The goal of this paper is to better understand how the neuromuscular system of a pilot, or more generally an operator, adapts itself to different types of haptic aids during a pitch control task. A multi-loop pilot model, capable of describing the human behaviour during a tracking task, is presented. Three different identification techniques were investigated in order to simultaneously identify neuromuscular admittance and the visual response of a human pilot. In one of them, the various frequency response functions that build up the pilot model are identified using multi-inputs linear time-invariant models in ARX form. A second method makes use of cross-spectral densities and diagram block algebra to obtain the desired frequency response estimates. The identification techniques were validated using Monte Carlo simulations of a closed-loop control task. Both techniques were compared with the results of another identification method well known in literature and based on crossspectral density estimates. All those methods were applied in an experimental setup in which pilots performed a pitch control task with different haptic aids. Two different haptic aids for tracking task are presented, a Direct Haptic Aid and an Indirect Haptic Aid. The two haptic aids were compared with a baseline condition in which no haptic force was used. The data obtained with the proposed method provide insight in how the pilot adapts his control behavior in relation to different haptic feedback schemes. From the experimental results it can be concluded that humans adapt their neuromuscular admittance in relation with different haptic aids. Furthermore, the two new identification techniques seemed to give more reliable admittance estimates.
In this paper, identification methods are proposed to estimate the neuromuscular and visual responses of a multiloop pilot model. A conventional and widely used technique for simultaneous identification of the neuromuscular and visual systems makes use of cross-spectral density estimates. This paper shows that this technique requires a specific noninterference hypothesis, often implicitly assumed, that may be difficult to meet during actual experimental designs. A mathematical justification of the necessity of the noninterference hypothesis is given. Furthermore, two methods are proposed that do not have the same limitations. The first method is based on autoregressive models with exogenous inputs, whereas the second one combines cross-spectral estimators with interpolation in the frequency domain. The two identification methods are validated by offline simulations and contrasted to the classic method. The results reveal that the classic method fails when the noninterference hypothesis is not fulfilled; on the contrary, the two proposed techniques give reliable estimates. Finally, the three identification methods are applied to experimental data from a closed-loop control task with pilots. The two proposed techniques give comparable estimates, different from those obtained by the classic method. The differences match those found with the simulations. Thus, the two identification methods provide a good alternative to the classic method and make it possible to simultaneously estimate human's neuromuscular and visual responses in cases where the classic method fails.
External aids are required to increase safety and performance during the manual control of an aircraft. Automated systems allow to surpass the performance usually achieved by pilots. However, they suffer from several issues caused by pilot unawareness of the control command from the automation. Haptic aids can overcome these issues by showing their control command through forces on the control device. To investigate how the transparency of the haptic control action influences performance and pilot behavior, a quantitative comparison between haptic aids and automation is needed. An experiment was conducted in which pilots performed a compensatory tracking task with haptic aids and with automation. The haptic aid and the automation were designed to be equivalent when the pilot was out-of-the-loop, i.e., to provide the same control command. Pilot performance and control effort were then evaluated with pilots in-the-loop and contrasted to a baseline condition without external aids. The haptic system allowed pilots to improve performance compared with the baseline condition. However, automation outperformed the other two conditions. Pilots control effort was reduced by the haptic aid and the automation in a similar way. In addition, the pilot open-loop response was estimated with a non-parametric estimation method. Changes in the pilot response were observed in terms of increased crossover frequency with automation, and decreased neuromuscular peak with haptics.
Haptic aids have been largely used in manual control tasks to complement the visual information through the sense of touch. To analytically design a haptic aid, adequate knowledge is needed about how pilots adapt their visual response and the biomechanical properties of their arm (i.e., admittance) to a generic haptic aid. In this work, two different haptic aids, a direct haptic aid and an indirect haptic aid, are designed for a target tracking task, with the aim of investigating the pilot response to these aids. The direct haptic aid provides forces on the control device that suggest the right control action to the pilot, whereas the indirect haptic aid provides forces opposite in sign with respect to the direct haptic aid. The direct haptic aid and the indirect haptic aid were tested in an experimental setup with nonpilot participants and compared to a condition without haptic support. It was found that control performance improved with haptic aids. Participants significantly adapted both their admittance and visual response to fully exploit the haptic aids. They were more compliant with the direct haptic aid force, whereas they showed stiffer neuromuscular settings with the indirect haptic aid, as this approach required opposing the haptic forces
This paper investigated use of a haptic support system for learning purposes. A 2 Degrees of Freedom (DoF) haptic force feedback system was designed for a dual-axes compensatory tracking task. The haptic system was used in a human-in-the-loop experiment with inexperienced participants on a xed-base simulator. In the experiment, participants were divided into 3 groups. All participants performed 30 trials of the compensatory tracking task. A group of participants (NoHA group) performed the whole experiment without haptic aid. The other two groups (HA20 and HA10 groups) performed a training phase with haptic aid, followed by an evaluation phase without haptic feedback. The HA20 group performed 20 trials in the training phase, whereas the HA10 group performed only 10 trials. The results show that haptic aid was benecial for performing the tracking task in the training phase for both the axes, compared to manual control. In the pitch axis performance of the HA20 group did not worsen when the feedback was switched o, whereas a considerable deterioration in performance was visible for HA10 group. Thus, haptic force feedback was eective to learn the control task in the pitch axis, compared to manual control. In the roll axis overall performance was found to be worse than the pitch axis.\ud Moreover no benets were found from training with haptic feedback in the roll axis for both the haptic groups
Methods for identifying neuromuscular response commonly assume time-invariant neuromuscular dynamics. However, neuromuscular dynamics are likely to change during realistic control scenarios. In a previous paper we presented a method for identifying time-varying neuromuscular dynamics based on a Recursive Least Squares (RLS) algorithm. To date, this method has only been validated in a Monte Carlo simulation study. This paper presents an experimental validation of the same method. In the experiment, three different disturbance-rejection tasks were performed: a position task with the human instructed to minimize the stick deflection in front of an external force disturbance, a relax task with the instruction to relax the arm, and a time-varying task with the instruction to alternate between position and relax tasks. The position and relax tasks induce different time-invariant neuromuscular dynamics, whereas the time-varying task induces time-varying neuromuscular dynamics. The RLS-based method was used to estimate neuromuscular dynamics in the three tasks. The neuromuscular estimates were reliable both in time-invariant and time-varying tasks. These findings indicate that the RLS-based method can be used to estimate time-varying neuromuscular responses in human-in-the loop experiments
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