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AIAA Modeling and Simulation Technologies Conference and Exhibit5-8 August 2002, Monterey, CaliforniaRobust Flight Control: A DistributedSimulation ImplementationDavid Harman ¤ and Hugh H. T. Liu yInstitute for Aerospace Studies, University of Toronto.4925 Du®erin Street, Toronto,Ontario, CanadaThe consideration of robustness in °ight control design is an important issue. In thispaper we address the real-time simulation of a benchmark aircraft model with robust°ight controllers. The distributed system implementation steps are outlined. Further,a comparison of o²ine simulation results and published results is o®ered to verify thevalidity of the model to be implemented in the real-time environment. These results willbe used as the basis for future investigation of real-time simulations.AIAA 2002-4473IntroductionModern aircraft include a variety of automaticcontrol systems that aid the °ight in navigation,°ight management, and augmenting the stabilitycharacteristics of the airplane. The robustnessproblem, involved with the design of these °ightcontrol systems, is intended to deal with system uncertainties.These system uncertainties in °ight maycome from either parameter variations, unstructuredmodels inaccuracies, or external disturbance, such asturbulence and wind gust. Further, large envelope°ight operation requires the °ight control system tobe robust; aircraft agility requires attention of therobustness aspect when the aerodynamic control islost; and even in hypersonic °ight, high speed requiresstability robustness as well.There exist many robust °ight control techniques.Some of them require complicated control structures,which lead to a high level of computing complexity.Further, aircraft systems consist of multiplesubsystems and components that interact with eachother. Therefore, real-time simulation for distributedsystems become a critical stage in order to bringdesign to reality. However, investigation in this ¯eldis rarely reported. This paper presents the researchwork of implementing distributed aircraft models forreal-time simulation.The remainder of this paper is organized as follows.First, an introduction to the benchmark aircraft modelis given. The real-time computing facility is then described,followed by the discussion of the distributedmodel along with the implementation and modi¯cationsteps necessary for real-time simulation. Preliminaryo²ine simulation results are obtained to provide veri¯cationof the newly distributed model's accuracy¤ Graduate student, UTIASy Assistant Professor, UTIAS, AIAA MemberCopyright c° 2002 by Hugh H.T. Liu. Published by the AmericanInstitute of Aeronautics and Astronautics, Inc. with permission.compared with the published results. Finally, concludingremarks and future development work are o®ered.The GARTEUR ProjectIn 1995, the Group for Aeronautical Researchand Technology in Europe (GARTEUR) issued adesign challenge geared towards the improvementand optimization of computer aided aircraft designintegration. 1 The focus of this study was on the °ightcontrol discipline and making the controller analysisand design methodology suitable for muli-disciplinaryconsiderations. It was determined that robust controlmethodology had the potential to satisfy these criteria.A ¯ctitious commercial aircraft model (namedRCAM) was developed, using Matlab/Simulink,and supplied to a number of research teams in theEuropean aerospace community. The goal was foreach team to design an autopilot for the ¯nal segmentsof a landing approach. Each team centralized arounda di®erent robust technique and the result was acollection of technical papers comparing each of themethods bene¯ts and drawbacks. 2The block diagram model, originally developed byGARTEUR, is illustrated in Figure 1. This templatewas deemed ideal for our investigation of remodelingand implementation for real-time simulation, since itsstructure allows for easy replacement and distributionof subsystems. As well, its development and o²ineresults are well documented.Real-Time Computing FacilityPresently, at the University of Toronto Institute forAerospace Studies, we are developing a real-time systemssimulator (RTSS). The core computing facility ofRTSS consist of a networked cluster of high-end commercialo®-the-shelf (COTS) real-time computers, andhas been installed in our laboratory. The current systemsetup is depicted in Figure 2.The main components of the facility include: 31 of 9American Institute of Aeronautics and Astronautics Paper 2002-4473Copyright © 2002 by the author(s). Published by the American Institute of Aeronautics and Astronautics, Inc., with permission.

system for hardware-in-the-loop simulation.² RTSS is also connected through a 1.25 Gbit/sGiganet to a similar facility to share data andresources.This real-time computing facility is suitable forour proposed distributed real-time simulation of thebenchmark aircraft model.Fig. 1GARTEUR Benchmark Model (RCAM)Distributed ModellingThe research objective of this project is to runreal-time distributed simulations on the RCAM modelwith di®erent robust °ight controllers, and to conducta comparative analysis.The interface software that interprets the Matlab/Simulinkmodels and runs them on the real-timenodes is RT-LAB, developed by OPAL-RT Technologies.4 The original RCAM model was created usingMatlab (version 4.2) and Simulink (version 1.3), neitherof which is supported by RT-LAB. Therefore, aninitial task is to upgrade the models and their correspondingcodes. This section details the changes thatwere made in order to prepare this model for real-timedistributed simulation.Model Sub-System SeparationThe original RCAM model (see Figure 1) was regroupedinto sub-systems as illustrated in Figure 3.Fig. 2UTIAS RTSS Facility² Three host computers each having dual-Pentiumprocessorsrunning Windows 2000 operating system.² Four real-time computers each having dual-Pentium-processors running Neutrino (QNX6)real-time operating system.² The real-time nodes are directly connected by400Mbit/sec FireWire and communicate with thehosts over a dedicated 100Mbit/s Ethernet network.² The system consists of 108 multiple channel IOFig. 3New RCAM Block DiagramThe model was grouped into the depicted subsystemsin a manner that best resembled a real aircraft,for the purposes of running distributed simulations.Each sub-system is described below:2 of 9American Institute of Aeronautics and Astronautics Paper 2002-4473

1. The trajectory generator is the ¯rst subsystemand it is a Matlab S-function which outputs aset of reference signals that the virtual aircraftmodel is to follow, as shown in Figure 4. The°ight is broken down into four segments (markedby A,B,C and D in Figure 4). The ¯rst segment(A) consists of a level °ight with a track angle of-90 degrees (west). An engine failure is simulatedpart way through this segment to investigate thelateral features of the robust controllers. The secondsegment (B) is a controlled 90 degree turn,to a track angle of zero degrees (north). Thethird segment (C) is a two-phase approach, ¯rstwith a commanded °ight path angle of -6 degreesfollowed by a commanded °ight path angle of -3degrees. Finally, in the last segment (D), the -3degrees °ight path angle is to be maintained duringa wind shear. The wind shear model is twodimensional and derived from. 52. The wind inputs and aircraft actuators aregrouped into a separate subsystem and are thoroughlyoutlined in the technical report TP-088-3of GARTEUR (AG08). 1 All system actuators areassumed to have ¯rst order system dynamics withrate limits and saturations. As well, all sensorsare assumed to be perfect.3. The next subsystem contains the nonlinear aircraftmodel. This is also a Matlab S-function andrepresents the aircraft's dynamic behavior as a setof equations.4. The controller is the next subsystem to be separated.The separation of the controller from therest of the model allows the incorporation of differentrobust controllers to be quite easy. The restof the model can remain constant while only thecontroller algorithms are changed.5. The ¯nal subsystem is known as the console. Itconsists of all the outputs from the nonlinear aircraftmodel subsystem as well as the simulationclock. This subsystem is necessary for user interfacewhen implementing the model into RT-LABand simulating in real-time.Controller ModelThe original RCAM model incorporated a robustcontroller that GARTEUR designed; it is outlined inthe technical report TP-088-9 of GARTEUR (AG08). 6This controller was used in our simulations solely forthe reason that the results of the original RCAM model(using this controller) are well documented. Theseresults provide a basis of comparison to verify the validityof our o²ine simulation results of the upgradedRCAM model. The design approach of the controlleris based on loop shaping using normalized coprimeFig. 4 Trajectory for the Landing Approach of theRCAMfactorization. This method is based on H 1 synthesisand uses an uncertainty structure that does notrequire explicit uncertainty description. 7 Both the lateraland longitudinal controllers are broken down intoinner and outer loops. The purposes of the inner loopsare to stabilize and augment the handling qualities ofthe aircraft while the outer loop guides the aircraftalong the generated trajectory.Remodeling of RCAMAs mentioned earlier, some modi¯cations had to bemade to the original RCAM model in order for it tobe used in the RT-LAB real-time environment.S-functionsThe purpose of an S-function is to provide acomputer language description of a dynamic system.Two S-functions exist in the RCAM: the trajectorygenerator and the nonlinear aircraft dynamics block.Both of these were originally written in C-code forSimulink (Version 1.3).The upgrading of these S-functions took place in twophases. The ¯rst phase was to reprogram them as Matlabscript ¯les (¯le extension .m). The second phasewas to reprogram the original RCAM S-functions in C-code in accordance with the upgraded stipulations ofSimulink (Version 3). 8 The C-code S-functions werethen converted into Matlab executable ¯les (CMEX¯les) which are recognized by Simulink.RT-LAB Model Con¯gurationFigure 3 illustrates the proper format for which aSimulink model must be grouped in order for RT-LABto acknowledge it. The RT-LAB software groupsmodels into three di®erent categories: slave blocks3 of 9American Institute of Aeronautics and Astronautics Paper 2002-4473

Fig. 5Segment AFig. 7Segment CFig. 6Segment BFlight Segment C ComparisonFigure 7 shows the di®erences between the M-¯leand CMEX-¯le RCAM third segment o²ine simulations.Once again, as in the second segment, both theM-¯le and CMEX-¯le simulation results are almostidentical with an error that is to small to be considered.Flight Segment D ComparisonFigure 8 shows the di®erences between the M-¯leand CMEX-¯le RCAM ¯nal segment o²ine simulations.This plot uses the same variables as in the segment Cplot (x-position and altitude). Once again, the resultsFig. 8Segment Dreveal no signi¯cant error between the two simulations.Evaluation CriteriaAccording to the technical report TP-088-3 ofGARTEUR (AG08), 1 each designed robust °ightcontroller will be evaluated by several criteria to\obtain an objective comparison between completelydi®erent controllers". Therefore, we will validateour remodeled RCAM model by checking the samecriteria. The tested controller is designed usingeigenstructure assignment and four test cases areconducted: (1) nominal case, (2) CG fwd case wherethe horizontal center of gravity has been shifted tothe most forward position, (3) CG aft case where theCG is shifted to the most afterward position, and5 of 9American Institute of Aeronautics and Astronautics Paper 2002-4473

(4) time delay case where the °ight is executed witha nominal center of gravity and a time delay of 100 ms.Five quantitative criteria are given for each segment(A, B, C, D) of the °ight path:² performance;² quality;² safety;² control; andTable 1Segment A Criteria ComparisonCases P A Q A S A C A R AGARTEUR 0.0764 0.5432 0.0038 0.0037 0.0309nominal 0.0766 0.5482 0.0041 0.0031CG fwd 0.0743 0.5269 0.0104 0.0031CG aft 0.0792 0.5744 0.0091 0.0032delay 0.0794 0.5770 0.0096 0.0032average 0.0774 0.5566 0.0083 0.0032 0.0347² robustnessSegment A Criteria EvaluationThe performance criterion de¯nes the lateral deviationboundary of 20m to account for the e®ect ofturbulence, and the boundary of 100m during enginefailure:P A = 1 ³2maxeyb (t)t 0·t·t a 100 + e yb(t a )´(1)20where e yb (t) denotes the lateral deviation in bodycoordinates.The quality criterion considers the maximum lateralacceleration of 0:2g:Q A =³ ny (t)´maxt 0·t·t a 0:2(2)The safety criterion sets the limit of the maximumangle of attack ® of 12 deg:S A =³ j®(t)j´3maxt 0·t·t a 12(3)The control criterion concerns the rudder actuatore®ort to stabilize the aircraft after engine failure isrecovered:C A =Z tat 0± 2 R dt (4)The maximum di®erence between the lateral deviationof the trajectories with nominal and perturbedcenter of gravity (CG forward, CG backward) and withthe time delay is de¯ned as:³´¢ eyb (t) = max je ybmax (t)¡e yb (t)j; je ybmin (t)¡e yb (t)j(5)and the robustness criterion sets the limit of maximalallowable deviations and the limit at the end of thissegment:R A = 1 2³ max¢eyb (t)t 0·t·t a 10+ ¢ eyb(t a )´2(6)The o®-line simulation results of Segment A comparedwith the published results are shown in Table 1and Figure 9.Fig. 9 Segment A Criteria Evaluation: Case (1)- nominal, (2) - CG fwd, (3) - CG aft, (4) - delay,(5) - averageSegment B Criteria EvaluationThe performance criterion de¯nes the maximum lateraldeviation of 200m due to the turn and the lateraldeviation of 20m at the end of the segment:P B = 1 2³ maxeyb (t)t a·t·t b 200 + e yb(t b )´20(7)The quality criterion considers the maximum lateralacceleration of 0:02g:Q B =³ ny (t)´maxt a·t·t b 0:02(8)The safety criterion sets the limit of the maximumangle of attack ® of 12 deg:S B =³ j®(t)j´3maxt a·t·t b 12(9)The control criterion concerns the rudder andaileron actuator e®ort:Z tb ³ ´C B = ± R 2 + ± A2 dt (10)t aThe robustness sets the limit of maximal allowablelateral deviations with perturbed center of gravity and6 of 9American Institute of Aeronautics and Astronautics Paper 2002-4473

time delays:R B = 1 2³ max¢eyb (t)t a·t·t b 20+ ¢ eyb(t a )´2(11)The o®-line simulation results of Segment B comparedwith the published results are shown in Table 2and Figure 10.Table 2Segment B Criteria ComparisonCases P B Q B S B C B R BGARTEUR 0.4964 0.7340 0.0382 0.0027 0.0161nominal 0.6090 0.7095 0.0302 0.0024CG fwd 0.6092 0.6925 0.0374 0.0024CG aft 0.6086 0.7332 0.0236 0.0025delay 0.6086 0.7356 0.0239 0.0025average 0.6089 0.7177 0.0288 0.0025 0.0156The control criterion concerns the tailplane actuatore®ort:Z tcC C = ± T 2 dt (15)t bThe robustness sets the limit of maximal allowablevertical deviations with perturbed center of gravityand time delays:R C = 1 ³2max¢ezb (t)+ ¢ ezb(t c )´(16)t b·t·t c 2 0:6The o®-line simulation results of Segment C comparedwith the published results are shown in Table 3and Figure 11.Table 3Segment C Criteria ComparisonCases P C Q C S C C C R CGARTEUR 0.3285 1.1808 0.0070 0.0150 0.4926nominal 0.3475 1.1907 0.0078 0.0160CG fwd 0.3759 1.2080 0.0109 0.0261CG aft 0.3268 1.1757 0.0054 0.0084delay 0.3271 1.1774 0.0054 0.0084average 0.3443 1.1880 0.0074 0.0147 0.5034Fig. 10 Segment B Criteria Evaluation: Case (1)- nominal, (2) - CG fwd, (3) - CG aft, (4) - delay,(5) - averageSegment C Criteria EvaluationThe performance criterion considers the maximumvertical deviation during the capture of the ¡6 degreeglide slope and the vertical deviation at the end ofthis segment. Further, speed variations should be keptsmall in spite of the change in required angle of attack:P C = 1 ³3maxezb (t)+ e zb(t c )+ jV ¡ V commandjt b·t·t c 20 64(12)The quality criterion considers the maximum verticalacceleration:³ nz (t)´Q C = max(13)t b·t·t c 0:05The safety criterion sets the limit of the maximumangle of attack ® of 12 deg:S C =³ j®(t)j´3maxt b·t·t c 12´(14)7 of 9Fig. 11 Segment C Criteria Evaluation: Case (1)- nominal, (2) - CG fwd, (3) - CG aft, (4) - delay,(5) - averageSegment D Criteria EvaluationThe performance criterion considers the maximumvertical deviation due to the wind shear and the verticaldeviation at the end of this segment:P D = 1 2³ maxezb (t)t c·t·t d 20+ e zb(t d )1:5´(17)The quality criterion considers the maximum verticalacceleration:³ nz (t)´Q D = max(18)t c·t·t d 0:2American Institute of Aeronautics and Astronautics Paper 2002-4473

Table 6 Average Error Comparison Table of NominalTest CaseNominal Case Criteria AVRSeg A P A 0.26%Q A 0.92%S A 7.89%C A 16.22%R ASeg B P B 22.68%Q B 3.34%S B 20.94%C B 11.11%R BSeg C P C 5.78%Q C 0.84%S C 11.43%C C 6.67%R CSeg D P D 3.04%Q D 30.83%S D 6.96%C D 3.24%R D2 Magni, J.-F., Bennani, S., and Terlouw, J., Robust FlightControl: A Design Challenge, Springer-Verlag, 1997.3 Liu, H., \Real-Time System Simulation using COTS for°ight cotnrol integration," AIAA Modeling and SimulationTechnologies Conference & Exhibit, August AIAA Paper A01-37308,2001.4 The Opal-RT Technologies Inc., RT LAB 4.2 User's Guide,September 2000.5 Robinson, P., \The Modeling of Turbulence and Downburstsfor Flight Simulators," Tech. Rep. # 339, UTIAS, 1991.6 GARTEUR, \RCAM Preliminary Design Document," Tech.Rep. TP-088-9, Group for Aeronautical Research and Technologyin Europe (GARTEUR), Action Group FM (AG08), 1995.7 Francis, B., A Course in H 1 Control Theory, Springer-Verlag, 1987.8 The Mathworks Inc., Writing S-Functions (Version 3), October1998.9 Harman, D. and Liu, H., \Robust Flight Control: A Real-Time Simulation Investigation," International Council of theAeronautical Sciences (ICAS), May (accepted on 18-Oct-2001),2002.this upgraded RCAM model matched the simulationresults of the original RCAM model. 6 The comparisonbetween the CMEX-¯le and M-¯le RCAM simulationmodels brought to light the realization that theformer runs approximately 20 times faster, in termsof clock time. With minor exception of the ¯rst°ight segment results (showing small discrepanciesin °ight path), each of segments B, C and D showedinsigni¯cant error between the M-¯le and CMEX-¯leo²ine simulations. It is obvious, from the resultsof this paper, that the CMEX-¯le RCAM model isthe superior choice for simulation. Further, o®-linesimulation results have been analyzed extensively,based on the evaluation criteria. Comparison workshows satisfactory results as well.The next step, in this area of research, is to implementthe CMEX-¯le RCAM model into the realtime environment using RT-LAB. Real-time simulationsneed to be conducted to evaluate the designcriteria. Comparison work will also be performed betweeno®-line results and real-time results and amongdi®erent robust controllers. It is beyond the scope ofthis paper and is the topic presented in another paper.9References1 GARTEUR, \Robust Flight Control Design Challenge ProblemFormulation and Manual: The Research Civil AircraftModel (RCAM)," Tech. Rep. TP-088-3, Group for AeronauticalResearch and Technology in Europe (GARTEUR), ActionGroup FM (AG08), 1997.9 of 9American Institute of Aeronautics and Astronautics Paper 2002-4473