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DEVELOPMENT OF A PILOT MODEL FOR THE MANUAL BALKED LANDING MANEUVER

Ruud Hosman1 AMS Consult, Delfgauw, the Netherlands

and Peter van der Geest2 and Jeroen van der Zee3

National Aerospace Laboratory, NLR, Amsterdam, the Netherlands

Based on the need to set up requirements for the obstacle free zone for the New Large Aircraft, and renew the Collision Risk Model, the Instrument Flight Procedural Panel (IFPP) of the ICAO asked for the development of pilot models capable to simulate pilot’s skill-based control behavior of a manual flown approach to land followed by a balked landing. The pilot model was developed to control the B-747-400 based on a dedicated aircraft model developed by the Boeing Company. The FAA will use the combined pilot-aircraft model as the basis for a Monte Carlo Simulation to determine the aircraft deviations from the nominal flight path under a wide range of atmospheric and operational conditions. The IFPP asked for two pilot models based on different principles. The present model is based on control engineering principles. In the course of the development of both pilot models it turned out that a procedural model was required to model and control pilot’s rule-based behavior initiating the discrete procedural actions. Since the first publication of McRuer on models of pilot’s control behavior a wide range of models describing human control behavior have been developed. The present model is based on an extension of McRuers work, has visual and vestibular feedback and is describing pilot’s control behavior in the inner attitude control loop. For the present application, sub models to describe pilot’s behavior in the outer loops were developed. In addition, special attention had to be paid to the pilot model to obtain accurate lateral control of the aircraft during the de-crab. Special attention had to be paid to the non-linear part of pilot’s control behavior necessary to match pilot model tracking performance. The pilot model is build up into three groups of models for symmetric, asymmetric and longitudinal control for the different phases of the total maneuver: Flight Director segment, visual segment, flare and de-crab, go-around and re-crab and Flight Director climb out. Thrust control is applied only for the FD and visual segment. The pilot model parameters are adjusted for each phase with criteria for tracking performance, control effort and control bandwidth and stability. To match the results of the pilot model with measured pilot performance use is made of the results of a Balked Landing Simulator Experiment performed at NASA Ames. The paper will describe the pilot model, the interaction with the procedural model, the tracking performance results and the comparison with the experimental results of the NASA Ames simulator study.

1. Introduction

Based on the need to set up requirements for the obstacle free zone for the New Large Aircraft, and renew the Collision Risk Model the Instrument Flight Procedural Panel (IFPP) of the ICAO asked for the development of pilot models capable to simulate pilot’s skill-based control behavior during a manual flown approach to land followed by a balked landing. A balked landing is an approach to land which is aborted in a very late phase of the landing; e.g., at a height at or below 50 ft above the runway. The need to address this issue arises from questions concerning the

1 Director, Dijkgraafstraat 26, 2645 KN Delfgauw, the Netherlands, r.hosman@amsconsult.demon.nl, AIAA Senior Member. 2 Sr Scientist, Air Transport-Safety and Flight Operations, P.O. Box 90502, 1006 BM, Amsterdam. 3 Scientist, Air Transport-Safety and Flight Operations, P.O. Box 90502, 1006 BM, Amsterdam.

AIAA Modeling and Simulation Technologies Conference10 - 13 August 2009, Chicago, Illinois

AIAA 2009-5818

Copyright © 2009 by Copyright 2009 by R.J.A.W. Hosman. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission.

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dimensions of the Obstacle Free Zone (OFZ) that is required in order that an NLA be allowed to operate at given aerodromes. As currently addressed in the ICAO Instrument Flight Procedures Panel (IFPP), it needs to be established whether the OFZ, as specified for the largest category of aircraft (code F, ICAO Annex 14) is appropriate for the NLA. It should be noted that the definition of the OFZ in the past has been based on aircraft equipped with significantly less advanced technologies (both the aircraft flight control system and the navigation aids) than the current generation of transport aircraft. If the current specification of the OFZ would be overly conservative, it might unduly restrict the introduction of NLA at certain aerodromes, or vice versa may require unnecessary investments for aerodromes to allow the introduction of NLA. In order to address this problem, the ICAO IFPP took the initiative to ask for a quantitative (probabilistic) safety assessment. The ICAO, the national aviation authorities and the airplane industry1 have supported this initiative. In this way the level of safety provided by the OFZ can be established, and compared with a given target level of safety. In addition to the balked landing following an automatic approach, the IFPP has also asked for an analysis of the manually flown balked landing following a flight director approach. The Flight Operations and Analysis Branch of the FAA offered to support this effort by the use of the Airspace Simulation and Analysis Tool (ASAT) to perform the analysis2. For this purpose, sufficiently reliable and accurate aircraft and pilot models need to be developed. Today the state-of-the-art modeling techniques are largely developed to a level that enables the development of these models. Since no validated models for NLA were available jet, the Boeing Company made the model of the B747-400 airplane available for this study. The IFPP asked for the independent development of two pilot models based on different principles. For this purpose QinetiQ, at Farnborough in the UK, Belyavin3, and the National Aerospace Laboratory in Amsterdam /AMS Consult were invited to employ their expertise to develop the pilot models. The aim for these models is that they should be able to simulate pilot’s control behavior during an approach followed by a balked landing under a range of aircraft configurations (center of gravity, weight and flap setting), atmospheric conditions (wind, gust and visibility) and should be able to capture the effects of pilot variability. To support the development of the pilot models, a simulation of the closed loop of the pilot and aircraft model was required. For this purpose, Boeing in Seattle developed a simulation of the B747-400, representing the characteristics of a NLA, including models of the aerodynamics, engines, and flight controls, and rather detailed models of the flight director and yaw damper4. This model was implemented in MSC Software's Engineering Analysis System (EASY5) environment and made available to QinetiQ and NLR. After the pilot models have been validated, they will be integrated into the high fidelity aircraft model and delivered to the FAA to perform Monte Carlo simulations in ASAT. The results of these simulations will be used to determine the OFZ. In addition to this effort, manned simulations of the balked landing were performed in the B747-400 FFS at NASA Ames and at Boeing in a fixed base engineering simulator in order to generate a database of performance data. The purpose of the database is to provide data on rule- and skill-based pilot behavior to support the evaluation and fine-tuning of the pilot models. To this end an extensive statistical analysis of the database has been carried out by Hörmann5, van der Geest6, Hosman7, de Leege8. Finally, the Central Aerohydrodynamic Institute (TsAGI) in Moscow has been invited to evaluate the final pilot models in order to assure that they are adequate for establishing an OFZ, which meets the required target level of safety9.

2. Basic pilot model and parameter adjustment

The pilot's task in a modern transport aircraft represents more and more one of management (i.e., supervisory control) than of manually controlling a dynamic system. As a supervisory controller, the pilot directs and/or controls the different aircraft systems and is free to decide which part of the task should be performed manually and which part by the automatic systems. Rasmussen10, in an attempt to structure human operator behavior, classified this behavior into three levels: skill-based, rule-based and knowledge-based behavior. During the manually controlled balked landing maneuver the pilot generates control input to the aircraft at the skill-based level and takes discrete actions at the rule-based level. The pilot models developed at QinetiQ and NLR deal with the control behavior at the skill-based level. Rule-based behavior will be based on the statistical analysis of the flight simulator data obtained during piloted simulations of the balked landing maneuver. A procedural model to generate these discrete rule-based actions was developed at the NLR6.

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The original request from the ICAO IFPP was to develop a pilot model, which should be able to simulate pilot’s manual control of an aircraft through the whole balked landing maneuver, based on flight director commands11. The maneuver should start at a distance of 8.15 km from the runway threshold (corresponding to about 1400 ft on the ILS approach path) and end after climbing through 400 ft on the go-around. However, given the given the fact that a manual flown approach always end with a visual landing, it was decided to incorporate a visual segment into the maneuver starting at or above the decision height. Consequently, the pilot model should correct the aircraft position based on visual cues from the outside world starting at an altitude no lower than the decision height, and should resume control based on flight director commands after the go-around initiation. Accordingly, the go-around maneuver controlled by the pilot model has been divided into five segments:

1) Flight director approach down to an altitude no less than 200 ft 2) Visual segment down to flare and de-crab initiation 3) Flare and de-crab 4) Pitch attitude rotation and re-crab after go-around initiation 5) Flight director climb-out

In addition, the pilot models for these segments incorporate speed/thrust control for segments 1 and 2. For all segments it was assumed that the deviations from the intended path are sufficiently small that the pilot models for symmetric and asymmetric control of each segment are independent. So, pilot model parameters for symmetric and asymmetric control can be adjusted independently. The results of the statistical analysis5,6,7,8 performed on the manned simulation data show that the flare and de-crab are initiated at different altitudes above the runway. The NLR/AMS Consult pilot model is based on the descriptive pilot model, Hosman12,13, which was developed to describe pilot’s control behavior in the inner attitude control loop, Fig. 2.1 This pilot model uses visual and vestibular feedback cues as input. Research results show the contribution of both visual and vestibular cues to pilot’s control behavior. Therefore, it is important that a moving base FFS, with a high performance visual and motion system was used to generate the NASA data base on pilot’s behavior and performance during the balked landing. A block diagram of the descriptive pilot model is shown in Fig. 2.2.

Sensor dynamics, perception and decision delay and neuro-motor dynamics have been derived from experimental research and the literature. The only free parameters of the model are the gains Ki weighing the sensory outputs Ri. For the inner attitude control loop these parameters can easily be adjusted using an optimization procedure with the proper control loop characteristics, i.e. aircraft model, forcing function i(t) and external disturbance w(t). When a pilot adjusts his/her behavior to a certain control task, the first objective is to achieve an acceptable level of tracking performance. When the pilot tries to minimize the tracking error alone, his control actions do not take into account the aircraft characteristics, structural loads and passenger comfort, for example. In reality, the pilot will normally consider putting more effort into the task as a function of the benefit of the resulting performance improvement, and to the corresponding increase in workload. For these reasons, to bring the workload effect into account, the mean square of the control signal )(tu and its derivative )(tu& have to be added to the cost function. There is another consideration: When a pilot tries to improve tracking performance, he will increase his gain. This will result in an increase of the crossover frequency ωc and a decrease in phase margin ϕm. A too high gain will

Figure 2.1. The pilot model in the inner attitude control loop.

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reduce the stability of the control loop. So, the choice of the cost function and its weight factors should aim at the following:

• Good tracking performance • Effective control effort • Adequate bandwidth and stability of the control loop as expressed by the crossover frequency and the phase

margin

In order to achieve these goals, the following general cost function can be applied.

J =Σ ( 2e + Q. 2u + R. 2u& ) (1)

By incorporating the control output u(t) and its derivative )(tu& the control effort and the control bandwidth are taken into account. The weighing factors Q and R in the cost function depend on the aircraft characteristics, and on the task to be performed. So, the pilot model adjustment procedure is to first adjust the model to the task based on the above mentioned principles and after that to compare and fine tune the pilot model control behavior and performance by adjusting the model parameters. The cost function is considered to be representative for pilot’s control strategy. The same adjustment procedure can be applied for the between and within (inter- and intra-) pilot variability. Varying the bandwidth of the pilot model - aircraft open loop by adjusting the weighing factors and consequently the model parameters simulates more aggressive or relaxed pilot behavior. It has to be noted here that part of the intra pilot variability is due to the adaptation of the pilot to independent realizations of the external atmospheric disturbance. Adjusting the pilot model parameters to other aircraft configurations, i.e. weight, cg, and flap setting, can be performed with the appropriate aircraft model and the same cost function. The aircraft configurations taken into account for the pilot model adjustment for the benefit of the pilot model validation correspond to those of the NASA Data collection configurations. There is another method to adjust the pilot model parameters possible. In that case, pilot model parameters are adjusted to fit pilot model behavior to actually measured pilot behavior as in the NASA Data collection. This method, however, has a number of limitations:

1. Pilot model parameters are valid only for the condition measured and dependent on: aircraft configuration, weather condition and turbulence simulated, and individual pilot behavior. Extrapolation to other conditions is limited.

Figure 2.2. The descriptive pilot model.

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2. Although the inter and intra pilot variability will be reflected into the pilot model parameters it is doubted if for all segments reliable parameter sets can be obtained due to the highly non-steady condition (flare, de-crab and go-around) and difficulty in identifying the model parameters in the available short time intervals.

3. A large number of pilot model parameter sets will be gained from the manned simulation data base with limited coverage of aircraft configurations envelope.

It has been shown by several authors14,15 that the two external disturbances on the control loop, the forcing function i(t) and the disturbance function w(t), Fig. 2.1, have a different influence on pilot’s control behavior especially when motion feedback is available. In the more complicated situation where the pilot controls the aircraft position in a set of nested control loops, Fig. 2.3, it is of importance to correctly characterize the external disturbances on the control loops when adjusting the pilot model parameters to obtain realistic pilot model behavior. This dictates careful attention to the setup of the control loop and external disturbances during the parameter adjustment in the different phases of the balked landing maneuver as will be discussed in the next section. The control of the symmetric and asymmetric aircraft position is treated separately. In the case of the application of the pilot model to simulate pilot’s control behavior and performance, it is required to extend the model to simulate the non-linear behavior of the human operator. In real life, pilot’s control behavior will be affected with inaccuracies and errors. To take that into account, a remnant signal n(t) (a bandwidth limited noise signal) is added to the model control output, Fig. 2.4. The way the remnant signal is generated and incorporated into the pilot model is described in the next Section.

Pilot model parameter adjustment was executed in Matlab/Simulink. In the final application of the pilot model during the Monte Carlo simulations the aircraft model will be disturbed with von Karman turbulence. During the NASA Data Collection, however, the simulated aircraft was disturbed by the CAE atmospheric disturbance model. Therefore, both atmospheric disturbance models can be applied. Due to the fact that each atmospheric disturbance realization leads to a different set of model parameters, 10 different atmospheric disturbance files are used in the parameter adjustment process. This corresponds with the real live situation where the pilot adapts his behavior to the actual atmospheric disturbance on the aircraft. This is the main cause of intra pilot variability. Averaging the 10 sets of model parameters provides the final values of the model parameters for the particular application.

3. The pilot model

In 2005 the pilot model was already described16. Since a number of changes and improvements were required to adjust the model to the more extended aircraft model Boeing made available. Describing all details of the pilot

Figure 2.3. Nested loops with the pilot controlling aircraft position.

Fig. 2.4. Pilot model with remnant n(t)

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model is therefore not necessary and would make this paper too long. Only, a number of special features developed for the application of the pilot model for the Balked Landing Study will be discussed below. The visual segment The visual segment is the last part of the manual flown ILS approach. The pilot controls the aircraft primarily based on the outside visual scene. It starts at or above decision height (≥ 200 ft) when the aircraft approaches the runway and ends at the start of the flare and de-crab or the go-around. For the pilot model it is assumed that during the visual segment, the pilot primarily looks outside and has to interpret the outside visual scene to determine his position relative to the intended flight path. The intended flight path corresponds with the extended runway centerline and the - 3° glide path. To correct for the lateral and vertical deviations to the intended approach path, the pilot has to close the inner attitude loops and the outer flight path and position loops, Fig. 2.3. In the inner attitude control loop, the pilot model12,13 describes pilot’s control behavior. The model takes visual and vestibular inputs into account and is basically a linear model, Fig. 2.2. In addition to the remnant, describing pilot’s inaccuracy and non-linearity in generating his control output, pilot’s inaccuracy in perceiving the aircraft’s position relative to the intended approach path has to be added to ensure a realistic performance of the pilot model for the present application. Based on the work of McRuer17 on pilot’s control behavior, Clement18 on pilot control during the manual ILS approach, and by Wewerinke19 on perception accuracy during the visual segment, pilot’s perception uncertainty of the aircraft position can be modeled. This is modeled by inserting a low bandwidth random bias (pilot position uncertainty), Figs 3.2, with a magnitude dependent on the distance to the touch down aiming point, Fig. 3.1. The bandwidth of the uncertainty random signal is 0.075 rad/s for the height uncertainty and 0.05 rad/s for the lateral position uncertainty. Due to the short duration of the visual segment (± 14 s) these random signals has a mean and a variation both decreasing when closing in to the runway and are based on the results of the manned simulations of the balked landing and on the results of a study on the position perception accuracy by Wewerinke, Fig. 3.1.

Stand. Dev. (Δh) = 11.5 + 0.0053*x [ft]

Stand. Dev. (Δy) = 2 + 0.0054*x [ft], (2)

where x is the horizontal distance of the aircraft to the touchdown point.

Figure 3.1. The standard deviation of the perception uncertainty of height Δh and lateral deviation Δy.

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The pilot model parameters for the inner loop control are adjusted with an optimization procedure. The bandwidth of the inner control loops depends on the aircraft characteristics, i.e. the short period mode and the roll mode. The outer loop pilot gains are adjusted according to Hess20. The bandwidth as expressed by the crossover frequency of the outer loops is a fraction of the bandwidth of the inner loop. Hess gives an indication by: ωc outer loop ≈ 0.25 - 0.3 * ωc inner loop. For control of the lateral deviation to the approach path the pilot controls the aircraft roll angle in the inner attitude control loop. The descriptive pilot model is applied to the inner roll-attitude control loop. To minimize the lateral position error ey, the pilot has first to make corrections of the aircraft heading ψ to control the lateral position. Therefore a track feedback loop has to be incorporated and pilot gains for lateral deviation Ky and heading control Kψ are required in the outer loops, Fig. 3.2. Due to the lateral wind component the aircraft drifts across the approach path. To compensate for the drift the pilot will correct the reference track ψref , which can be performed by extending the pilot gain Ky to a PI controller. The transfer function of this PI controller is:

sCsKsH latdev

ytrollerLateralcon)()( +

= . (3)

The parameters for the lateral control Ky, Clatdev, and Kχ are adjusted with a separate optimization after the inner attitude loop is established.

De-crab and re-crab after go-around initiation In the sequence of the approach/balked landing so far, the pilot model has performed the approach to land by elevator and aileron control with zero sideslip. In case of a crosswind, the aircraft heading deviates from the track angle by the drift or crab angle. Before touchdown, the pilot may de-crab the aircraft by correcting the aircraft heading with rudder input to correspond with the track angle and correcting for the resulting drift by rolling the aircraft into the crosswind. This has also to be accomplished by the pilot model. From the analysis of the NASA data collection5.6,7,8 it turns out that the subject pilots used a mixture of the sideslip and touchdown in crab technique. To de-crab the aircraft they start the sideslip at around 40 ft for the high crosswind and at about 30 ft for the low crosswind condition. Subject pilots did not perform the de-crab completely and land with a reduced crab angle of about 0.5 times the original crab angle. Based on the NASA Data the procedural model6 generates a side slip command to decrease the crab angle. When initiating the de-crab with the sideslip technique, a rudder input is generated based on the side slip command while the drift is compensated by maintaining the required track by aileron input. In Fig. 3.3 a yaw control loop is added to the lateral control, Fig. 3.2, during the visual segment. The inner yaw loop model is activated when the de-crab is started and the pilot inner loop yaw model provides the required rudder input. The pilot inner loop yaw model is basically the descriptive pilot model as presented in Fig. 2.

Figure 3.2. Lateral control during the visual segment.

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Track control by aileron input is too slow to compensate fast enough for the disturbance of the track angle resulting from the rudder input. The skilled pilot is aware of that effect and corrects correspondingly. Therefore, the rudder input is fed back to the pilot model to generate a compensatory aileron control input.

The procedural model starts the re-crab after the go-around is initiated by reducing the commanded side slip. For those cases where the go-around is initiated before the de-crab is started, the de-crab will not be performed. Pilot model parameters are adjusted in three steps. First the inner roll attitude loop and second the lateral control loop is adjusted with the aircraft model for 30 ft for the flare and de-crabs. Thereafter the yaw control loop is adjusted.

Figure 3.4 Aft quadrant wheel (CIACQPOS) position for run n11-1013 during FD segment. Aircraft weight 420.000 lbs, Wind 090/10, gust medium. Remnant The output related remnant magnitude is considered to have a certain relation to the magnitude of the final control signal. The standard deviation of the remnant is about 0,25 to 0.3 of the standard deviation of the final control signal20. Therefore, the pilot model remnant magnitude is a function of the control signal magnitude. When remnant is generated with white noise a filter has to be applied to configure the power spectrum to represent the remnant characteristics. From the literature it is clear that remnant powers spectra depend on the task and may have varying magnitude of the power spectrum up to a frequency of 2 to 4 rad/s. Above that frequency the power spectrum falls off with 40 db/decade, Steurs21.

Figure 3.3. Lateral control during the visual segment extended with rudder control for the de-crab.

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With a closer look at the time histories of the NASA data files it turns out that the subject pilots during the FD, visual segment, and the climb out generate a pulse like control output when compensating for errors rather than a continuous control signal. See Fig. 3.4. This can be modeled by generating an output only if a certain threshold is exceeded.

By adding the remnant directly to the model output, it will be non-zero while the control signal may be zero. To avoid this, the remnant signal is multiplied with the control output, Fig. 3.5. After the control signal passed the threshold it is multiplied with the noise signal, filtered and the remnant is added to the control signal. The threshold value may be varied according to the pilot’s control behavior in the NASA or Boeing data set. The final remnant magnitude is adjusted with a gain Kremnant. As an example the pilot model roll control output during the FD segment is presented in Fig. 3.6.

4. Results So far, the NLR/AMS Consult pilot model structure has been determined for longitudinal, lateral and speed/thrust control for the five segments of the balked landing maneuver.

Figure 3.6. Pilot model roll control output for the FD segment. Aircraft weight 420000 lbs, wind 090/10, gust medium.

Figure 3.5. Pilotmodel with remnant incorporated

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When it became clear that TsAGI will use the completed EASY5 model for the first step of the pilot model validation, the aircraft model in EASY5 was extended with the undercarriage, ground effect and a more refined engine model. In addition, it was decided to develop a Procedural model6 describing pilot’s discrete procedural actions during the balked landing maneuver. Presently, the final pilot model and the Procedural model are implemented in EASY5 and when integrated ready to be tested. To first test the integrated pilot model and procedural model, the pilot model was adjusted to configuration 1 of the NASA Data Collection. So far, performance data have been derived for the off line simulations in Matlab/Simulink in the subsequent segments and compared with the performance data from the NASA Data Collection as far as a clear vertical or lateral reference is available. The pilot model data are obtained from 10 realizations of the simulated atmospheric disturbance during the NASA Data Collection while the NASA data are valid for the particular flight segment and based on 13 pilot subjects and 4 replications. The pilot model parameters were varied to simulate the inter and intra pilot variability. The performance data are presented in Fig. 4.1.

The final pilot model will first be adjusted to the 12 Configurations of the NASA Data Collection for validation of the pilot model performance. When validated, the pilot model will be adjusted to 36 aircraft configurations (aircraft weight, cg, flap setting, and MCP speed) for application in the Monte Carlo simulations. So far the pilot model development is completed and fine tuning of the model parameters to assure that the pilot model tracking performance will match the NASA FFS simulator data will be performed this year. The question whether manual flown simulated balked landings have statistically the same tracking performance as manual flown balked landings in the day to day flight operation has still to be answered. Since a full pilot model for the balked landing has been developed an analysis with the simulation of the B747 FFS will be performed to evaluate the influence of the simulation process on pilot’s tracking performance.

5. Discussion A pilot model to control a large transport aircraft, B-747, through the approach to land followed by a balked landing and climb out has been developed and is ready to be used. For the application to the Balked Landing Study, validation in both the EASY 5 environment and ASAT has to be performed. It may be expected that during the validation fine tuning of the pilot model parameters is necessary. Where the structure of the pilot model is

Figure 3.7. Off line pilot model performance compared with pilot subject performance during the NASA Data Collection. Pilot model data based on 10 runs, NASA Data based on 13 subjects and 4 replications.

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independent of the aircraft model, application to other aircraft types is foreseen for the extension of the Balked Landing Study. Some questions are still open. The transition from segment to segment was developed and tested with an earlier version of the pilot model but has to be tested with the present model and to demonstrate to work properly. For the NASA Data Collection, which will be used for the pilot model validation, a full flight simulator was used. To what extend the simulation process (time delays, motion cueing, and motion system dynamics) influenced the tracking performance is not well known and a point of concern. An analysis with the present pilot model to investigate the contribution of the simulation process to the tracking performance is planned. Finally, the pilot model has to be adjusted to a wider range of aircraft configurations, i.e. aircraft weight, cg, and flap setting and environmental disturbance, i.e. von Karman turbulence.

6. References 1 Robinson, J (2009). The Use of Pilot Modeling in Aviation Regulatory Affairs. AIAA Modeling and Simulation

Technology Conference, Chicago 10-13 August, 2009 AIAA-2009-5825. 2 Barnes, S, G. Lankford, G. McCartor, and S. Ladecky (2005). “The new FAA flight simulation laboratory’s impact on flight

procedure design”. AIAA Modeling and Simulation Technologies Conference, San Francisco, CA. August 15-18, 2005. AIAA 2005-5880.

3 Belyavin, A. and C. Ryder (2009). A Discrete Event Controller as a Model of Pilot Control behaviour. AIAA Modeling and Simulation Technology Conference, Chicago 10-13 August, 2009 AIAA-2009-5820.

4 Nuygen, D., G. Robel, J. Robinson, J. Towler, and J. Woolworth (2005). Implementation of a large airplane simulation model to support pilot model development. AIAA Modeling and Simulation Technologies Conference, San Francisco, August 15-18, 2005. AIAA 2005-5882.

5 Hörmann, H., J. Peixato, J. Robinson, T. Rager, A. Belyavin, and R.Hosman (2005). Analysis of pilot control behavior during balked landing maneuvers. AIAA Modeling and Simulation Technologies Conference, San Francisco,CA. August 15-18, 2005. AIAA 2005-5881.

6 Geest, P. van der (2009). Development of a Procedural Pilot Model for the Manual Balked Landing Maneuvre. AIAA modeling and Simulation Technologies Conference, San Francisco, August 15-19, 2005, AIAA 2009-5819.

7 Hosman, R.J.A.W., P.J. van der Geest, and H.T.H. van der Zee (2008). A control engineering pilot model describing skill-based control behaviour for the balked landing. NLR report NLR-CR-2008-764.

8 Leege, A.M.P. de, and H.J. Hörmann (2006). Modelling of Flare Initiation Altitude and Determination of Decrab Initiation Altitude (ver 1.3). Boeing Research and Technology Europe. Madrid, June 2006.

9 Yaroshevskiy, V.A., A.V. Bobylev, V.G. Nekrasov, A.N. Stepanenko, H.J. Hörmann, J. Peixoto, G. Robel and J. Robinson (2005). A Validation Plan for Pilot Models Developed for New Larger Airplanes. AIAA Modeling and Simulation Technologies Conference, San Francisco, CA. August 15-18, 2005. AIAA 2005-5883.

10 Rasmussen, J. (1983). “Skills, Rules, and Knowledge; Signals, Signs, and Symbols, and Other Distinctions in Human Performance Models”. IEEE Transactions on Systems, Man and Cybernetics, Vol. SMC-13, No. 3, May/June 1983.

11 Hosman, R.J.A.W., J. Schuring, and L.J.J.Erkelens (2004). A pilot model developed for investigation the balked landing maneuver. National Aerospace Laboratory NLR, Amsterdam. Contractory Report NLR-CR-2003-637.

12 Hosman, Ruud (1996). Pilot’s perception and control of aircraft motions. Ph. D. Thesis. Delft University of Technology. Delftse Universitaire Pers, Delft, the Netherlands

13 Hosman, Ruud and Henk Stassen (1999). Pilot’s perception in the control of aircraft motions. Control Engineering Practices. Vol. 7, No. 11, pp. 1421-1428, 1999.

14 Vaart, J.C. van der, and R.J.A.W. Hosman (1987). Compensatory tracking in disturbance tasks and target following tasks. The influence of cockpit motion on performance and control behavior. Delft University of Technology, Faculty of Aerospace Engineering, Report LR-511, November 1987.

15 Levison, W.H., and A.M.Junker (1977). A model for pilot's use of motion cues in roll-axis tracking tasks. Bolt Beranek and Newman Inc. Report No. 3528, Cambridge, Ma.

16 Hosman, R., J. Schuring, and P. van der Geest (2005). Pilot model development for the manual balked landing maneuver. AIAA Modeling and Simulation Technologies Conference. San Francisco, CA. August 15-19 2005. AIAA-2005-5884.

17 McRuer, D.T., D.Graham, E.S. Krendel, and W. Reisener (1965). Human Pilot Dynamics in Compensatory Systems. Theory, Models, and Experiments with Controlled Element and Forcing Function Variations. Wright Patterson AFB, AFFDL-TR-65-15.

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18 Clement, W.F., H.R. Jex, and D. Graham (1968). Application of system analysis theory for manual control displays to aircraft instrument landing. Forth Annual NASA University Conference on Manual Control. NASA SP-192

19 Wewerinke, P.H. (1978). Visual scene perception process involved in the manual approach. National Aerospace Laboratory (NLR), Amsterdam. NLR TR 78130 U.

20 Hess, R.A. and W. Siwakosit (2001). Assessment of flight simulator fidelity in multitaxes tasks including visual cue quality. Journal of Aircraft. Vol.38,No. 4,July-August 2001.

21 Jong de, J.N.N. and A van Lunteren (1972). Human operator remnant in a subcritical task. Proceedings of the Eighth Annual Conference on Manual Control. University of Michigan, Ann Arbor, Michigen. Airforce Flight Dymanics Laboratory, Wright Patterson AFB. Report AFFDL-TR-72-92.

22 Steurs,M., M.Mulder, and R. Van Paassen (2004). A cybernetic approach to assess flight simulator fidelity. AIAA Modeling and Simulation Technology Conference. Providence, RI, August 16-19, 2004. AIAA Paper 2004-5442.