mismatches are perceived. However, if a mismatch is perceived, it can impair the simulation realism and even cause simulator sickness. For MCA design, a good understanding of when mismatches are perceived, and ways to prevent these from occurring, are therefore essential.In this thesis a data-driven approach, using continuous subjective measures of the time-varying Perceived Motion Incongruence (PMI), is adopted. PMI in this case refers to the effect that perceived mismatches between visual and physical motion cues have on the resulting simulator realism. The main goal of this thesis was to develop an MCA-independent off-line prediction method for time-varying PMI during vehicle motion simulation, with the aim of improving motion cueing quality.To this end, a complete roadmap, describing how to measure and model PMI and how to apply such models to predict and minimize PMI in motion simulations is presented. Results from several human-in-the-loop experiments are used to demonstrate the potential of this novel approach. Control & Simulatio
Objective evaluation of human manual control adaptation boundaries using a cybernetic approach
No full textManual control tasks can be found everywhere in our daily activities, and the human ability to adapt in controlling many different vehicles such as cars and airplanes make it possible for us to travel farther, faster and higher. The human adaptation ability to changes in the controlled element dynamics is indispensable for tasks requiring high performance and safety, and none of the state-of-the-art automatic control systems can compete. For example, in the racing industry, professional racing drivers are needed to adapt to different carconfigurations and consistently push the car to its performance limit in the driving simulator and on the track, which is important for designing and tuning the cars. In aviation, pilots are our “last line of defense” for flight safety, especially in emergency situations in which automatic flight systems fail.Control & Simulatio
Identification of manual control behaviour to assess rotorcraft handling qualities
No full textFlight safety has been a fundamental aspect of aircraft, and the future demand for wider usage of aerial operations leads to more focus on the flight safety. Particularly rotorcraft require high standards of flight safety due to their inherent features, such as complicated rotary mechanisms, close-to-ground operations, and complex aerodynamic environment. Consequently, rotorcraft pilots need to exert relatively high workload to safely operate these vehicles. An understanding of the interaction between the rotorcraft and the pilot is essential for improving flight safety. This interaction is elaborated by the Handling Qualities (HQ) discipline, which aims to identify and, if possible predict any deficiency in HQ that could potentially jeopardize safe flight. A typical (and potentially catastrophic) example of a HQ deficiency are the Aircraft / Rotorcraft Pilot Couplings (A/RPC), formerly referred to as Pilot Induced Oscillations (PIO). A/RPC is defined as the involuntary and adverse interaction between the pilot and the vehicle under control. Generally for rotorcraft, the ‘vehicle’ part of this interaction is evaluated by objective HQ criteria and online Rotorcraft Pilot Coupling (RPC) detection tools, whereas the ‘pilot’ part is assessed with subjective pilot ratings. Using subjective ratings has several disadvantages, such as being used at very late stages of the design when a prototype vehicle is already built. Addressing a serious HQ deficiency after this late design stage then requires immerse effort to re-design the vehicle systems and repeat the flight tests...Control & Simulatio
Modelling Individual Driver Trajectories to Personalise Haptic Shared Steering Control in Curves
No full textRoad safety is still a challenging issue. In 2020, 1.35 million people have died as a result of traffic accidents, where the number one cause of death for young adults between the age of 5 and 29 is car accidents. In an attempt to improve road safety, the automotive industry has developed numerous types of Advanced Driver Assistance Systems (ADAS). These systems are in general effective in improving safety. However, these systems will only be used if and only if drivers perceive the assistance as intuitive and cooperative. It is recently found that 61% of drivers sometimes switch off the assistance, 23% feel that current assistance are annoying and bothersome, whereas only 21% find them helpful. A safe system that is not used has no safety benefits. A promising way to improve driver acceptance and to increase safety is to employ haptic shared control (HSC), which is an effective way of keeping drivers in the active control loop. Support in the form of HSC benefits situation awareness and ensures effective monitoring of the environment and automation. However, torque conflict resulting from opposing intentions of driver and automation is reported to be a bottleneck for drivers' acceptance of HSC. Particularly, such conflicts are found to be most debilitating in curves. With each driver having an individual driving style, with different preferences and skill levels, the current standard 'one-size-fits-all' assistance approach to HSC, and driver support in general, is not satisfactory for every individual. An effective approach to increase acceptance in ADAS, and a reliable way to align the automation to the driver's preferences, is through personalisation. Here, personalisation is generally defined as 'making something suitable for the needs and preferences of a particular person'. For HSC, personalisation can be effectively realised by adapting the system's adopted trajectory to that of the driver. Therefore, the personalisation of HSC requires a driver modelling approach that predicts an individual driver's behaviour. Before this thesis, the personalisation of HSC was attempted by adjusting the gains of a corrective feedback HSC, as though it were a driver steering model itself. What was missing was 1) a HSC that allows for personalisation, i.e., a framework where a personalisable reference trajectory is independent of the haptic controller and, 2) a computational driver model or a data-driven driver classification approach that is able to describe individual drivers. When this thesis was started, a theoretical HSC concept, the 'Four-Design-Choice-Architecture' (FDCA) was introduced within our group. This promising concept was, however, not realised or implemented yet. As for modelling individual drivers, it was not known what type of driver steering and trajectory model(s) are suitable to generate personalised trajectories, if any, due to the lack of a standardised way to compare and evaluate the output performance of driver behaviour models with different structures and complexities. It was not known exactly how to achieve successful personalisation in curves, nor was the needed level of personalisation understood, i.e., adapting to the intricacies of each individual or adapting to a more general style. Moreover, whether personalisation in itself improves the acceptance of HSC systems, was still to be verified. These challenges are addressed in the four parts of this thesis: 1) Driver model assessment: The development of an assessment method and application on prominent control-theoretic driver models in the literature. %This was done to gain in-depth understanding of what is needed to model and describe individual drivers. 2) Driver trajectory classification: Understanding and categorising the types of individual driver trajectories present in the driving population. 3) Driver prepositioning: Understanding and modelling driver prepositioning behaviour, a behaviour found to be an essential, yet mostly overlooked aspect of curve-driving behaviour. 4) Application to Haptic Shared Control: Apply and evaluate personalised haptic shared control. This thesis has achieved it's highest level goal, which is to improve the acceptance of the haptic shared control driver support. This thesis provides an improved understanding and new insights into 1) how the novel FDC HSC has solved much of the acceptance issue put forward, and 2) an understanding of how to personalise with the FDC HSC. In terms of modelling tools and methods, this thesis has contributed with: 1) a model assessment procedure that can highlight the strengths and weaknesses of any control theoretic model, 2) a trajectory classifier, which can categorise different types of drivers, 3) a prepositioning path model, which, when combined with the Van Paassen control-theoretic driver model results in the first individual control-theoretic driver model, i.e., a model that can capture all main styles of individual driver behaviour and 4) the first personalisable HSC, where the developed modelling methods are applied to evaluate personalised haptic shared control. The findings and insights from this thesis have contributed to design guidelines and, can accelerate future research. Some examples include 1) using the individualised driver steering model, personalisation of ADAS can now be done in real-time, 2) using the developed trajectory classifier, explicit personalisation can be achieved, i.e., the driver can select the type of trajectory guidance he may want, and, 3) the driver trajectory modelling methods developed in this thesis can be used for the personalisation of path-planning in fully autonomous-vehicles.Human-Robot InteractionOLD Intelligent Vehicles & Cognitive RoboticsControl & SimulationCognitive Robotic
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