The civil light helicopter domain has not fully benefited yet from the advantages system identification methods can offer. The aim of this paper is to show that system identification methods are mature enough to be successfully implemented in the civil helicopter domain. To achieve this goal, a Robinson R44 Raven II is identified in this work. The identification focuses on the hover trim condition. A lean frequency domain identification method is adopted. Furthermore, a new procedure is proposed to limit the sensitivity of the state-space minimization algorithm to initial parametric values and bounds. The resulting state-space model presents good predictive capabilities and is able to capture high-frequency rotor-body dynamics. The model is also validated with the help of a helicopter pilot by performing closed-loop control task maneuvers in the MPI CyberMotion Simulator. The overall validation shows that the implemented model is suitable for handling qualities studies, high-frequency control system designs, and realistic simulations that involve piloted closed-loop control tasks.

The paper describes the implementation and validation of a nonlinear model of the UH-60 helicopter. The implemented model is based on a physical vehicle and includes various important subsystems in order to increase the model fidelity. The validation is carried out through a Handling Qualities (HQ) evaluation and a comparison with flight data. Various standardized tests have been performed in the time and frequency domain for hover and the forward flight condition. Results obtained have been analyzed according to the criteria defined by the Aeronautical Design Standard ADS-33E-PRF. The behavior of our helicopter model is very similar to flight test data of the UH-60 in hover and in forward flight, although some coupling effects are not well described. Overall the model provides a reliable basis for use in motion-base simulators and as framework for conducting studies on control augmentation systems.

In this paper an augmentation strategy is implemented with the goal of making the behavior of an actual helicopter similar to that of a new class of aerial systems called Personal Aerial Vehicles (PAVs). PAVs are meant to be flown by flight-naive pilots, i.e., pilots with minimal flight experience. One feature required for achieving this goal, is to have a Translation Rate Command (TRC) response type in the hover and low-speed regime. In this paper, a TRC response type is obtained for a UH-60 helicopter simulation model in hover and low-speed regime through the implementation of nonlinear backstepping control. The responses of the rotorcraft with TRC response type are evaluated with the metrics defined in the Aeronautical Design Standard ADS-33E-PRF. Simulations show the efficiency of the control scheme in tracking the reference velocities and the achievement of the requirements to have level 1 Handling Qualities (HQ) for the TRC response type.

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.

This paper presents the implementation of classic augmented control stategies applied to an identified civil light helicopter model in hover. Aim of this study is to enhance the stability and controllability of the helicopter model and to improve its Handling Qualities (HQs) in order to meet those defined for a new category of aircrafts, Personal Aerial Vehicles (PAVs). Two control methods were used to develop the augmented systems, H∞ control and µ-synthesis. The resulting augmented systems were compared in terms of achieved robust stability, nominal performance and robust performance. The robustness was evaluated against parametric uncertainties and external disturbances modeled as real atmospheric turbulences that might be experienced in hover and low speed flight. The main result achieved in this work is that classical control techniques can augment a linear helicopter model to match PAVs responses at low frequencies. As a consequence, the achieved HQs performance resemble those defined for PAVs pilots. However, both control techniques performed poorly for some specific uncertainty conditions demonstrating unsatisfactory performance robustness. Differences, advantages and limitations of the implemented control architectures with respect to the considered requirements are described in the paper.

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.

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.

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.

Flight simulators are often assessed in terms of how well they imitate the physical reality that they endeavor to recreate. Given that vehicle simulators are primarily used for training purposes, it is equally important to consider the implications of visualization in terms of its influence on the user's control performance. In this paper, we report that a complex and realistic visual world environment can result in larger performance errors compared to a simplified, yet equivalent, visualization of the same control task. This is accompanied by an increase in subjective workload. A detailed analysis of control performance indicates that this is because the error perception is more variable in a real world environment.

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.

Highway-in-the-sky displays and haptic shared control could provide an easy-to-use control interface for non-expert pilots. In this paper, various display and haptic approaches are evaluated in a flight control task with a personal aerial vehicle. It is shown that a tunnel or a wall representation of the flight trajectory lead to best performance and lowest control activity and effort. Similar results are obtained when haptic guidance cues are based on the error of a predicted position of the vehicle with respect to the flight trajectory. Such haptic cues are also subjectively preferred by the pilots. This study indicates that the combination of a haptic shared control framework and highway-in-the-sky display can provide non-expert pilots with an easy-to-use control interface for flying a personal aerial vehicle.

Highway-in-the-sky displays and haptic shared control could provide an easy-to-use control interface for non-expert pilots. In this paper, various display and haptic approaches are evaluated in a flight control task with a personal aerial vehicle. It is shown that a tunnel or a wall representation of the flight trajectory lead to best performance and lowest control activity and effort. Similar results are obtained when haptic guidance cues are based on the error of a predicted position of the vehicle with respect to the flight trajectory. Such haptic cues are also subjectively preferred by the pilots. This study indicates that the combination of a haptic shared control framework and highway-in-the-sky display can provide non-expert pilots with an easy-to-use control interface for flying a personal aerial vehicle. Evaluation of Haptic Shared Control and a Highway-in-the-Sky Display for Personal Aerial Vehicles F. M. Nieuwenhuizen,∗ Max Planck Institute for Biological Cybernetics, Tübingen, Germany and H. H. Bülthoff† Max Planck Institute for Biological Cybernetics, Tübingen, Germany Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea Highway-in-the-sky displays and haptic shared control could provide an easy-to-use control interface for non-expert pilots. In this paper, various display and haptic approaches are evaluated in a flight control task with a personal aerial vehicle. It is shown that a tunnel or a wall representation of the flight trajectory lead to best performance and lowest control activity and effort. Similar results are obtained when haptic guidance cues are based on the error of a predicted position of the vehicle with respect to the flight trajectory. Such haptic cues are also subjectively preferred by the pilots. This study indicates that the combination of a haptic shared control framework and highway-in-the-sky display can provide non-expert pilots with an easy-to-use control interface for flying a personal aerial vehicle.

This paper describes the different phases of realizing and validating a helicopter model for the MPI CyberMotion Simulator (CMS). The considered helicopter is a UH-60 Black Hawk. The helicopter model was developed based on equations and parameters available in literature. First, the validity of the model was assessed by performing tests based on ADS-33E-PRF criteria using closed loop controllers and with a non-expert pilot. Results on simulated data were similar to results obtained with the real helicopter. Second, the validity of the model was assessed with a helicopter pilot in-the-loop in both a fixed-base simulator and the CMS. The pilot performed a vertical remask maneuver defined in ADS-33E-PRF. Most metrics for performance were reached adequately with both simulators. The motion cues in the CMS allowed for improvements in some of the metrics. The pilot was also asked to give a subjective evaluation of the model by answering the Israel Aircraft Industries Pilot Rating Scale (IAI PRS). Similarly to results of ADS-33E-PRF, pilot responses confirmed that the motion cues provided more realistic flight experience.

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.

Delays between user input and the system's reaction in control tasks have been shown to have a detrimental effect on performance. This is often accompanied by increases in self-reported workload. In the current work, we sought to identify physiological measures that correlate with pilot workload in a conceptual aerial vehicle that suffered from varying time delays between control input and vehicle response. For this purpose, we measured the skin conductance and heart rate variability of 8 participants during flight maneuvers in a fixed-base simulator. Participants were instructed to land a vehicle while compensating for roll disturbances under different conditions of system delay. We found that control error and the self-reported workload increased with increasing time delay. Skin conductance and input behavior also reflect corresponding changes. Our results show that physiological measures are sufficiently robust for evaluating the adverse influence of system delays in a conceptual vehicle model.

This paper presents the implementation of a Multi-Input Single-Output fully coupled transfer function model of a civil light helicopter in hover. A frequency domain identification method is implemented. It is discussed that the chosen frequency range of excitation allows to capture some important rotor dynamic modes. Therefore, studies that require coupled rotor/body models are possible. The pitch-rate response with respect to the longitudinal cyclic is considered in detail throughout the paper. Different transfer functions are evaluated to compare the capability to capture the main helicopter dynamic modes. It is concluded that models with order less than 6 are not able to model the lead-lag dynamics in the pitch axis. Nevertheless, a transfer function model of the 4th order can provide acceptable results for handling qualities evaluations. The identified transfer function models are validated in the time domain with different input signals than those used during the identification and show good predictive capabilities. From the results it is possible to conclude that the identified transfer function models are able to capture the main dynamic characteristics of the considered light helicopter in hover.