Mario Olivari

Pilot Adaptation to Different Classes of Haptic Aids in Tracking Tasks

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 h...

Multi-loop Pilot Behaviour Identication in Response to Simultaneous Visual and Haptic Stimuli

The goal of this paper is to better understand how the neuromuscular system of a pilot, or more generally an operator, adapts itself to di erent 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 di erent identi cation 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 identi ed 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 identi cation techniques were validated using Monte Carlo simulations of a closed-loop control task. Both techniques were compared with the results of another identi cation 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 di erent haptic aids. Two di erent 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 di erent haptic feedback schemes. From the experimental results it can be concluded that humans adapt their neuromuscular admittance in relation with di erent haptic aids. Furthermore, the two new identi cation techniques seemed to give more reliable admittance estimates.

Identifying time-varying neuromuscular response: experimental evaluation of a RLS-based algorithm

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 timevarying 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.

An experimental comparison of haptic and automated pilot support systems

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.

Methods for Multiloop Identification of Visual and Neuromuscular Pilot Responses

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 humans neuromuscular and visual responses in cases where the classic method fails.

Identifying Time-Varying Neuromuscular System with a Recursive Least-Squares Algorithm: a Monte-Carlo Simulation Study

A human-centered design of haptic aids aims at tuning the force feedback based on the effect it has on human behavior. For this goal, a better understanding of the influence of haptic aids on the pilot neuromuscular response becomes crucial. In realistic scenarios, the neuromuscular response can continuously vary depending on many factors, such as environmental factors or pilot fatigue. This paper presents a method that online estimates time-varying neuromuscular dynamics during force-related tasks. This method is based on a Recursive Least Squares (RLS) algorithm and assumes that the neuromuscular response can be approximated by a Finite Impulse Response filter. The reliability and the robustness of the method were investigated by performing a set of Monte-Carlo simulations with increasing level or remnant noise. Even with high level of remnant noise, the RLS algorithm provided accurate estimates when the neuromuscular dynamics were constant or changed slowly. With instantaneous changes, the RLS algorithm needed almost 8s to converge to a reliable estimate. These results seem to indicate that RLS algorithm is a valid tool for estimating online time-varying admittance.

Evaluation of haptic support system for training purposes in a tracking task

Haptic guidance has previously been employed to improve human performance in control tasks. This paper presents an experiment to evaluate whether haptic feedback can be used to help humans learn a compensatory tracking task. In the experiment, participants were divided into two groups: the haptic group and the no-aid group. The haptic group performed a first training phase with haptic feedback and a second evaluation phase without haptic feedback. The no-aid group performed the whole experiment without haptic feedback. Results indicated that haptic group achieved better performance than the no-aid group during the training phase. Furthermore, performance of haptic group did not worsen in the evaluation phase when the haptic feedback was turned off. Moreover, the no-aid group needed more experimental trials to achieve similar performance to the haptic group. These findings indicate that haptic feedback helped participants learn the task quicker.

Identifying Time-Varying pilot's responses: A regularized recursive Least-Squares algorithm

Methods for identifying pilot’s responses commonly assume time-invariant dynamics. However, humans are likely to vary their responses during realistic control scenarios. In this work an identification method is developed for estimating time-varying responses to visual and force feedback during a compensatory tracking task. The method represents pilot’s responses with finite impulse response filters and use a Regularized Recursive Least Squares (RegRLS ) algorithm to estimate the filter coefficients. The method was validated in a Monte-Carlo simulation study with different levels of remnant noise. With low levels of remnant noise, estimates were accurate and tracked the time-varying behavior of the simulated responses. On the other hand, estimates showed high variability in case of large remnant noise. Taken together, these findings suggest that the novel RegRLS algorithm could be used to estimate time-varying pilot’s responses in real human-in-the-loop experiments.

On Effects of Failures in Haptic and Automated Pilot Support Systems

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. It is possible to design Haptic aids that allow pilots to improve performance compared with the baseline condition, even if these are usually outperformed by automation. It is not very well understood yet however, what happens to performance in the event of a failure of the Pilot support system. To investigate how and if a pilot can recovery its performance after a failure of the haptic or automated support system, a quantitative comparison is needed. An experiment was conducted in which pilots performed a compensatory tracking task with haptic aids and with automation. Half of the runs were affected by a failure of the support system, resulting in complete removal of the support action. 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 compared to a baseline condition without external aids. As expected pilots performance is better with the automated support system, than with Haptic when no failure happens. When a Failure happens, pilots experience a sudden decrease of performance in both cases, but loss of performance is much higher in the automation case. In addition and somehow surprisingly, after the initial loss of performance, pilots flying with the Haptic aid return approximately to the performance level they had just before the failure, while pilots flying with Automation cannot re-gain pre-failure levels of performance, at least in the time span of the experiment.

Identifying Time-Varying Neuromuscular Response: A Recursive Least-Squares Algorithm with Pseudoinverse

Effectiveness of haptic guidance systems depends on how humans adapt their neuromuscular response to the force feedback. A quantitative insight into adaptation of neuromuscular response can be obtained by identifying neuromuscular dynamics. Since humans are likely to vary their neuromuscular response during realistic control scenarios, there is a need for methods that can identify time-varying neuromuscular dynamics. In this work an identification method is developed which estimates the impulse response of time-varying neuromuscular system by using a Recursive Least Squares (RLS) method. The proposed method extends the commonly used RLS-based method by employing the pseudo inverse operator instead of the inverse operator. This results in improved robustness to external noise. The method was validated in a human in-the-loop experiment. The neuromuscular estimates given by the proposed method were more accurate than those obtained with the commonly used RLS-based method.