Pilot Adaptation to Different Classes of Haptic Aids in Tracking Tasks

International Journal of Human–Computer Interaction

Ellerbroek

et al. 2021

Modern aircraft can be equipped with a flight envelope protection system: automation which modifies pilot control inputs to ensure that the aircraft remains within the allowable limits. Overruling the pilot inputs may lead to mode confusion, even when visual or auditory feedback is provided to alert pilots. We advocate using active control devices to make the flight envelope protection system tangible to the pilot. This paper presents the main findings of an evaluation of three haptic feedback designs for flight envelope protection. The first concept used both force feedback and vibro-tactile alerts, producing promising, yet inconclusive, results. The second concept used asymmetric vibrations to give directional alerting cues, which did not result in improved performance on initial use, but which did yield improved learning rate for the task. The third system employed force feedback to physically guide the pilot away from flight envelope limits, which yielded safety improvements from the first use, but created dependence: pilot performance degraded immediately after the force feedback was removed. From this, we advise to use asymmetric vibrations during training for flight envelope excursions, to leverage active control interfaces for providing force feedback during operation, and reevaluate a combination of both to combine their advantages for single-pilot operations.

Section: Single-pilot Operationsmentioning

confidence: 99%

Flying by Feeling: Communicating Flight Envelope Protection through Haptic Feedback

Baelen

International Journal of Human–Computer Interaction

Ellerbroek

et al. 2021

“…The literature shows different ways of changing the haptic profile: in the automotive field, there is a strong focus on using a forcing function that can be used as both a warning signal [29,30] or as a guidance force [20,21]. Aerospace applications show examples that add a soft stop (a local step in the amount of force required), a hardstop (a change in maximum deflection) [5], forcing functions [31], changes in the stick neutral position [12], and changes in nominal stick stiffness [32,33]. An example of haptic feedback in the current Airbus A320 flight deck is the detent present on the thrust levers: the controls "click" in the important thrust positions (such as maximum thrust, or the take-off/go-around setting) and require a threshold force to move away from this position.…”

Section: A Haptic Feedback Definitionsmentioning

confidence: 99%

“…If applied, the information provided by the haptic display is directly proportional to a required control command and, in principle, the pilot can "just follow the position." Previous research showed that using such an approach increased tracking performance while reducing the physical effort [31]. If the pilot does not agree, however, (s)he can choose to override the cue and keep the stick position fixed by actively counteracting, using co-contraction of the muscles [36].…”

Section: Change the Position Of The Neutral Pointmentioning

confidence: 99%

Design of a Haptic Feedback System for Flight Envelope Protection

Baelen

Ellerbroek

Journal of Guidance, Control, and Dynamics

2020

Several modern aircraft use a passive control manipulator: a spring-damper system that generates command signals to the flight control computers in combination with a flight envelope protection system that limits pilot inputs when approaching the aircraft limits. This research project aims to increase pilot awareness of this protection system through the use of force feedback on the control device, that is, haptics. This paper describes in detail how the haptic feedback works and when it triggers; another paper will discuss the results of an experimental evaluation. With the current haptic design, pilots can get five cues: first, a discrete force cue when approaching the limits; second, an increased spring coefficient for control deflections that bring the aircraft closer to its limits; third, a stick shaker for low velocities; fourth, if a low-velocity condition requires an input, the stick is moved forward to the desired control input; and finally, the stick follows the automatic Airbus "pitch-up" command during an overspeed condition. This novel system is expected to help pilots correctly assess the situation and decide upon the right control action. It will be evaluated in two scenarios close to the flight envelope limits: a windshear and an icing event. Nomenclature a = acceleration, m∕s 2 C L = lift coefficient D = drag, N F = force, N g = gravitational acceleration, m∕s 2 K = gain k = spring, N∕rad L = lift, N m = mass, kg n = load factor, g q = pitch rate, rad∕s S = surface, m 2 T = thrust, N t = time, s V = velocity, m∕s W = weight, N α = angle of attack, rad β = sideslip angle, rad γ = flight path angle, rad δ = control device deflection, rad θ = pitch angle, rad ρ = density, kg∕m 3 φ = roll angle, rad Subscripts br = breakout max = maximum value min = minimum value nom = nominal value np = neutral point prot = protected region value

“…All four requirements involve one or more objective thresholds chosen by the user, which will depend on the application. Applications relying on precise predictions of future control inputs, e.g., advanced motion cueing [31], will set more stringent requirements than those that rely on an 'average' HC model, e.g., haptic aids for easy-to-control dynamics [32].…”

Section: Model Selection Criterion Tuningmentioning

confidence: 99%

Objective Model Selection for Identifying the Human Feedforward Response in Manual Control

Drop

Pool

et al. 2018

IEEE Trans. Cybern.

Abstract-Realistic manual control tasks typically involve predictable target signals and random disturbances. The human controller (HC) is hypothesized to use a feedforward control strategy for target-following, in addition to feedback control for disturbance-rejection. Little is known about human feedforward control, partly because common system identification methods have difficulty in identifying whether, and (if so) how, the HC applies a feedforward strategy. In this paper an identification procedure is presented that aims at an objective model selection for identifying the human feedforward response, using linear time-invariant ARX models. A new model selection criterion is proposed to decide on the model order (number of parameters) and the presence of feedforward in addition to feedback. For a range of typical control tasks, it is shown by means of Monte Carlo computer simulations that the classical Bayesian Information Criterion (BIC) leads to selecting models that contain a feedforward path from data generated by a pure feedback model: 'false-positive' feedforward detection. To eliminate these false-positives, the modified BIC includes an additional penalty on model complexity. The appropriate weighting is found through computer simulations with a hypothesized HC model prior to performing a tracking experiment. Experimental human-in-theloop data will be considered in future work. With appropriate weighting, the method correctly identifies the HC dynamics in a wide range of control tasks, without false-positive results.