Pilot Model Rotocraft Evaluation

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ABSTRACT

The evaluation of rotorcraft performance and handling qualities inpiloted flight using a desk-top simulation is a desirable facility. Thedevelopment of the SYCOS pilot model has brought this capabilitycloser by simulating piloted flight along prescribed trajectories suchas the Mission Task Elements of ADS-33. This development hasovercome some of the limitations of the precise control of inversesimulation, by adopting a structure which includes a correctivecomponent that adjusts the control strategy to counteract departuresfrom the desired flight path. The development and implementationof SYCOS for a range of rotorcraft types and applications is shownto be well defined and straightforward. The capability and reliabil-ity of SYCOS is demonstrated through a number of recent applica-tions in performance, control strategy and pilot workload studies.Some of the applications benefit from enhancements to the basicstructure and these are described in context. The accumulation ofexperience with the SYCOS pilot indicates that it is a significant

advance as a predictor of human control activity during helicoptermanoeuvring flight.

NOMENCLATURE

k crossover gaink operating point adjustment gainkg guide coupling constant

p,q,r body referenced angular velocity componentsu,v,w body referenced velocity components

xa, xb, control displacements: lateral cyclic stick, longitudinal

xc, xp cyclic stick, collective lever, pedal

x, u, y state, control and output vectors of linear system

y,yref output vector for linearising feedback, reference value

A, B, C state, control and output matrices of linear system

C,D matrices of linearising feedback

G,H matrices of linearising feedback for CTM

T manoeuvre time

ue, ve, we Earth referenced velocity components

X,Y,Z Earth referenced position components

, , Euler attitude angles0,1C, blade pitch angles: collective, lateral cyclic, longitudinal1Str cyclic and tail rotor collective crossover delay.

,m,g, closure time: general, manoeuvre and guide, parameters of CTM

GLOSSARY

CTM compensatory tracking model

SYCOS synthesis through constrained simulation

AFS advanced flight simulator

PIO pilot induced oscillation

AFCS automatic flight control system

THE AERONAUTICALJOURNAL NOVEMBER 2003 731

Paper No. 2802. Manuscript received 8 October 2002, revised version received 9 April 2003, accepted 23 May 2003.

Progress in the development of a versatile

pilot model for the evaluation of rotorcraftperformance, control strategy and pilot workload

R. Bradley

School of Computing and Mathematical Sciences

Glasgow Caledonian University

Glasgow, UK

G. Brindley

School of Computing

Robert Gordon UniversityAberdeen, UK


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1.0 INTRODUCTION

The SYCOS pilot model came into being in 1996 as a dynamic con-troller for helicopter simulations which could pilot a helicopterthrough prescribed manoeuvres in a manner similar to a human pilot.At the time there was considerable experience of inverse simulationof helicopter flight(1) and its ability to produce open loop controlactions which could guide a helicopter simulation through manoeu-

vres in a precise and efficient manner. There were, however, certainfeatures of a human pilot that the ideal represented by inversecontrol could not address in a realistic manner.

An important aspect was the reaction to external disturbances suchas atmospheric turbulence. Inverse simulation in its pure form calcu-lates control activity that will precisely nullify the effects of turbu-lence and, as a result, generates control activity with anunrepresentative proportion of high frequency activity. The humanpilot normally largely ignores disturbances of such high frequency reacting only to those frequencies which cause a lasting departurefrom the intended flight path.

A further requirement was an ability to respond in a realisticmanner to system constraints such as control limits which naturallycause failure of the inverse method. Modifications to the inversealgorithm can be made to carry it through regions where the basic

algorithm is challenged but an aim of the SYCOS work was torespond to disturbances and constraints in a human manner.Another motivating factor was the recognition that pure inverse sim-ulation did not capture the compromises and trade-offs which ahuman pilot makes when the task demands are escalated.

Originally, it was believed that a new design for a pilot modelwould be best carried through by a synthesis of control componentsbased on the physio-neurological behaviour of human pilots andtheir behaviour when faced with the constraints of flight-path andhelicopter systems. Hence the acronym SYCOS (SYnthesis throughCOnstrained Simulation) was coined but in the event developmenttook a different direction and a pilot model which was phenomeno-logical, based on the work of McRuer and Krendel(2), resulted.

The aim of this paper is to describe the theoretical development ofthe SYCOS model and to present a variety of examples where it hasbeen successfully implemented. These examples have been selectedto illustrate its straightforward implementation and for studiesemploying different simulation environments, for a variety of rotor-craft configurations and model formulations.

The paper begins with the development of the basic SYCOSmodel structure which consists of two components: the first consoli-dates the error between prescribed and actual flightpath and thesecond acts as the learned response of the pilot his internalisedmodel of vehicle behaviour. It is followed by practical examples ofimplementations of the basic model. Firstly, its fundamental abilityto fly a prescribed manoeuvre is demonstrated, followed by an illus-tration of the models capacity to disregard high frequency externaldisturbances. Results comparing a human pilot and the SYCOSmodel encountering turbulence near a helideck illustrate that thislatter aim has been achieved. Next, it is shown that it is straightfor-ward to apply the SYCOS methodology to novel configurations byflying a tilt rotor simulation through a prescribed manoeuvre andadditionally, a demonstration that even a helicopter model incorpo-rating a modern, decoupling, control system succumbs to a routineapplication of the method. Finally, to emphasise the versatility ofSYCOS, its application to recent advances in control strategy basedon optical variables specifically optical tau, (), is formulated asa recasting of the routine SYCOS variables.

In these examples a variety of rotorcraft simulation models fromdifferent studies have been employed in order to demonstrate theversatility of the SYCOS technique and that minimal access to theinternal variables of the models is required in order to apply it. Thatis, the pilot model can be simply wrapped round even a complexsimulation model.

The paper continues by describing extensions to the basicSYCOS model which have been developed to cater for specificstudies. The first enhancement allows trajectories to be definedexplicitly in terms of vehicle position and orientation. This modifi-cation has been used to successfully simulate the recovery of a heli-copter to a ship under the effects of ship airwake and turbulence anda further enhancement which improves the realism of the controlactivity is also described. Finally, a further modification to themodel is described which can cater for changes in wind directionand speed and which replicates the corresponding human pilotcontrol activity to a certain degree.

The paper concludes by summarising some known limitations ofthe model and future work to ameliorate these limitations issuggested.

2.0 THE BASIC SYCOS PILOT MODELThe principal difference between the SYCOS model and inversesimulation lies in its configuration as a corrective system. That is,instead of the open loop structure where the control actions are gen-erated directly from a set of reference output values, they are calcu-lated from the difference between the current system outputs andtheir reference values. This structure is shown in Fig. 1.

McRuer and Krendel in their definitive study of human pilotbehaviour(2) provided evidence that in such a structure the humanpilot adapts to the system being controlled in a particular way. Aconclusion of their study is that pilots (human controllers) adapttheir behaviour so that the transfer function, T(s), between error andoutput is:

where kis the loop gain and is a delay with typical values of 2 and02 respectively. This adaptation is valuable because it enables adesign for a pilot model to be based on the net result or outcome ofthe pilots strategy rather than the detailed internal dynamic func-tioning. The result is that the model contains only two parameters, kand , and these are easily comprehended. In this form, the model iscalled the crossover model because at low frequency the outputtracks the reference whereas high frequency changes in reference arereflected in the system output in only an attenuated form. For a zerovalue of we can write:

or

a transfer function with a break or crossover frequency equal to thegain k. This is the behaviour that is assigned to the adapted controlof a human pilot. The principal focus of the original work was singleinput/single output systems so that for helicopter applications it isnecessary to consider four axes of control with reference and controlvectors, but the principle, that of an assumed adaptation to thesystem being controlled, is taken as the basis of the SYCOS model.It is therefore relatively sparse in tuneable quantities and containsonly overall gain and delay parameters.

The next step is to determine an implementation which gives therequired overall transfer function. One solution is the simple structureconsisting of a crossover component and an inverse shown in Fig. 2.

732 T H E A E R O N A U T IC A L JO U R N A L N O V E M B E R2003

Figure 1. The corrective pilot model.

( )ske

T s

s

=

( )ref

ky y y

s =

ref

y k=y s + k

. . . (1)

PilotHelicopterDynamics

OutputFunction

+

reference error control state output


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The inverse component combines with the system to form anidentity so that overall the behaviour is described by the trackingmodel above Equation (1). In fact, the formal inversion of dynamicalsystems does not always result in an exact inverse, as will be dis-cussed below, and, in any case, the inversion of a complex model isnot a trivial exercise computationally and may be intractable analyti-cally. Nevertheless the form above has been successfully used withthe VSH (very simple helicopter) model where an analytical inverse

can be found(3). In a wide range of applications the need for an exactinverse has been found unnecessary and moreover since the inverse,from a design point of view, represents the learning or adaptation ofthe pilot to the response of the system output to control inputs, it isnot expected that exact adaptation takes place. Therefore for tworeasons; (i) computational simplicity and (ii) pilot modelling, anapproximate inverse is an acceptable substitute which has provedsuccessful in man applications. The nature of the most suitableapproximation has not yet been fully explored and there are optionsfor retaining some of the non-linearity but for the applications dis-cussed here a linear inverse has been employed: specifically, theinverse of a six DOF linearisation of the helicopter being piloted.

The derivation is straightforward. From a linearisation of the heli-copter in the usual form:

x = Ax + Bu

with state and control vectors x = (u, v, w, p, q, r, , , ) andu = (0,1C,C,0tr,) respectively and output equation

y = Cx

The inverse is obtained by linearising feedback. (Alternatively, thecontrol vector may be expressed in inceptor positions:u = (xa,xb,xc,xp)). Some important issues can be explained by describ-ing a specific case so consider the common situation where amanoeuvre is defined by prescribing the three earth referenced com-ponents of velocity and the heading angle. The output vector is then

y = (ue, ve, we, )

and the development of the feedback to invert the original systemwith respect to the outputy begins by differentiating the output equa-tion to get

y = Cx + CAx + CBu

In this case CB is singular. The last row of CB is identically zeroreflecting the fact that the rate of change of heading is not directlyinfluenced by the controls. The product CB is rank three because thecontrols, however, do directly influence the body referenced acceler-ations. Differentiating again gives

y = CA2x + CABu + CBu

and the last equation, which is for the heading acceleration, , doesnot involve u since it is known that the last row of CB is zero. It istherefore possible to write

where:

and

The matrixDis non singular so that when u is found from

it is this feedback which guarantees that the output y is equal to itsreference values.

The use of side-slip angle rather than the heading angle presents aninteresting special case for the linearising feedback approach. It is dis-cussed in Ref. 4 and, once established, is only a slight complication ofthe basic method.

In the SYCOS structure this inversion must be applied to theoutput from the crossover element rather than the vector of refer-ences. The resulting structure is shown in Fig. 3(a) where the diago-nal componentL(s) contains the differentiations to generate y from y.Combining L(s) with the integration from the crossover componentsimplifies Fig. 3(a) to the structure in Fig. 3(b) where only theheading component needs to be differentiated. In fact, if the output iscast in terms of the rate of change of heading from the outset pos-sible since it can be expressed solely in terms of the state vector (thatis, not involving the control vector) then no differentiations are

required at all. The structure in Fig. 3 is termed the FCCM version ofSYCOS the fully compensating crossover model and it is driven byreferences consisting of velocities and rate of heading angle.

3.0 APPLICATIONS OF THE BASIC SYCOSMODEL

To demonstrate the effectiveness of the SYCOS model we first con-sider an acceleration-deceleration manoeuvre a longitudinal hoverto hover translation(5) flown by a Lynx helicopter simulated in theAdvanced Rotorcraft Technologys Flightlab environment. The sim-ulation model is state of the art for piloted simulation with fully non-linear dynamics and a rotor system with individual aeroelasticblades. The model is parameterised in Imperial/US units since the

Flightlab environment was developed in the USA. The manoeuvre iscarried out over 545ft and takes 28sec with a maximum velocity of40ft/sec. The velocity references Ve, We and the heading are set tozero and the longitudinal motion is defined by a profile for thevelocity ue which increases smoothly from hover through an intervalof constant acceleration and then decreases through a similar intervalof deceleration and return to hover.

The resulting response is shown in Fig. 4 with the crossover para-meters k= 2 and = 02. TheX-axis velocity and displacement areseen to track the reference values in the expected manner with adelay of 05sec, while the remaining references hold the zero valuesreasonably well. The control activity relative to trim, expressed as afraction of maximum displacement, is shown in Fig. 5 and the coor-dinated actions necessary to fly the manoeuvre are clear. The mainactivity is in the longitudinal cyclic but the collective is dropped in

B R A D L E Y A N D B R IN D L EY PR O G R E SS IN T H E D E V E L O P M E N T O F A V E R S A T IL E P IL O T M O D E L F O R T H E E V A L U A T IO N O F R O T O R C R A F T ... 733

Figure 2. The basic SYCOS structure.

Figure 3. (a) The SYCOS fully compensating crossover model,(b) Simplified implementation.

..

..

.

.

.

.

.

y = Cx + Du

( , , , ) 'u v w= e e ey

-1= ( - x)

refu D y C

i j i j

2

i j i j

i 1 3 j 1 4 i 1 3 j 1 4

i 4 j 1 4 i 4 j 1 4

= = = = = = = = = =

, ,

, ,

(CA) , ..., ; , ..., (CB) , ..., ; , ...,C D

(CA ) ; , ..., (CAB) ; , ...,i,j i,j


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the middle of the manoeuvre separating the accelerating and deceler-ating phases in order to maintain a set altitude as the vehicle rotatesthrough its vertical position.

The second example of the basic SYCOS is of a helicopter config-ured again in the Flightlab environment to resemble a helicopter ofthe S76 type. Turbulence from wind tunnel measurements of flowaround an offshore platform is scaled and applied to the simulation.The pilot model is therefore required to hold station and correct

departures from the zero velocity reference datum. Fig. 6 shows thecollective lever responses as a fraction of maximum displacement ofboth the SYCOS pilot and a corresponding piloted simulation (AFSPilot) for 25sec of hovering flight 33ft above the centre of thehelideck in a wind of 35kt from the direction of the exhaust stacks.Simulations were carried out for wind speeds in the range 15-60ktfor wind directions that were both unobstructed and from the direc-tion of obstructions such as derricks, exhaust stacks, and cranes. In acomprehensive study(6), it was demonstrated that the workload pre-dicted by the SYCOS pilot was in the range of values typical ofhuman pilots. The tendency for the human pilot to ignore high fre-quency disturbances is clearly shown in Fig. 6. The basic SYCOSpilot, however, retains some of the high frequency component. Itwill be seen that the introduction of non-linear elements into theSYCOS model, which is described in the following section, pro-

duces a more authentic pilot response.The development above for a conventional helicopter is easily

extended to the tilt rotor configuration where in helicopter modethere are five controls:

1. Combined collective pitch,

2. Combined longitudinal cyclic pitch,

3. Combined lateral cyclic pitch,

4. Differential collective pitch,

5. Differential longitudinal cyclic pitch.

It is possible with this combination to satisfy five reference outputsand one of interest additional to those discussed earlier is the main-taining of a specified bank angle, . The development followssimilar lines to that of the heading angle so that the rate of change of is ultimately specified. If the output vector is established as

y = (ue, ve, we, , )then the same procedure leads to the linearising feedback

where

The adaptation to the new configuration is quite straightforward andFig. 7 shows the control activity and vehicle attitude during a speedburst manoeuvre an acceleration-deceleration from a 30kt trim oflow aggression for a fully non-linear, individual blade model of anXV-15 tilt rotor(7) in helicopter mode. In this case a FORTRANmodel is imported into a Matlab environment where the linearisa-tion and model reduction for the SYCOS formulation together with

the final simulation take place.A further illustration of the capability of the SYCOS formulation

is the control of a helicopter simulation in the Matlab/Simulink

environment which incorporates a Partial Authority Flight ControlAugmentation (PAFCA)(8). This makes use of the series actuator toimplement a model following controller via the AFCS limitedauthority series actuator applied to a simulation of a battlefield heli-copter similar to a Lynx. A simplified form of the PAFCA structureis shown in Fig. 8. The model is designed to implement a decoupledrate control system so that fore-aft and collective inceptor move-ments alone are required for an accelerationdeceleration manoeu-vre. The compensating inputs are applied through the series actuatoras shown.

The derivation of the SYCOS model is straightforward even inthis tightly controlled architecture with the design model,


Figure 4. Output response of FCCM during acceleration-decelerationmanoeuvre.

Figure 5. Control activity produced by FCCM during acceleration-deceleration manoeuvre.

Figure 6. Collective lever activity hovering in turbulence (S76 type).

-1

refu = D (y -Cx)

2

1 3 1 5 1 3 1 5

4 5 1 5 4 5 1 5

i,j i,j

i,j i,j

i,j i,j

i j i j

i j ) i j

(CA) , ..., ; , ..., (CB) , ..., ; , ...,C , D

(CA ) , ; , ..., (CAB , ; , ...,

= = = = = = = = = =


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Figure 7. Response of a XV-15 tilt rotor simulation during a speed-burst manoeuvre from 30kt to 50kt.

Figure 8. Structure of the PAFCA system.

Figure 9. Inceptor inputs to the PAFCA system in side step (50%authority).

Figure 10. Series actuator inputs to the PAFCA system in side step(50% authority).

Figure 11. Inceptor inputs to the PAFCA system in side step (25%authority).

Figure 12. Series actuator inputs to the PAFCA system in side step(25% authority).


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controller dynamics, and helicopter model all included in the model

reduction procedure. Figures 9 and 10 show results for an accelera-

tion-deceleration manoeuvre for a hover to hover time of tenseconds and a peak velocity of 33ft/s. In Fig. 9 the series actuator

limits are set to 50% and there is no saturation during the manoeu-vre. The SYCOS control displacements are completely decoupled

with no pedal or right/left inceptor activity required. The compen-

sating inputs of the series actuator are also shown in Fig. 10. InFigs. 11 and 12 the series actuator limits are set to 25% and the

extra coordinated inputs of the SYCOS pilot are evident as theseries actuator saturates. The SYCOS approach successfully pro-

duces a pilot model with which to assess the performance of the

controller in defined tasks.A final illustration shows how a new control strategy can be

recast in a form which allows the SYCOS approach to be followed.In a significant contribution to understanding how human pilots use

visual cues to perform the principal tasks of manoeuvring flight,

Padfield et al(9) espoused the pioneering work of Lee(10) to investi-gate the role of the time to closure variable, optical .

For example, when a helicopter is travelling at speed v towards astopping point a distance d away then the time to close is given by

= d/v

It is found that pilots control their deceleration by maintaining aconstant value of rate of change of typically in the range 04 to05. The value 05 results in a steady deceleration to stop whilevalues either side result in a hard (< 0 5) or soft stop (> 05).These concepts have been applied to various manoeuvres including

acceleration-decelerations, side steps, and pull ups. Alongside theinvestigations involving piloted simulation, work has been carried

out to incorporate this human-like control strategy into the SYCOSframework. Given a -based strategy that is, knowing as a func-tion of time the closure relationship can be recast as:

v = d/.

and the evaluated v employed as reference values for the FCCM in

the normal way.

Figure 13 shows typical responses from a 1,100ft accel-decelmanoeuvre flown by a SYCOS piloted UH60 Flightlab simulationbased solely on this strategy with the time to closure m of themanoeuvre determined from pilots internally generated guide g by

m = kg gwhere g is hypothesised to be:

where T is the total manoeuvre time, in this case 29sec. Thisapproach is potentially important because it encapsulates in a single

parameter kg a whole manoeuvre strategy and a detailed specificationof a velocity profile is not required.

Some appropriate trends in the control strategy can be identified,

for example the sharp pull up at the end of the manoeuvre, but againit may be noted that the detail of the control activity is not what

would be expected from a human pilot. An example of the stickactivity of a human pilot flying a similar acceleration-deceleration

manoeuvre on the flight simulation facility at the University of

Liverpool(9) is shown in Fig. 14. Over the greater part of themanoeuvre the coupling corresponds to value of kg 05 obtained byfitting an appropriate segment of the simulation data to the profileabove Equation (2). The general trends of the control activity are

well captured by the SYCOS simulation but the start and finish

behaviour is not. This discrepancy is to be expected at the start of themanoeuvre where the human pilot has to first pitch the helicopter

into the acceleration demanded by the guide profile but at the veryfinish of the manoeuvre it appears that the pilot eases off to achieve

a soft stop.


2 2

g2

T t

t

=

. . . (2)

Figure 13. SYCOS responses from a tau-guided accelerationdeceleration kg = 0.35-0.5.

Figure 14. Human pilot control activity for acceleration decelerationmanoeuvre.

Figure 15. The structure of the CTM ompensating tracking model).

Figure16. The SYCOS super-pilot structure.


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4.0 ENHANCEMENTS TO THE BASICSYCOSMODEL

While velocities are acceptable variables in which to define manymanoeuvres, in some applications it is necessary to use actual dis-placements as references. This can be achieved in several ways. Onethat has been used in a number of studies replaces the output vectorabove with

y = (X, Y, Z, )where the state vector is x = (X, Y, Z, u, v, w, p, q, r, , , ) . Thecorresponding output equation has a simple form since its elementsare zero or one for the specified output. The product CB is zero sothat in this case

C = CA2, D = CABand now put

Hyref = + Gy + Hy

where H = diag(2) and G = diag(2) so that the reference vectoris tracked by the output in the manner of a damped second ordersystem with natural frequency and damping factor . The feedbackfor inversion and tracking is simply obtained from

Hyref= Cx + Du + GCAx + HCxas

u = D

-1

(Hyref (C + GCA + HC)x)The structure of the SYCOS model with this formulation is shown inFig. 15 and is called the CTM (Compensating tracking model). It hasbeen used successfully to investigate the simulation of deck traversesin the presence of airwake and turbulence in a study of the Heli/Shipdynamic interface(11). There are additional parameters in the CTMmodel, the damping and the frequency , for which values of 04and 5 respectively have been used in various studies. This componentof the structure and the values chosen for its parameters have not yetbeen validated against human pilot control activity but without posi-tion auto-guidance, the pilot is faced with holding a position refer-ence essentially by controlling acceleration so some strategy must beadopted. It should also be noted that each reference channel can betuned separately by selecting different values for and .

The CTM structure modifies the basic stability properties of the

SYCOS model. The tracking dynamics leading to Equation (1) arereplaced by:

The criterion for stability of these dynamics is determined fromRouth-Hurwitz theory to be

In the case of a simulation involving a non-linear helicopter model andatmospheric turbulence this stability boundary is a useful, but notdefinitive guide.

A further improvement of the basic model addresses the effect of exter-nal influences. Long-lasting gusts, or entry into a different wind condition,are ameliorated but not eliminated by the CTM version of the SYCOSpilot. The effect is reduced in proportion to the loop gain kbut the human

pilot would act to offset the changing trim position even to the extent ofre-trimming the aircraft. It has been possible to emulate this behaviour to acertain extent by introducing a feedback loop which senses persistentchanges in the operating conditions of the aircraft. The combination of air-craft/inverse should result in an identity relationship and departure fromthis can be sensed and corrective actions introduced. This is illustrated inFig. 16 wherekis the gain associated with this loop. It has successfullyprovided control offsets to back off the effects of changing wind condi-tions and as a result of the optimism introduced by being able to deal withsuch situations it was dubbed the SYCOS super-pilot.

This backing off is illustrated in Fig. 17 where a Lynx helicopter,hovering in still air, is subjected after 2sec, to a 10kt wind from thelateral (green 90) direction. The adaptation of the control positions tothese new flight conditions can be seen. The value of needs to reflectboth the pilots perception of a persistent change and the time scale of

the adjustment to correct for it. In this example a time scale of 10sec(k= 01) has been employed. Similar results for a 10kt wind fromahead are shown in Fig. 18.

Limitations in the authenticity of the predicted control activity led tothe inclusion of additional components in the SYCOS structure whichattempted to capture specific human pilot characteristics. The basicstructure was enhanced by (i) a dead zone to emulate uncertainly indetecting the error between outputs and reference values and ( ii) hys-teresis to capture hesitation in moving the controls. The latter effectcould be observed on traces of collective lever and pedal control activ-ity in piloted simulation. The enhanced structure is depicted in Fig. 19.

Figure 20 shows sample results from the use of this enhanced structurefor a traverse of a Westland Lynx across a deck in the presence of turbu-lence and airwake(11). It can be seen that the collective responses, and to alesser extent, the pedal activity give a realistic comparison with thecontrol activity of a human pilot in the AFS (advanced flight simulator)facility at QinetiQ Bedford. The cyclic stick activity is not so good: itdoes not capture the higher level of activity of the human pilot who


2k<

.

Figure 17. Super-pilot responses to green 90 instantaneous 10kt wind.

Figure 18. Super-pilot responses to instantaneous 10kt wind from ahead.

2 2 2 2( ( 2 ))refk y k s s s y = + + +

Figure 19. The SYCOS pilot with non-linear elements.


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appears to have components of almost spontaneous activity.The absence of the full range of detail of pilot activity makes the pre-

dicted responses deficient in the components which would allow the suc-cessful application of wavelet-based workload metrics (11).

Reference 4 contains an analysis of the inverse component of theSYCOS model. It derives eigenvalues associated with the zero dynamics;they correspond to lightly damped oscillations in both lateral and longitu-dinal axes. This behaviour is typical of all types of conventional helicopter

investigated so far (The tilt rotor directly controls the roll angle in heli-copter mode so only the longitudinal oscillations are present). There maybe more or less damping or even encroachment into unstable positivevalues but the oscillatory behaviour appears to be generic. Earlierwork(10) has associated this behaviour with tightly controlled piloting strat-egy which, as the workload increases, gives rise to a pilot induced oscilla-tion (PIO). While there may be some debate about this being the source ofPIO, the SYCOS model does include an inverse components which canintroduce an oscillatory element into the predicted control actions. Theseare engendered by external disturbances or lack of smoothness in the sup-plied references and it is recognised that in situations of low workloadthey can be unrepresentative of piloted activity. It is believed that thehuman pilot would not allow such behaviour to develop unless his atten-tion was directed to more important activities. He would, it is believed,relax the performance requirements of the manoeuvre in order to subdue

the unpleasant oscillations. Two ways of addressing this behaviour in thecontext of the SYCOS model have been considered. The first is to usepole placement techniques to calculate an appropriate feedback for theinverse model. The second is to amend the references by including anelement of the associated attitude angle. This has the effect of introducinga damping of the oscillations in a way that has a physical interpretation.The pole placement method essentially carries out a similar type of feed-back but from a control design standpoint. To date, it is accepted that thesedevices, are not validated and are simply a fix to attempt to include moreauthenticity into the responses from SYCOS. What it does do is illustratethat the pilot has a wider piloting task than simply following referencesand that the basic SYCOS model has limitations in that respect.

5.0 CONCLUSIONS AND FUTURE WORK

This paper has brought together, and illustrated, the SYCOS pilot modeland some recent developments. Its achievements may be listed as:

(i) It is effective at piloting rotorcraft through prescribed manoeuvres.(ii) As required, it corrects for atmospheric turbulence and similar dis-

turbances.(iii) It can compensate for changes in wind speed and direction.(iv) It is flexible in that the procedure can be applied to a variety of rotor-

craft configurations even with AFCS included.(v) It can replicate some of the features of human pilot control activity.

On the other hand there are certain limitations that are clear and shouldbe targeted for immediate future work; here we identify three.

The first limitation is in the authenticity of the control activity. Humanpilots are not comparable in the detail of their piloting technique soprecise replication of time responses are inappropriate. Rather, the aim is

to be able to produce control responses that give statistics for workloadmetrics that are indistinguishable from a human pilot. The human pilottrades off guidance and stabilisation tasks during a manoeuvre thistrade off may need to be explicitly included in the SYCOS model.

The second limitation is the nature of the approximate inverse in theSYCOS formulation. At present the pilots learning is represented by aninverse of a six DOF linearisation of the helicopter dynamics about a ref-erence flight condition. While this approach has been adequate for theapplications so far considered, it is likely that the human pilot perceives amodel of a simpler structure but one which adapts to a wider range offlight conditions.

Finally, the SYCOS model, being essentially corrective, does notanticipate future demands. For some external disturbances, for exampledue to turbulence, this is credible since they are random in nature. Fortracking a flight path reference, however, it is unlikely to be valid since

the pilot is fully aware of where he needs to be in the immediate future.There is scope therefore for including anticipation, in the form of a lead

component, on the flight path references but not onto the vehicle outputs.

ACKNOWLEDGEMENTSThe authors wish to acknowledge the following organisations for theirsponsorship of various aspects of the work described in this paper: BMTFluid Dynamics, the Civil Aviation Authority, QinetiQ (previously theDefence Evaluation and Research Agency) and Flight Stability andControl. The -guidance and heli/ship dynamic interface studies werefunded by the chemical and biological defence and human sciences, andenergy, guidance and control domains of the MoD corporate researchprogramme respectively.

REFERENCES1. THOMSON, D.G. & BRADLEY, R. The development and verification of an

algorithm for helicopter inverse simulation. Vertica, May 1990, 14, (2).2. MCRUER, D.T. and KRENDEL, E.S. Mathematical models of human pilot

behaviour. AGARD-AG-188, January 1974.3. BRADLEY, R. The flying brick exposed: non linear control of a basic heli-

copter model. Dept Mathematics, Glasgow Caledonian University,Technical Report, TR/MAT/RB/96-51, 1996.

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the maritime environment, 2001.7. MCVICAR, J.S.G. and BRADLEY, R.A. Generic tilt-rotor simulation model

with parallel implementation and partial periodic trim algorithm. 18thEuropean rotorcraft forum, 1992, Avignon, France.

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Figure 20. Predicted and human pilot control activity for a traverse inturbulence and airwake.

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Pilot Model Rotocraft Evaluation