For many rotorcraft platforms, incorrect timing of the autorotation flare and deceleration maneuvers may result in significant aircraft damage and injury to the crew, or worse. There is a clear need for new pilot cueing and control augmentation technologies that lead to a higher probability of a successful autorotation landing. This paper describes a recent effort to develop two different Tau (time-to-contact)-based autorotation controllers that can be used to drive visual aids to help guide a pilot to apply the required control inputs to complete a safe autorotative landing. Such controllers may also be useful for fully autonomous autorotation landing for unmanned vehicles.

A novel trajectory generation and control architecture for fully autonomous autorotative flare that combines rapid path generation with model-based control is proposed. The trajectory generation component uses optical Tau theory to compute flare trajectories for both longitudinal and vertical speed. These flare trajectories are tracked using a nonlinear dynamic inversion (NDI) control law. One convenient feature of NDI is that it inverts the plant model in its feedback linearization loop, which eliminates the need for gain scheduling. However, the plant model used for feedback linearization still needs to be scheduled with the flight condition. This key aspect is leveraged to derive a control law that is scheduled with linearized models of the rotorcraft flight dynamics obtained in steady-state autorotation, while relying on a single set of gains. Computer simulations are used to demonstrate that the NDI control law is able to successfully execute autorotative flare in the UH-60 aircraft. Autonomous flare trajectories are compared to piloted simulation data to assess similarities and discrepancies between piloted and automatic control approaches. Trade studies examine which combinations of downrange distances and altitudes at flare initiation result in successful autorotative landings.

Autorotation maneuvers in helicopters are generally performed in an emergency following some form of catastrophic mechanical or system failure. It is a complex maneuver to perform because the pilot is required to perform several tasks simultaneously and the timing of each of them needs to be precise. Workload can be high and the consequences of getting things wrong can be fatal. Following on from a series of studies that investigated the use of symbology presented on a Head-Up Display to try to assist a helicopter pilot to fly the autorotation maneuver more safely and accurately, this paper presents a pilot-in-the-loop flight simulation study to explore the use of haptic cueing to help the pilot maintain indicated air- and main rotor speeds. Various entry conditions to autorotation maneuver are assess via simulated flight trial at Liverpool's HELIFLIGHT-R full motion flight simulator. Subjective evaluation of the results show that the haptic cues are useful to pilots in terms of reducing the workload to perform a successful autorotation landing.

No AccessTechnical NotesRapid Method for Computing Reachable Landing Distances in Helicopter Autorotative DescentBrian F. Eberle, Jonathan D. Rogers, Mushfiqul Alam and Michael JumpBrian F. EberleGeorgia Institute of Technology, Atlanta, Georgia 30332*Graduate Research Assistant, Mechanical Engineering.Search for more papers by this author, Jonathan D. Rogers https://orcid.org/0000-0001-7196-1691Georgia Institute of Technology, Atlanta, Georgia 30332†Lockheed Martin Associate Professor, Aerospace Engineering, 270 Ferst Drive; Associate Fellow AIAA (Corresponding Author).Search for more papers by this author, Mushfiqul AlamCranfield University, Cranfield, England MK43 0AL, United Kingdom‡Lecturer, Centre for Aeronautics. Member AIAA.Search for more papers by this author and Michael JumpUniversity of Liverpool, Liverpool, England L69 3GH, United Kingdom§Senior Lecturer, Department of Mechanical, Materials and Aerospace Engineering, Member AIAA.Search for more papers by this authorPublished Online:25 May 2022https://doi.org/10.2514/1.I011035SectionsRead Now ToolsAdd to favoritesDownload citationTrack citations ShareShare onFacebookTwitterLinked InRedditEmail About References [1] Bachelder E. N. and Aponso B. L., “Using Optimal Control for Rotorcraft Autorotation Training,” Proceedings of the 59th Annual Forum of the American Helicopter Society, AHS International, Fairfax, VA, May 2003. Google Scholar[2] Aponso B. L., Lee D. and Bachelder E. 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TopicsAircraft Components and StructureAircraft ControlAircraft DesignAircraft Flight Control SystemAircraft OperationsAircraft Operations and TechnologyAircraft PerformanceAircraft Stability and ControlAircraft Wing DesignAircraftsFixed-Wing AircraftHelicoptersRotorcrafts KeywordsHelicoptersComputingTest PilotsBanking TurnAirspeedFixed Wing AircraftAvionics SystemsGraphics Processing UnitLift to Drag RatioProportional Integral DerivativeAcknowledgmentsThis research/investigation was sponsored by the U.S. Army Research Laboratory and was accomplished under cooperative agreement number W911NF-16-2-0027 and the U.S. Army/Navy/NASA Vertical Lift Research Center of Excellence with Mahendra Bhagwat serving as the Program Manager and Technical Agent (grant numbers W911W6-11-2-0010, W911W6-17-2-0002, and W911NF-16-2-0027). The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Army Research Laboratory or the U.S. Government.PDF Received27 June 2021Accepted23 April 2022Published online25 May 2022

Autorotation is a challenging flight maneuver that involves highly coordinated control actions and competing constraints. It is generally recognized that pilot performance in autorotation may benefit from additional cues that improve decision-making as well as timing and coordination of control inputs. This paper describes the development and simulator testing of various pilot cues designed for autorotation. A set of discrete and continuous cues are defined to assist in the initiation and execution of various phases of the maneuver. Furthermore, a reachability cue is created to assist a pilot in rapidly evaluating the vehicle glide distance, thereby facilitating selection of a landing site. Piloted simulated flight trials are performed using various combinations of cues in both good and degraded visual environments. Results are evaluated through assessment of pilot workload as well as quantitative measures of landing performance with and without the cues. Overall, results and pilot evaluations show the potential utility of several of the cueing methods but point to specific improvements to them that may facilitate more precise pilot tracking as well as reductions in task workload.

Physical-law-based models are widely utilized in the aerospace industry. One such use is to provide flight dynamics models for use in flight simulators. For human-in-the-loop use, such simulators must run in real-time. Owing to the complex physics of rotorcraft flight, to meet this real-time requirement, simplifications to the underlying physics sometimes have to be applied to the model, leading to errors in the model's predictions of the real vehicle's response. This study investigated whether a machine-learning technique could be employed to provide rotorcraft dynamic response predictions. Machine learning was facilitated using a Gaussian process (GP) nonlinear autoregressive model, which predicted the on-axis pitch rate, roll rate, yaw rate, and heave responses of a Bo105 rotorcraft. A variational sparse GP model was then developed to reduce the computational cost of implementing the approach on large datasets. It was found that both of the GP models were able to provide accurate on-axis response predictions, particularly when the model input contained all four control inceptors and one lagged on-axis response term. The predictions made showed improvement compared to a corresponding physics-based model. The reduction of training data to one-third (rotational axes) or one-half (heave axis) resulted in only minor degradation of the sparse GP model predictions.

Physical law based models (also known as white box models) are widely applied in the aerospace industry, providing models for dynamic systems such as helicopter flight simulators. To meet the criteria of real-time simulation, simplifications to the underlying physics sometimes have to be applied, leading to errors in the model's predictions. Grey-box models use both physics-based and data-based models. They have potential to reduce the difference between a simulator's and real rotorcraft's response. In the current work, a preliminary step to the grey-box approach, a machine learnt data-based, i.e 'black box' model is applied to the dynamic response of a helicopter. The machine learning methods used are probabilistic and can capture uncertainties associated with the model's prediction. In the current paper, machine learning is used to create a Gaussian Process (GP) non-linear autoregressive (NARX) model that predicts pitch, roll and yaw rate. The predictions are compared to a physical law based model created using FLIGHTLAB software. The GP outperforms the FLIGHTLAB model in terms of root mean squared error, when predicting the pitch, roll and yaw rate of a Bo105 helicopter.

This study aimed to understand more fully some of the factors that influence decisions as related to air defence in a naval vessel's operation room. The study considered the impact of decision criticality (DC) and task load (TL) on measures of accuracy, confidence, and within-subjects confidence-accuracy (W-S C-A; a measure of metacognition). Personality constructs, workload, and situational awareness were also assessed. Participants were allocated to either a high, moderate, or low TL condition. Each took part in a computer-generated simulated air defence scenario where they were required to make a range of decisions and provide a corresponding confidence rating for each decision taken. Results showed that low DC increased confidence in decisions and high DC increased decision accuracy. Thus, DC significantly impacts decision confidence and decision accuracy. In addition, those less tolerant of ambiguity were less accurate in their decision-making. Future studies should take account of these factors.

The time-to-contact tau theory posits that purposeful actions can be conducted by coupling the actor's motion onto the so-called tau guides generated internally by their central nervous system. Although significant advances have been made in the application of tau for flight control purposes, little research has been conducted to investigate how pilots are able to adapt their tau-guidance strategy to different aircraft dynamics, or how a tau-guide-based pilot-aircraft model might be used to represent control behavior. This paper reports on the development of such a model to characterize the adaptation of pilot guidance to variations in aircraft dynamics using data obtained from a clinical pilot-in-the-loop flight simulation experiment. The results indicate that pilots tend to maintain a constant coupling between the dynamic system's motion and the tau guide across a range of different configuration parameters. Simultaneously, the pilot modulates the guidance maneuver period to adapt to these different aircraft dynamics that result in changes in workload. Modeling the complete pilot stabilization and guidance function as a regulator plus inverter yields good comparative results between the pilot-aircraft model and simulator trajectory data, and it supports the hypothesis that the following tau-based guidance strategies suppress an aircraft's natural dynamics.

In future inspections of offshore assets utilizing robots, robots will not only be expected to collate new data from their payload of instruments, but they will also be expected to interact with the infrastructure being inspected, undertake remedial tasks and engage with embedded monitoring systems of the asset. This increasing level of interaction and deployment frequency of robot inspections requires an understanding of how we can embed safe and trusted operational architectures within robots. Currently, robots can undertake constrained semi-autonomous inspections, using predetermined tasks (missions) with minimum supervision. However, the challenge is that the state of the world changes with time as does the condition of the robot. Therefore, robots must be able to undertake adaptive measures to support optimal outcomes during autonomous missions. In this paper, we propose an initial architecture to the safe verification and validation of health condition and certification of robotic and autonomous inspection systems for offshore assets. Our first contribution relates to the verification and validation architecture, which takes into account risks associated with asset inspection, safety protocols, evolving ambient changes, as well as the inherent state of health of the robot. The second part of our paper looks to how prognostic analytics can be used to support robot resilience in terms of sensor drift and accurate state of health estimates of critical sub-systems. Initial results demonstrate that methods such as relevance vector machines and Bayesian networks can be used to accurately mitigate risks to autonomy.

This paper details the design and limited flight testing of a preliminary system for visual pilot cueing during autorotation maneuvers. The cueing system is based on a fully-autonomous, multi-phase autorotation control law that has been shown to successfully achieve autonomous autorotation landing in unmanned helicopters. To transition this control law to manned systems, it is employed within a cockpit display to drive visual markers which indicate desired collective pitch and longitudinal cyclic positions throughout the entire maneuver, from autorotation entry to touchdown. A series of simulator flight experiments performed at University of Liverpool’s HELIFLIGHT-R simulator are documented, in which pilots attempt autorotation with and without the pilot cueing system in both good and degraded visual environments. Performance of the pilot cueing system is evaluated based on both subjective pilot feedback and objective measurements of landing survivability metrics, demonstrating suitable preliminary performance of the system.

Interest in personal aerial vehicles (PAVs) is resurgent with several flying prototypes made possible through advances in the relevant technologies. Whilst the perceived wisdom is that these vehicles will be highly automated or autonomous, the current regulatory framework assumes that a human will always be able to intervene in the operation of the flight. This raises the possibility of manually operated PAVs and the requirement for an occupant flying training programme. This paper describes the development of training requirements for PAV pilots. The work includes a training needs analysis (TNA) for a typical PAV flight. It then describes the development of a training programme to develop the skills identified by the TNA. Five participants with no real flying experience, but varying levels of driving experience, undertook the training programme. Four completed the programme through to a successful simulation flight test of a commuter flight scenario. These participants evaluated the effectiveness of the training programme using the first three Levels of Kirkpatrick’s method. The evaluation showed that the developed training programme was effective, in terms of both trainee engagement and development of the handling skills necessary to fly PAV mission-related tasks in a flight simulator. The time required for the four successful participants to develop their core flying skills was less than 5 h. This duration indicates that future simulation PAV training would be commensurate with the training duration for current personal transportation modes.

With recent increased interest in autonomous vehicles and the associated technology, the prospect of realizing a personal aerial vehicle seems closer than ever. However, there is likely to be a continued requirement for any occupant of an air vehicle to be comfortable with both the automated portions of the flight and their ability to take manual control as and when required. This paper, using the approach to landing as an example maneuver, examines what a comfortable trajectory for personal aerial vehicle occupants might look like. Based upon simulated flight data, a "natural" flight trajectory is designed and then compared to constant deceleration and constant optic flow descent profiles. It is found that personal aerial vehicle occupants with limited flight training and no artificial guidance follow the same longitudinal trajectory as has been found for professionally trained helicopter pilots. Further, the final stages of the approach to hover can be well described using the Tau theory. For automatic flight, personal aerial vehicle occupants prefer a constant deceleration profile. For approaches flown manually, the newly designed natural profile is preferred.

The involuntary interaction of a pilot with an aircraft can be described as pilot-assisted oscillations. Such phenomena are usually only addressed late in the design process when they manifest themselves during ground/flight testing. Methods to be able to predict such phenomena as early as possible are therefore useful. This work describes a technique to predict the adverse aeroservoelastic rotorcraft-pilot couplings, specifically between a rotorcraft's roll motion and the resultant involuntary pilot lateral cyclic motion. By coupling linear vehicle aeroservoelastic models and experimentally identified pilot biodynamic models, pilot-assisted oscillations and no-pilot-assisted oscillation conditions have been numerically predicted for a soft-in-plane hingeless helicopter with a lightly damped regressive lead-lag mode that strongly interacts with the roll mode at a frequency within the biodynamic band of the pilots. These predictions have then been verified using real-time flight-simulation experiments. The absence of any similar adverse couplings experienced while using only rigid-body models in the flight simulator verified that the observed phenomena were indeed aeroelastic in nature. The excellent agreement between the numerical predictions and the observed experimental results indicates that the techniques developed in this paper can be used to highlight the proneness of new or existing designs to pilot-assisted oscillations.