Abstract

KNOWLEDGE REPRESENTATlON FOR INSTRUCTORS AIDS IN CIVIL FLIGHT SIMULATORS

Y.&C. ROBERTS

Rddlffu8h Simulation Ltd.

The aim of the study is to investig- the suitability of fuzzy sets and rules for monitoring and assessing flight and pilot control during a simulator exercise. through the intelligent interpretation of insmment reedings.The study concentrates on instrument landins s y s m approaches as used by civil pibts. The sets and ndes that haw been developed represent the kmwidge mquked to predii critical dluations, to reoognise the intention to correct for these situations and also to desaibe the aircraft handling. A software demonstration system runs at comparable speed to real life approaches, and contains descriptions and predicrions displayed linguisticaay. The applicability of a aitiquing technique with fuzzy reasoning is investigated. This technique requires the specifiition of the limits outside which acceptable performance is unlikely to be achieved,. thus simplifying knowledge acquisition. It is conduded that the fuzzy reasoning and critiquing techniques used are suitable for achieving the aims.

Rediffusion Simulation Ltd. (RSL) has just completed three studiis and demonstrations conceming the use of intelligent knowledge based systems as instructors' aids in flight simulators [Alvey 19881. The first two apply to route flying and ground controlled approach (GCA), typically used by military pilots. and the third. the subject of this paper, applies to instrument landing approaches (11s) as used by a w l pilots.

One aim of the study is to investigate the suitability of fuzzy sets and rules for monitoring and assessing flight and pilot control during a simulator exercise, through the intelligent interpetation of insmment readings. The sets and rules that have been developed repredent the knowledge required to predii critical situations. to recognize the intention to correct for these situations and also to describe the aircraft handling. The outcome of the monitoring is to be presented in an easily assimilated form in order to provide an 'intelligent decision support' system to aid the mstnrctor during his assessment of a pilot in a training session for 11s approaches. This paper draws extensively from an RSL internal report by

J.A.Sime [RSL 1988) which describes the basic system Concept.

For an initial assessment the intelligent decision support s y s m is interfaced to a demonstration aircraft-pilot simulation, monitoring its instruments and reporting as required by an instructor. The demonstration involved the design and implementation of a combined aircraft-pilot simulation for a small passenger jet , in which a nominal mathematical model of the airaaft was combined with a knowledge based representation of a pilot. The pilot was simulated through a set of production rules which mimic a real pilors decision making processes.

An instructor assesses a pilot on the basis of a variety of information obtained from: instrument readings, observation of the pilot's behaviour, out of the window observation, auditory information and simulator data ( by observing, for example, the graphical representation of the flight path on the insbuctds console).

However, the intelligent decision support system is limited to the monitoring of instrument readngs and simulator data; thus, it cannot make a full assessment of the pilot's performance. Its primary aim is to reduce the instructofs w d o a d by monitoring the aircraft handling; this frees the instructor to concentrate more on the other aspects of the assessment detailed above.

The intelligent decision support system requirement is intentionally limited by WO factors. It will only provide support for the instructots decision, rather than actually making the decision, thus reponsibility still lies firmly in the hands of the instructor. Also, it will not make performance assessments and therefore it could be ignored if there were just cause. The system provides expert criticism only when deemed necessary.

Another aim of the study is to investigate the applicability of a critiquing approach with fuzzy reasoning to the design of an intelligent decision support system to aid instructors in the monitoring of aircraft handling during ILS approaches. Such a system should notify instructors of the existence of a critical condition, rather than constantly providing information on pilot performance. An ILS approach is only one of a number of manoeuvres carried out in the simulator, but it provides a simple exercise from which the feasibility of a complete

1061

CH2759-9/89/0000-1061 $1 .OO 0 1989 IEEE

system for flight monitoring can be investigated.

2 Svstem Reau' irements and Tech niuuah

Historically in a typical automated performance measurement program, the computer monitws a number of parameters such as heading, altitude and speed, to see that the flight meets the criteria for satisfmory perkmance. The student attempts to maintain a certain value for a parameter, known as the reference value. and any performance outside this tolerance is called a deviation. Two types of display are provided for the instructor: a graphical display showing the actual flight path and the desired flight path, and a linguistic display providing a desaiption of the leg, the reference values and the relevant tolerances - possibly with some remarks. A summary is provided that lists the total number of deviations. the maximum deviations and the armulative time out of tolerance. A MXXB for the flight may also be presented, based either on simple percentages or on more complex statistical measures [D ick" 1982,19841.

The graphical display and linguistic display are also requirements of this project However with the concept used in this project, it is unnecessary to amtinually compare the state of the aircraft with ideal references. Generally the criteria for assessing the approach-tdand 811) that at decision height, the aircraft is heading in the right direction to land on the runway, is travelling at the right speed, and is in the right configuration to land with full flaps and gear down and that this is all achieved whilst ensuring high standards of comfort and safety for the passengers. The problem has been changed by tuming it around; instead of specifying what state the aircraft should be in at each stage of the flight, we sp8cify when the aircraft has deviated to such an extent that a normal approach is unlikely. This changes the nature of the knowledge acquisition to being a task of determining the limik beyond which failure lies. instead of determining the many variations of state that are acceptable in aircraft flight.

In this stue we will concentrate on 11s approaches where the ILS provides an approach path for the alignment and descent of an airaaft on final approach to the runway. As the pilot f l i s the approach he must aim to achieve three objectives. He must reduce the speed of the aircraft to the appropriate landing speed for the aircraft weight and weather conditions. He must put the landing gear down and complete a series of flap changes, so that the aircraft is in its final mfiguration before descent. Lastly. on the final approach he must maintain the airaaft heading and the W i s e r position and descend down the glideslope, so that he reaches the runway to land on the touchdown area at the beginning of the runway. During these manoeuvres he must maintain communication with ATC and complete the landing check list.

The requirements of the system are that it should provide support to the insbuctw in the monitoring of flight parameters and the assessment of a i m f t handling. It was proposed that this should be achieved through the development of an 'expert critic', a system which provides 'over the shoulder'

advice and aitiasm in an expert manner, using IKBS (intelligent knowledge base system) techniques.

The knowledge for the system was acquired in a number of ways: from text books, from the RSL test pilot, and from a number of civil airline instructors. The final knowledge base is a fusion of the knowledge obtained by all three methods. Knowledge from the i n s t r u m was obtained from a total of ten experts, all of whom are instructors for civil airlines. The use of multiple experts is different from the approach often taken in the construction of expert systems; it was hoped that using a number of instructors would help to resolve differences between individual standards of assessment.

Based on recommendations of earlier studies which looked at knowledge acquisition techniques [Heaton,1986], it was deaded to use a combination of knowledge elicitation tutorials and direct observation to determine instructors' assessment rules.

The system, based on this knowledge, should provide interpretation of the basic simulator parameters and present it to the instructor in a concise, easily understandable manner. This esentialty means that the information should be presented in a linguistic form. There are three types of interpreted information to be presented: descriptions of the flight, repotting and prediction of aitical events, and a final description of the flight handling. A qualitative description of the flight of the aircraft should be produced in a linguistic manner as the changes occur, for example, describing that the aircraft has begun to descend on the final approach, or that the aircraft has reached decision height. It should also report and prediit the critical areas of the flight, for example, when a critical event occurs such as the aircraft approaching stall, or when a measure reaches a critical limit, such as when the deviation from heading has become great and the aircraft is close to decision height. At the end of each approach, a final desaiption of the flight should be given, which should involve looking at the long-term measures derived from the parameters throughout the course of the approach.

Information should only be reported to the instructor if it is critical; this means that information relating to acceptable perfonnance is not required to be reported. If only critical events are reported, the instructor will know quickly that something needs attention, so that the time and effort necessary for determining the importance of a message is minimised. It must be remembered that the whole point of the system is to reduce instructor workload.

It was decided to use fuzzy reasoning in the system in an attempt to mimic human reasoning, which is by no means predse. There is always a margin of indecisiveness between 'absolutely yes' and 'absolutely no' in the decisions we make every day. Fuzzy reasoning attempts to capture this and makes use of the degrees of 'maybe yes' and 'maybe no' in the reasoning process. Fuzziness exists both in the data received from the instruments and also in the rules used by the instructors. For example, they are quite tolerant of minor infringements so long as these are quickly corrected. They are also aware of the inaccuracies of the instruments and adjust their assessments accordingly.

1062

To summarise, the use of fuzzy reasoning means that measures derived from parameters will be known with a certain degree of confidence. In addition, the use of fuzzy algebra allows fuzzy measures to be combined [Sum and Towill, 19851.

As there are large dfferences in the standard procedures and hence in the assessments made by each airline, an assessment system for the simulator would need to be not only aircraft specific but also flexible enough to be modified to suit each airline’s requkements. In addiion the rules mwld need to be easily modifieble in order to cope with periodic changes made by the airlines. For demonstration purposes, a generic set of NI- has been devised to illustrate the potential of the MI version. There are two types of rules: phase rules which determine and describe the stage of flight, and situation assessment rules which continually monitor the state of the aircraft for critical events and, where possible, make predictions on future flight

51 Phase Rules

The main purpose of phase rules is to partition the knowledge base into d o n s of manageable size. This means that at each phase or stage of flight only a portion of the knowledge base is examined, which speeds up the running of the system by not doing unnecessary processing.

The instructors naturally described the flight of the aircraft in terms of phases of flight. A phase represents the state of the aircraft in terms of attitude, configuration (flap, gear), and position relative to the runway. There are many correct ways of approaching the a m a y for landing and it would be diffiatlt to enumerate them all. Hawever all aircraft must go through the same pattem of reduang speed, changing to landing configuration and din ing with the localiser and glideslope guidance. The exact positions at which changes occw may vary, as may the speed of changes, but these are not generally important factors.

The object of lhese rules is !o identify and prediit critical parts of the flight during Um exerdse, enabling the instructor to focus on these stage6 during debrief. The situation assessment ~ l e s am divided into hno types: rules for continual measures which are derived from parameters, and rules for state measures.

The rules for continual measures look at the raw values of parameters generated by the plane-pilot simulation (in the demonstrated case) and analyse them into short term and long term measures. Long term measures are weighted means calculated over, for example, 20 or 60 iterations of the model. The weighting is such that more importance is given to more recent information. Continual measures which exceed a predefined limit are deemed aitical and reported to

the insbuctor. Measures are not reported if they are below this limit.

Rules for state measures examine continual measures, other state measures or both. For example, state rules exist for determining the status of the localiser (ie. whether it is alive, captured or established). These rules look primarily at continual measures, whereas a rule for missed approach prediction is based upon continual measures, such as deviation in rate of descent. and other state rules such as localiser stabs. Changes of state such as capturing the localiser or approaching decision height are reported to the instructor and are referenced in time.

The set of assessment rules devised comprises of a mixture of crisp (i.e. discrete answers) and fuzzy rules. An example of a crisp rule would be the status of the localiser. ie. whether it is alive, captured or established. An example of a fuzzy rule would be an assessment of the control of speed: ie. speed is )ow i f it is less than 1.3 times stalling speed (1.3~~).

The fuzzy set representing low speed is shown in the Appendix 1.

Fuzzy reasoning was implemented through the use of triangular or trapezoidal distributions about a value. Each distribution is constructed from knowledge of its nominal value, and the uncertainty in this value. For example, if speed is known to within three knots then a triangular distribution of width six knots about the estimated speed is used. This is shown in the Appendix 1.

The confidence level of a simple proposition such as the speed is low is represented by the intersection of the two distributions, shown in Appendix 1, which gives a single value between 0 and 1, where 1 represents a high level of confidence and 0 a low level. Complicated propositions are resolved by taking the maximum confidence level from the set of the simple critical propositions involved. For example, the confidence level for not maintaining approach and descent. once established, is a complicated proposition based upon six simple propositions of criticality involving, for example, localiser deviation, glide slope deviation, descent rate, heading, airspeed and energy; the confidence level for each simple proposition is evaluated, and the maximum value selected, resulting in the confidence level for not maintaining approach and descent. As the instructor is only interested in the criticality of the proposition, this method is sufficiint to show the presence of at least one critical component. The rules used for the ILS approach are outlined in Appendix 2.

Some rules involving speed, rate of descent and rate of turn indude an intention proposition so that intention to correct is sensed. Eg. i f the airspeed is critical and significant corrective action is sensed then the criticality measure is significantly reduced.

The concept of criticality is introduced via the value of the

1063

confidence level associated with a given fuzzy le; the state of the aircraft is interpreted as going critical if the confidence level goes over 0.6 in a rule where 1 .O i nd i tes a fail point

The Intelligent Dedsion Support System is made up of 3 components:

*TheKnowledgeBase The Inference Engine The User Interface

4.1 The Kncrwledae

In the Knowledge Base there are two classes of rules , as described in section 3: the phase rules and the situation assessment des. comprising the continual assessment rules and the state rules. Each lype of rule is coded into the program as a clause which succBBdb if the conjunction of its conditions succeed.

For example,

h e bcaiiser is flagged if the aircraft is less han 25 nm kom the runway hmshdd, and within 10 w m e s of ihe localiser hading and within 30 degrees of h e runway headng.

The phase NI~S save two purposes: they describe the phase of the flight that the program thinks the pilot is flying through and they act as a means of partitioning the knowledge base. This cuts down on the processing time necessary beween each iteration and the demonstration runs at a comparable speed to a real-life approach.

The state and mtinual measure rules are selectively fired, depending on theii importance b the phase of flight. This is a representation of the instructor's selective scanning of the instnnnents; depending on the phase of fhiht he kmks at different measures.

4.2 The Inference Fna ine

The contrd mechanism for the program is simple; depending on the phase of f l iht a predetermined list of rule numbers is identified and this list h then used to call each of the required rules in turn. All the rules in the given list are called for each iteration, no matter whether they fail or succeed. This means that the rules are not dependent on the ordec in which they are stored within the knowledge base.

There am four basic procedures used by the rules:

biand- derives the confidence level that A and B are we for two fuzzy distributions.eg. the confidence that the speed is 1 15 knots and is 1.3vs is illustrated in Appendix 1.

bigt - derives the confidence level of A > B for two fuzzy

distributions.

bilt - derives the confidence level of A < B for two fuzzy distributions, eg. the confidence that the speed is 115 knots and below 1.3vs is also illustrated in Appendix1 .

h e a n - computes a weighted mean for a measure of the last n iterations, weighted so that greater importance is given to more recent information.

A text window allows the intelligent decision support system to display its critical comments. An example of the user interface can be seen in the screen dump for text shown in Fylure 1. The numbers in the left hand column show the time of the message from the start of the approach. The screen dump for the corresponding graphical representation of the flight path is shown in Figure 2. The functions contained within the vertical grids have time as the abscissa which corresponds to the timing in the text window. The other parameters, height and plan position have range as the abscissa.

There are five types of information that are displayed in the text window; these are listed with conesponding examples shown in Figure 1. Further examples are given in Appendix 3.

a) Reports on the phase of flight. eg. descent occuring at 98s. b) Reports on the state of the aircraft. eg. not maintaining approach and descent is critical at 103s. c) Warnings that a measure is aitical. eg. descent rate is critical at 103s.

d) Predictions about future states of the aircraft. eg. prediction of missed approach. e) A final description of the flight is displayed at the end of each approach. eg. the description of the final approach at 251s.

For continual measures that have gone critical, that is those that have a confidence level of greater than 0.8, the confidence level is also displayed after the comment in the format:

fireration number] [commend [confidence lewd

To aid in reviewing the flight, a file is created with a record of all the comments that have been given to the Instructor. This may be examined after the approach has been completed and Ute performance reviewed.

5. Conclusions

5.1 The Critiauina ADDroach with Fuzzv Reasoning

The use of a critiquing approach with fuzzy reasoning in the construction of an intelligent decision support system means

1064

that the problems commonly found in knowledge elidtation are greatly reduced. The existence of the knowledge engineering bottleneck is well documented as being the major problem in the implementation of expert systems. It has been claimed that the only way systems can be developed successfully is when the knowledge engineer is also the domain expert. The reporting of only critical information substantially reduces the magnitude of the bottls-neck problem, due to the reduction in the required knowledge elicitation; instead of trying to determine all of the possible aircraft states , the system concentrates on the identification of the acceptable limits of flight. This reduces the problem to a manageable size and provides the flexibility necessary for such a problem area as flight training.

The approach taken also seems to fit in with the instructors' conceptualisation of the problem. To them, there is usually no problem unless the aimft handling is bad, meaning that a useful decision aid is one which alerts them to a potential problem.

Lastly, guidelines set down by the CAA are themselves concerned with the limits of acceptable aircraft handling [CAA 19841.

Thus, from a number of criteria, the critiquing approach and funy reasoning are suitable techniques for the intelligent decision support system.

There am many problems in the design of a complete system for the monitoring of aircraft handling in flight simulators. A generic system would have to cope with the problems of airline variability as well as hose of aircraft variability. This is not an overwhelming problem, but it does increase the difficulty, It would also have to provide a means by which the users could modify the knowledge base, so that ch$nges in the airline requirements could be implemented after delivery. The coverage of the system would also have to be extended beyond 11s approaches. Fundamental knowledge acquisition would have to be camed aut for the new areas.

As there is so much variation between airlines, knowledge acquisition would have to be carried out separately for each. However, many of the associated problems have already been solved and the acquisition of knowledge for another airline would be less of a problem. The knowledge would be in the same format but with minor variations, so the task is reduced to one of determining the differences that exist between the airline's rules and those of the generic system.

Further wok is necessary on the interfaces of the system, both to the simulator and to the instructor. A study, in collaboration with instructors, is required to determine the best methods of presenting the interpreted infomation to the instructor.

evidence of instructor rejection of existing automated performance programs, it would be wise to conduct an initial survey of instructor attitudes to intelligent decision support systems. Hopefully they would find a decision support system such as this to be more useful than previous quantitative analysis systems.

[Alvey 19881 Rediffusion Simulation Ltd, Smiths Industries, Schlumberger Technologies, IKBS Applications for Simulation and Tactical Decision Aids - A R e p t i to the Alwy Directorate, Alvey Directorate, December, 1988.

[CAA 19841 Civil Aircraft Authority, Pilot licence tests: instructions to authorised examiners, CAA, London. Jan 1984.

[Dickman 19821 Dickman. J.L., Automated Performance Assessment: An Overview and Assessment. Proceedings of the 4th Intersewicdlndustry Training Equipment Conference, Naval Training Equipment Center Orlando, FL, November 1982.

[Dick" 19841 Dickman. J.L, Training Menagement Systems: An Assessment of Current Status and Fuiure Potential, Future Applications 6 Prospects for Flight Simulation, 9-14th May 1984, Fliiht Simulation Group, Royal Aeronautical society.

[Hrton 19861 Heaton. C., Know/e&e Elicitation for an Intelligent Instructor's Aid in a flight Simulator, MSc Dissertation, Dept. Electrical Engineering, Brunel University. August 1986.

[RSL 19881 Sime, J.A., An ltdl&?nt Decision Support System for ILS Approaches in Civil Flight Simulators, Rediffusion Simulation Ltd., Research Report (unpublished) RD-88-13,1988.

[Sutton and Towlll1985] Sutton, R. and Towill, D.R., An Infroduction to the Use of Fuzzy Sets in the Implementation of Control Algorithms, Joumal of the Institution of Electronic and Radio Engineers, 1/01 55, No 10. October 1985.

In short, it is feasible, but is it worthwhile? In the face of

1065

APPENDIX 1 : FUZZY SETS APPENDIX 2 : 11s APPROACH RULES

The folkwing examples show different FUUY sets for the The confidence of the validity of the following states is speed parameter. derived from the continual measures :-

a) set for speed is 1 . 3 ~ ( with an allowance of 0 .05~~) . 1. flagged ILS localiser 2. Flagged ILS glideslope 3.a) Localiser alive

b) Localisercapture. c) Established on localiser. d) Glideslope alive. e) Glideslope capture. f) Established on glideslope.

1.3vs

b) Set for speed below 1 . 3 ~ .

confidence

4. Approach to stall. 5. Thrust management is critical 6. Missed approach prediction. 7. Approaching decision height. 8. Not maintaining approach and descent

The following measures are monitored continuously for cn'ticality :- 1.3vs

c) Set for speed is 1 15 knots (within 3 knots).

d) Confidence for speedis 115 knots and is 1 . 3 ~ ; derived to be 0.45. using the sets from a) and c).

I

e) Confidence for speed is 7 15 knots and is low ; derived to be 0.9. using the sets from b) and c).

............... confidence "Lo+- 115 1.3~s speed

1. Altitude a) measure of glideslope error b) measure of height deviation c) longterm measure of height deviation d) measure of deviation of rate of descent e) longterm measure of control of rate of descent

2. Heading a) measure of localiser error, after established b) measures of heading m r c) longtenn measure of heading error d) prediction of not overflying beacon or marker e) measure of overflying beacon or marker 1) measure of wind correction being applied g) measure of excessive normal acceleration

3. Airspeed a) measure of airspeed deviation b) longterm measure of the deviation

4. Approach to stall.

5. Persistent undershoot or overshoot a) measure of longterm bias in glideslope error b) measure of energy deviation wrt height profile c) longterm measure of energy deviation.

1066

APPENDIX 3 : EXAMPLE

The Tea W W (Figure 1) statements on approach. in a 15 knot wind with ma and tail wind components are amplified further bebw. Tho graphical repre- of Um appmech is shown in Figure 2

001 002 019 020 021 022 097 098 1 0 3 104 206 251

The bcaliser hias been llagged and the aimh is in a masonable p i t i i n to start the approach. The badiser it diva. The ai@ tr hbhand looks critical. Seems the pibt is d n g the airspeed, so there is no comment. Maybe he is not correcting, so comment again on high airspeed. Pibt is correclng. so no furlher comment. Glidedopeis~so start the descent assessment. Descant rata is too high; descent is critical and not maintaining approach. F’ibtk camding Um approach. so no comment Aimft is approaching dedsion height. Deddan height initiaw description of the final approach.

The main points in the debarption am hat the short term contrd of airspeed and descent rate were aitical as commented on above, but !he bng term control of airspeed was ok and the shhort and long term control of altitude were ok.

1 o o o o m S t a r t o f Approach O * ’ * *

1 the l o c a l l s e r has been f lagged 2 Ncly PHASE - s t a r t f l n a l s 2 the l o c a l i s e r I s a l i v e 19 shor t term con t ro l o f alrspeed I s c r i t i c a l 21 shor t term con t ro l o f alrspeed i s c r l t l c a l 96 the g l ideslope has been f lagged 97 NEW PWSE - t r a n s l t l o n t o descent 97 the g l ides lope I s a l ive 98 NEW PHASE - descent 103 descent r a t e er ror I s c r l t l c a l 8.95 103 descent r a t e e r r o r I s c r l t l c a l 0.95 183 n o t maintaining approach and descent i s c r i t i c a l 286 approachlng dec is ion he igh t 251 END OF PHASE - declslon he lgh t has been reached 251 o*ooL Approach Completed L * o o *

251 Descr lp t lon o f F i n a l Approach.

8.837 8.827

8.95

251 Descent Rate con t ro l near dec is lon height was ok 8.43 251 Long-tem con t ro l o f energy was ok 0.59 251 Long-term con t ro l of airspeed was ok 0.556 251 Short-term con t ro l of a l t l t u d e was ok 8.476 251 Short-term con t ro l o f energy was ok 8.652 251 Long-tem con t ro l o f a l t i t u d e was ok 8.755 251 Short-term con t ro l of descent r a t e was c r l t i c a l 8.95 251 Short-term con t ro l o f airspeed was c r l t l c a l 8.837 251 Long-term con t ro l o f heading wa8 ok 8.637 251 Short-term con t ro l of heading was ok 0.637 I

1067

I I 100 158 200 258

I

1 4 wlnd

helght

48 e k l t range-to-threshold

I

1068