ISIC-2003-1 Valasek, Ioerger, Painter SOFT COMPUTING IN THE SMART COCKPIT A Workshop The 2003...

ISIC-2003-1 Valasek, Ioerger, Painter

SOFT COMPUTING IN THE SMART COCKPITSOFT COMPUTING IN THE SMART COCKPIT

A WorkshopThe 2003 International Symposium on

Intelligent ControlHouston, Texas

October 5-8, 2003


INSTRUCTORSINSTRUCTORS

Dr. John Valasek - Assoc. Professor, Aero. Eng., Texas A&[email protected] 979 845-1685.

Dr. Tom Ioerger - Assoc. Professor, Cptr. Sci., Texas A&[email protected] 979 845-0161.

Dr. John Painter - Professor, EE, Aero., Cptr. Sci. (Ret.), [email protected] 979 696-0429.


COURSE OUTLINECOURSE OUTLINE

SECTION I

• Introduction To The Smart Cockpit And Free Flight

• Free Flight Functionality

• Architecture: Aircraft & Simulator, Software & Hardware

SECTION II

• Artificial Intelligence In The Cockpit

• Intelligent Computing Techniques

• Software Agents: The Key To Smart Cockpit Software

SECTION III

• Real Time Flight Simulation: The Basics

• Real Time Flight Simulation For Free Flight

SECTION IV

• Selected Research Results

• Conclusion: Summing It Up


SMART COCKPIT COMPUTING - WHAT IS IT?SMART COCKPIT COMPUTING - WHAT IS IT?

• COCKPIT FUNCTIONAL INTEGRATION VIA SOFTWARE Helping the Pilot Visualize, Understand, and Fly. Helping the Airplane Automate Nominal Flying Tasks. Helping Air Traffic Control With Transition to “Free Flight.”

• VIEWING AVIATION AS A SOFTWARE PROBLEM. Reducing Pilot Task Load Via Automation. Increasing General Aviation (GA) Pilot Performance in Weather. Increasing GA Weather Access to Un-Instrumented Airports. Increasing Flight Safety With

- Automatic Collision Avoidance.- Automatic Weather Avoidance.- Automatic Terrain Avoidance.- All Three at the Same Time.


SMART COCKPIT COMPUTING - WHY IS IT?SMART COCKPIT COMPUTING - WHY IS IT?

• HAVE YOU FLOWN, LATELY? Major City Airports are Traffic Saturated. Air Travel is Geared to Using Major Cities. Only 715 Airports are Weather Instrumented. The Answer is Not More Jumbo Jets.

• WHY NOT FLIGHT-ENABLE THE MEDICALLY QUALIFIED? There are 5,400 Public Airports. Flying Can Become a Public Utility. Computing Technology Can Satisfy the Major Requirements.

- Inexpensive Airport Weather Instrumentation .- GA Aircraft Relative Affordability.- GA Pilot Weather Proficiency Increased.- GA Pilot Training Affordability.


IS THE SMART COCKPIT JUST FOR GA?IS THE SMART COCKPIT JUST FOR GA?

• TECHNIQUES APPLY TO AIR TRANSPORT AS WELL AS GA. Improves Situational Awareness and Multi-Tasking. Reduces Pilot Work-Load. Works for the Pilot, Not Vice-Versa. Works for the Pilot, But Doesn’t Replace Him.

• BUY THIS TECHNOLOGY “BY THE YARD.” Software Architecture is Modular - by Function. Some Functions Require Additional Cockpit Instrumentation. Implement Functions as You Can Afford Them. Augmentable Functionality is the “Name of the Game.”


SMART COCKPIT COMPUTING - WHERE IS IT?SMART COCKPIT COMPUTING - WHERE IS IT?

• AT THE CROSS-ROADS OF MULTI-DISCIPLINES. Flight Control & Air Traffic Management. Intelligent Control & Soft Computing. AI & Software Agents. Human Factors & User Interface Design.

• IN THE HANDS OF MULTIPLE AGENCIES. Government - NASA Langley Research Center (SATS). Academia - Aeronautical Engineering, Computer Science, Etc. Industry - Avionics Companies (FMS, GPS, etc.).


SATS - THE PROGRAMSATS - THE PROGRAMSmall Aircraft Transportation SystemSmall Aircraft Transportation System

• FIVE-YEAR DEMONSTRATION GOALS (2001-2005) Technology and Operational Capability:

1. Higher Volume @ Non-ILS Airports 2. Decreased Landing Minimums (Weather).3. Increased Single-Pilot/Mission Safety/Reliability.4. Integration of SATS Traffic With NAS.

• THE OPERATIONAL/TECHNOLOGY KEYS. Increasing Cockpit Technology Increases Operational Capability. “Self-Controlled Airspace” (SCA) for Smaller Airports. Increasing “Usable” Airports by 750% (4,685 + 715 = 5,400). ATC-Acceptable Procedural/Spatial Interface and Hand-off.


SATS - THE APPROACHSATS - THE APPROACHSmall Aircraft Transportation SystemSmall Aircraft Transportation System

• ATC Clears Aircraft to SCA Holding Stack at IAF. • Self-Separation via ADS-B (Req. Conflict Mgt. Software).• Approach Sequencing and Airport Info. via AMM.

FAF

RUNWAY

ATC: FAA Air TrafficControl.

IAF & FAF: Initial- andFinal-Approach Fixes.

ADS-B: AutomaticDependent SurveillanceBroadcast (Radar Xpndr.)

AMM: Airport ManagementModule (Digital Data-Link)


SMART COCKPIT RESEARCH - DOING ITSMART COCKPIT RESEARCH - DOING IT

• REQUIRES A FIXED-BASE FLIGHT SIMULATOR. General-Purpose Cockpit. Realistic Controls.

- Stick, Rudder, Throttles, Flaps, Trim, Gear, Brakes.- It Must be Realistic to Fly.

General Purpose Panel Displays.- Touch-Screen LCDs Are Good (for Research/Development).

Forward-Projection Screen System.- At Least 90-degrees Wide.- 160-degrees is Better (Wrap-Around, 3-Screen).

• THE SIMULATOR IS A SOFTWARE DEVELOPMENT TOOL. Software Modules Developed in MATLAB® - Ported to C++. Software Modules Integrated in the Flight Simulator Computers. Software Functionality Validated/Corrected by Pilots Flying It.


FREE FLIGHT - WHAT IS IT?FREE FLIGHT - WHAT IS IT?

• A MOVING TARGET. Its Specification is Not Yet Stable. Ideal Agreed, but Details Disagreed. Players: FAA, NASA, Aviation Industry.

• THE ORIGINAL IDEAL1

“Freedom of Choice” … of IFR Routes (RTCA - 1995). Pilot Selection of Trajectory … in Real Time. “Max/Max” Solution for Safety/Efficiency.

• THE PROBLEM. Requires New Air/Ground Operations and Technology.

1Control Applications and Challenges in Air Traffic Management, by Joseph W. Jackson and Steven M. Green, Proceedings of the American Control Conference, June, 1998, pp. 1772-1788.


FREE-FLIGHT - COCKPIT FUNCTIONALITYFREE-FLIGHT - COCKPIT FUNCTIONALITY

• VOICE RADIO.• NAVIGATION - Global Positioning System (GPS).• PANEL INSTRUMENTS - Flat LCD With Touch-Screen.• SITUATION DISPLAY - Flat LCD With Touch-Screen. (Moving Map Reqs. Nav. Data Base).• AVOIDANCE FUNCTIONS:

Collision - On Situation Display (Reqs. ADS-B Beacon Add-on). Weather - On Situation Display (Reqs. A/G Digital Data Link). Terrain - On Situation Display (Reqs. Nav. Data Base).

• HEAD-UP DISPLAY (HUD) - For Eyes Out of the Cockpit. - Instrument and Approach Displays.• APPROACH AIDS: - GPS, ILS, and/or SATS.

- On HUD and/or Situation Display.• PILOT ADVISOR - On HUD and/or Situation Display.

(Req. Flight Mode Interpreter)


PILOTFLIGHTPLAN

CONTROL

AIRFRAME

NAVIGATION + GUIDANCEAUTO-PILOT

FLIGHT MANAGEMENT SYSTEM

NOTE: CDU Interfaces Not Explicit

BASIC GUIDANCE LOOP(S)

ManualOpen-Loop

Closed-Loop


THE BASIC NAVIGATION TRIANGLETHE BASIC NAVIGATION TRIANGLEThe Problem is “Wind”The Problem is “Wind”

Air Vector: Heading/True-Air-Speed = 090°/150

Wind Vector:WD/WV = 350°/20

Ground Vector: Track/Ground-Speed = 097°/155

• CONVENTIONS. Map Convention: North (N) is “Up.” Directions Measured Clockwise, From True North (360°) Air Vector and Ground Vector are “To” Direction. Wind Vector is “From” Direction.

• AIR NAVIGATION REQUIRES COMPUTATION OF “WIND.” Manual Wind Computation Uses Mechanical Computer - “E6B.” Automated Digital Wind Computation in Avionics.


GLOBAL POSITIONING SYSTEM (GPS)GLOBAL POSITIONING SYSTEM (GPS)

• A SATELLITE MULTI-RANGING SYSTEM Global Coverage. All-Weather Capability. 24 12-Hour Satellites (3 On-Orbit Spares). Position/Velocity Output -

Latitude, Longitude, & Altitude. 4 Range Measurements - 3 Position Coordinates

& Precise Time (12 pico-sec.). Position Error: ~ A Few Meters. Position/Velocity Output - Lat., Long., & Alt. Soon to Replace Visual Omni Range (VOR)

& Instrument Ldg. System (ILS).

SV1

SV17

SV9

• THE NEW SOLE MEANS OF AIR NAVIGATION.


NAVIGATION/GUIDANCE FUNCTIONNAVIGATION/GUIDANCE FUNCTIONManual FlyingManual Flying

• BASIC NAVIGATION FUNCTION. GPS - Time, Position (LAT, LNG, ALT), Velocity (TRK, GS, ROC). Wind Computing - A/C Data Input (MAG HDG/VAR, IAS, TEMP). Guidance (Pilot Computed) - Waypoint HDG, ETE, ETA.

•AUGMENTED NAVIGATION FUNCTON.

Navigation Data Base (Waypoint Location Data). Guidance Computing - Adds Altitude (AGL) and Approach NAV.

• HIGH-LEVEL NAVIGATION FUNCTION. Flight Plan Driven - Requires Pilot Entry of Flight Plan. Guidance Computing - Adds Waypoint Maneuver Alerts (Turn, Climb).


NAVIGATION/GUIDANCE FUNCTIONNAVIGATION/GUIDANCE FUNCTIONAutopilot FlyingAutopilot Flying

• AUTOPILOT (A/P) FUNCTION. Autopilot Drives A/C Control Surfaces (Servo).

- Ailerons, Elevator, Rudder, Throttle (Optional). Autopilot Inputs - HDG, ALT, IAS (Optional). Inputs Manually by Pilot, or Computer-Generated.

•FLIGHT MANAGEMENT SYSTEM (FMS) FUNCTION.

Automates Guidance for High-Level NAV. Computes and Issues Guidance Commands to Autopilot. Simultaneous Automatic Navigation and Maneuver. Automates All Flight Phases, Including Approach/Landing.

• NAVIGATION/GUIDANCE FUNCTIONAL HIERARCHY. Manual Flying Autopilot FMS


COCKPIT DISPLAYS, BY FUNCTIONCOCKPIT DISPLAYS, BY FUNCTION

• THE PILOT’S VIRTUAL WORLD. It’s All About Situational Awareness. The Pilot “Reckons” His Situation in 4D Space-Time. Build a 4D Mental Image … Using 2D Displays.

• DISPLAY DIFFERENTIATION AND TYPING.

“Internal” Situation - The Airplane (Trad. Gauges & Switches). “External” Situation - The Flight.

Long-Term - Relatively Static (Trad. Maps & Books).Short-Term - Very Dynamic (Traditionally, Gauges).

Internal and Short-Term External Display Commonalities. Two Different Genre of Display.


COCKPIT DISPLAY - GUIDANCE & NAVCOCKPIT DISPLAY - GUIDANCE & NAV

• DISPLAYS TAILORED TO TEMPORAL INFO I/O NEEDS.

• INFORMATION OUT, ONLY. Long-Term - Navigation (ex. - Moving Map on Panel Display). Short-Term - Guidance/Control (ex. - A/C Dynamics on HUD). Continuously Functioning, Disparate Displays.

• INFO INPUT AND OUTPUT - Flight and/or System Management. Long-Term - Flight Planning (ex. - One mode of Panel MFD). Short-Term - System Management (ex. - A/P, FMS Modes). Sequentially Functioning, Multi-Function Display (MFD). I/O Displays Called “Control/Display Unit” (CDU)


THE VIRTUAL RUNWAYTHE VIRTUAL RUNWAY

• HUD DISPLAY OF A “VIRTUAL” RUNWAY OUTLINE. Generated from GPS and Aeronautical Data-Base. “Breaking Out” at 200 Feet, on an ILS Approach.


HAZARD AVOIDANCE FUNCTIONHAZARD AVOIDANCE FUNCTION

• HAZARDS TO FLIGHT. Severe Weather - (ex. - Thunderstorms). Conflicting Air Traffic - (ex. - VFR Collisions). Controlled Flight Into Terrain - (ex. - Mountain Flying).

• FREE FLIGHT HAZARD AVOIDANCE REQUIREMENTS. Cockpit Acquisition of Hazard Data - (Digital Radio Data). Cockpit Computation of Avoidance Trajectories - (Guidance). Cockpit Display of Hazard/Avoidance Imagery - (Map).

• FINER POINTS. Cockpit Trajectory-Balancing Between Multiple Hazards. Automated Negotiation Between Aircraft and Air Traffic Control.


COCKPIT DISPLAY - HAZARD AVOIDANCECOCKPIT DISPLAY - HAZARD AVOIDANCE

• THREE HAZARDS OF DIFFERING TEMPORALITY. Terrain - Long-Term (No Dynamics). Weather - Long- to Medium-Term - (Dynamics Not A/C-Scale). Traffic - Medium- to Short-Term - (Dynamics A/C-Scale).

• DISPLAY CHOICES. All Three Situations Require Info-Out, Only, Display. Imagery of All Three Hazards Can Overlay Moving Map (NAV). Long-Term Guidance Vectors Can Also Overlay Moving Map. Display “Clutter” is a Human Factors Issue, Here. Short-Term Steering Commands on A/C Dynamics Display (HUD).


SOFTWARE ARCHITECTURESOFTWARE ARCHITECTUREForm Follows FunctionForm Follows Function

• BUILDING A DEVELOPMENT TOOL AND ENVIRONMENT. When You’re Up to Your Armpits in Alligators … ? … Remember, the Goal is a Research Tool. Don’t be a Slave to Current Cockpit Avionics. This Problem is Like GPS … 90% Software.

• HOW TO LAY OUT THE SOFTWARE ARCHITECTURE. Modularize, Modularize, Modularize !! Structure the Whole, Function by Function. Go for Independent, Communicating, Software Modules.

- “Independence” as in Task Independence, not Data Independence.- Hence, “Communicating” Modules (AI’s “Message-Passing”).

Let the Computing Hardware Take Care of Itself. Think About Project Management and Configuration Control.


ARCHITECTURE - DATA-FLOW DIAGRAMARCHITECTURE - DATA-FLOW DIAGRAM

AircraftDynamics

NAV NAVDB

FCS

A/P

FMS WXAVD

EXECAVD

FLTPLN

TFCGEN

WXGEN

TFCAVD

HUD

CDU

MFD(MAP)

Pilot


SOFTWARE ARCHITECTURE REALIZATIONSOFTWARE ARCHITECTURE REALIZATIONIN A FLIGHT SIMULATORIN A FLIGHT SIMULATOR

• COST-EFFECTIVE SIMULATOR IMPLIES …… DISTRIBUTED COMPUTING.

Use “Simple” Computers, One Per Display. Displays:

- MFD, CDU, HUD, and the Three-Screen, Projected “World.” Projecting “The World” May Not Be So Simple - Graphics Engine.

- Surface Geometry Generation is Compute-Intensive.- Pilot Requires 30 Frame per Second Refresh Rate.

Use a “Projected HUD” - Simplify Cockpit Hardware. Simulator Controller Station - One More Computer & Display. Settle on 4 Computers - 3 MS-Windows PCs, 1 SGI Unix Machine.


SIMULATOR HARDWARE ARCHITECTURESIMULATOR HARDWARE ARCHITECTURE

Projection Screen With HUD

CTRLR(kybd)

CDU(touch)

MFD(button)

PC-1MFD

PC-2CDU

PC-3CTRLR

CPTRGRAPHICS

Flaps Throttles Stick Gear & & Rudder Trim

ProjectorCenter ProjectorRightProjector

Left

Multiplexed Serial Port Adapter

Ethernet


ARTIFICIAL INTELLIGENCE IN THE COCKPITARTIFICIAL INTELLIGENCE IN THE COCKPIT

• A FUZZY “FLIGHT MODE INTERPRETER.”1

Fuzzy Decision Tool - Bayes Connectives. Flight “Modes” as State Partition. Taxi; Take-off; Climb-out; Cruise; Hold; Initial, Final, and Missed

Approach; and Land - 9 Modes. Membership Functions Modeled for Particular Aircraft.

• A RULE-BASED “PILOT ADVISOR.” Keeping the Flight Within the “Envelope.” Mode-based - Driven by Flight Mode Interpreter. Rules for Instrument Flight. Rules for Performance of This Particular Airplane.

1 “Hypertrapezoidal Fuzzy Dynamic State Interpreter,” U.S. Patent 6,272,477,B1, by Wallace E. Kelly, III and John H. Painter, Aug. 7th, 2001.


HYPERTRAPEZOIDAL MEMBERSHIP FUNCTIONSHYPERTRAPEZOIDAL MEMBERSHIP FUNCTIONS

00

50

100

1500

0.5

1

airspeed[knots] altitude

[feet]

CRUISE

`INITIAL APPROACH

1000

20003000

LANDING

FINAL APPROACH

• A 2-DIMENSIONAL PROJECTION OF A 9-DIMENSIONAL M.F. 9 State-Variables and 9 Modeled Flight Modes.


FUZZY LOGIC - OTHER COCKPIT APPLICATIONSFUZZY LOGIC - OTHER COCKPIT APPLICATIONS

• AUTOMATING DISPLAY CALL-UP AND FORMATTING. Mode-Driven, With Pilot Over-ride.

• BLENDING MULTIPLE GUIDANCE TRAJECTORIES.(A Fuzzy Executive Guidance Agent.)

Multiple Hazard-Avoidance Trajectories. Example: Nominal Flight Plan versus Weather and/or Traffic. Define Spatial Trajectory-Risk Functions. Normalize the Set of Evaluated Risks (0-1). Prioritize Individual Avoidance Trajectory Generators. Minimize Highest Risk, First, Then Lower Risks - Sequence.



SECTION I




SECTION II




SECTION III



SECTION IV




Intelligent Computing Techniques

Software Engineering Object-oriented programming

Data communications: TCP/IP Modular design Message Passing

Intelligent Agents model complex decision-making/protocols simulate autonomous behavior


Data Communications TCP/IP enables modularization & scalability (>1 machine) example: connect independent flight dynamics engines to

common server typically 10MB/s pass text strings (e.g. set/get state vars) buffering, blocking


SERVERCreate socket on port 5000

listen for connections

accept connection

read block of bytes

write block of bytes

CLIENT

Open Socket connection to 128.135.194.2, port 5000

write block of bytes

read block of bytes

128.135.194.2 128.135.194.3

packets

packets


Two Flavors: TCP vs. UDP specify type when create socket TCP

guaranteed delivery; messages arrive in order checked for errors

UDP non-guaranteed; not error-corrected much faster!

choice depends on tolerance of msg failures example: comm. protocol vs. graphics updates


Inter-Module Communications

SPiFI

FMS LOGIC

COCKPIT DATA SIM

Dashed lines denotevirtual connectivity.

HUDGEN

TCP/IP

DSPLYHNDLR

TCP/IP

CMDGEN

A/P

FCS

EQMO

DSPLYHNDLR

FLT SIM

PILOT

HDDLeft

HDDRight

HUD

FLTCTRLS

DSPLYHNDLR

JPSNDB

NAVMOD

FLTPLN

WXAGT

TFCAGT

SIMRADAR

SIMADS-B

SIMCPDL

FMS EXEC

DATAOBJ

PA FMI


Other Communications Tech.

CORBA - industry standard www.omg.org/gettingstarted/corbafaq.html

network/object-oriented, location-independence Interface Definition Language: classes, methods remote method invocation (lang/OS independent)

HLA - High Level Architecture www.dmso.mil/public/transition/hla

standard for mil/gov simulations federations, broadcasts, time management objects, interactions SOM/FOM - interface definitions, methods


XML Data representation format

for scenarios, msgs/cmds, config, logs, etc.

Define tags (like HTML) <events>

<simevent id=“54321” time=“1:21”>

<type>request_takeoff_clearance</type>

<aircraft>US789<aircraft>

<location>KDEN</location>

</simevent>

<simevent>

...

</simevent>

</events>


Generic parsers available Xerces - Java, C++

Apache: xml.apache.org/xerces2-j IBM: www.alphaworks.ibm.com/tech/xml4j

SAX => incremental, parse when needed DOM => batch, produce “object trees”

root Doc node

Element: typetext=takeoff

Element: locationtext=KDEN

Element: aircrafttexst=US789

Element: simevent attribute: time=1:21 attribute: id=54321

Element: simevent attribute: time=1:35 attribute: id=54322

(list of child nodes...)


What are Agents?

Essential Characteristics: Situated

can sense and take actions in dynamic environment

Goal-oriented Autonomous Social/collaborative Adaptive


Agent Architectures Production Systems

Reactive, trigger rules, CLIPS, SOAR

Search Algorithms: A* (WX agent) Planning Algorithms Hierarchical Task Networks (Retsina, TRL) Decision Theoretic

Markov Decision Processes, maximize payoff

Cognitive (Mentalistic) BDI: beliefs, desires, intentions JACK, PRS, dMARS


Roles for Agents in Aviation

Simulate other aircraft, controllers In cockpit: planning flight path, managing fuel,

maintaining stability of flight, monitoring traffic or weather conflicts…

On ground (TRACON, ARTCC): planning trajectories, resolving conflicts, approach metering, handling emergencies, coordination with ground ops, airlines, etc.


Collaboration Models Teamwork

Hierarchical vs. distributed (command vs. consensus) Key concepts: roles and responsibilities Shared plans: implicit coordination, synchronization Theoretical basis: Joint Intentions

Negotiation protocols Distributed Constraint Satisfaction Share justifications and beliefs to determine compromise Monotonic Concession Protocol

Auctions Bids based on marginal utility Contract networks


Intent – transmit more than position/vector Desire to avoid weather, flight plan, will be turning north, descending due

to turbulence, reason for deviation…

Beliefs shared info (weather, congestion, aircraft emergencies) common picture of situation common knowledge: STAR’s, fixes, active runways, traffic patterns manage uncertainty

Role of Simulated “Mental Attitudes”


Concepts for Development of Multi-Agents for Free Flight

Strategic (trajectory planning/management) vs. Tactical (avoidance maneuvers)

Actionable decisions: Alter flight path: heading, altitude, speed

Factors: weather, terrain, traffic Constraints: fuel, speed/alt range Preferences: time, fuel cost, comfort


Utility function: Flight Plans => score Negotiation by “argumentation”

State what is wrong with proposed solution and why Communicate preferences as well as constraints

make up when behind schedule

minimize fuel consumption

maneuver limitations (safety, comfort)

Monotonic Concession Protocol (Rosenschein and Zlotkin) define a finite set of alternative trajectories each agent ranks trajectories by utility, proposes best take turns proposing next best deal till utilities match

Negotiation


w eather,terrain,traffic info.

conflic tdetection

conflic t detected?

ident ify &contact otheraircraft (bogey)

generatealternativetrajectories

rank themby utility

exchangecandidatesolutions w ithbogey

proposehighest-rankedsolution forow nship

receive counter-proposal from bogey

is utility of bogey’ssolution>=ow nship’s

confirmagreement on lastproposal

inform ATC

propose next-bestsolution for ow nship

are therealternatives left?

ask ATCfor help

yes

yes

yes

no

nono

Negotiation Flowchart


TRL Agents “Task Representation Language” Developed at Texas A&M Comp. Sci. Dept.

contact: [email protected]

knowledge bases declarative: rule base (“domain knowledge”) procedural: plans/methods for achieving goals

connection to simulator read state information trigger actions

agents can communicate with each other


TRL agent

sensingmessage

s

JARE KB: facts &Horn-

clauses

Simulation

operators

results

assert, query,retract

messages

APTEAlgorith

m

TRL TaskDecomposition

Hierarchy

ProcessNets

Other Agent

s

TRL KB:tasks &methods

TRL Agent Architecture


Example Task Description

(task flight-plan-1 ()

(method

(sequence (takeoff KCLL 16) (climb-out 3000)

(turn-heading 350)

(fly-direct-to KCNW)

(descend 500)

(land KCNW 17L))))

invokesub-task

command tosimulator

Things to add: • interaction with ATC (set new way-points, altitudes...)• handling developing weather (while (not cloudy)...)


The SATS Airport Controller

How to simulate this with agents? encode the formal protocol as plans in TRL

AMM agent - simple task, 1st come-1st serve pseudo-ADS-B=TCP/IP, test robustness of protocol w.r.t.

communications failures test empirically with various scenarios

arbitrary number of aircraft effects of timing, positions, speeds... test handoff from ATC, entry to SCA arrival/departure frequencies (Poisson distr.)



SECTION I




SECTION II




SECTION III



SECTION IV




real-time flight simulator

FLIGHT SIMULATION SYSTEM

Fixed-base: Commander 700; AV-8A Harrier, F-5A Freedom Fighter SGI Onyx Reality II sim engine

Networked bank of PC’s

Center stick; sidestick

155o projected field of view 30 Hz refresh rate

Programmable Head Up Display



Moving Map

NAV Display FMS & Autopilot Interface

Touch--Sensitive Screen

Gear

Handle

Head Down Displays (HDD) Reconfigurable

CRT; touchscreen LCD

Autopilot Glide slope capture

Heading

Altitude

Pitch attitude

Flight Management System (FMS) Jeppesen data base

Pre-flight planning and enroute updating

Moving map display


ISIC-2003-54 Valasek, Painter, Ioerger

HARDWARE ARCHITECTURE

Aikman WarnerFavre

VideoSignalOutput

VideoSignalOutput

TCP/IP UDP

GAPATS & Agent System PC

SPiFI PCSimulation and External Display SGI Onyx Reality II

Flutie

Bledsoe

Gannon



SOFTWARE ARCHITECTURE

SGI Machine - UnixMultiple PCs - Windows

HEAD UPDISPLAY

HEAD DOWNDISPLAY-L

FLIGHTINTERP

DATAOBJECT

TCPIP

GAPATS.CPP(GAPATS.H)

AGENTDATAOBJ

WEATHERAGENT

RADARDATA (SIM)

TRAFFICAGENT

ADS-BDATA (SIM)

AGENTEXECUTIVE

FCS

COMGEN (1)COMGEN (2)

TRAG

ROLCASPITCAS

YAWCAS

EFS Dynamics

PROJECTED3-SCREENDISPLAY

ENVIRONMENT

EFS EXECUTIVE

PilotFlight

Controls

NAV

PILOTADVISOR

SPIFI

FLIGHT PLANAGENT

HEAD DOWNDISPLAY-R

SGI Machine - UnixMultiple PCs - Windows

HEAD UPDISPLAY

HEAD DOWNDISPLAY-L

FLIGHTINTERP

DATAOBJECT

TCPIP


AGENTDATAOBJ

WEATHERAGENT

RADARDATA (SIM)

TRAFFICAGENT

ADS-BDATA (SIM)

AGENTEXECUTIVE

FCS


TRAG

ROLCASPITCAS

YAWCAS

EFS Dynamics


ENVIRONMENT

EFS EXECUTIVE

PilotFlight

Controls

NAV

PILOTADVISOR

SPIFI

FLIGHT PLANAGENT

HEAD DOWNDISPLAY-R

HEAD UPDISPLAY

HEAD DOWNDISPLAY-L

FLIGHTINTERP

DATAOBJECT

TCPIP


AGENTDATAOBJ

WEATHERAGENT

RADARDATA (SIM)

TRAFFICAGENT

ADS-BDATA (SIM)

AGENTEXECUTIVE

FCS


TRAG

ROLCASPITCAS

YAWCAS

EFS Dynamics


ENVIRONMENT

EFS EXECUTIVE

PilotFlight

Controls

NAV

PILOTADVISOR

SPIFI

FLIGHT PLANAGENT

HEAD DOWNDISPLAY-R

HEAD UPDISPLAYHEAD UPDISPLAY

HEAD DOWNDISPLAY-L

HEAD DOWNDISPLAY-L

FLIGHTINTERPFLIGHTINTERP

DATAOBJECT

DATAOBJECT

TCPIPTCPIP



AGENTDATAOBJ

AGENTDATAOBJ

WEATHERAGENT

WEATHERAGENT

RADARDATA (SIM)

RADARDATA (SIM)

TRAFFICAGENT

TRAFFICAGENT

ADS-BDATA (SIM)

ADS-BDATA (SIM)

AGENTEXECUTIVE

AGENTEXECUTIVE

FCS


TRAG

ROLCASPITCAS

YAWCAS

EFS Dynamics


ENVIRONMENT

EFS EXECUTIVE

PilotFlight

Controls

PilotFlight

Controls

NAVNAV

PILOTADVISOR

PILOTADVISOR

SPIFI

FLIGHT PLANAGENT

HEAD DOWNDISPLAY-R

SPIFI

FLIGHT PLANAGENT

FLIGHT PLANAGENT

HEAD DOWNDISPLAY-R

HEAD DOWNDISPLAY-R


SIMULATION SYSTEM SOFTWARE COMPONENTS

Aircraft model

• Six-degree-of-freedom dynamics model

• CD & R model

• FMS model (including Autopilot model)

• Pilot model

• ADS-B model • Additional research model

Flight Objectives Pilot

ADS-B

Flight PlanDynamics

Model

FMS(Autopilot)/NavigationCD & R Model

Other Research Models

Aircraft Trajectory

SurveillanceControllerCommunication



Combined GAPATS/Agents system functions like a simplified FMS

Soft Pilot/FMS Interface (SPiFI)

SPiFI

GAPATS

Autopilot Command Generator

Tracker

Engineering Flight Simulator

Agent System

Pilot Commands

SPiFI

GAPATS

Autopilot Command Generator

Tracker

Engineering Flight Simulator

Agent System

Pilot Commands


altitude

rate ofclimbairspeed

pitchangle

runwayoutline

localizerdata

glide slopedata

heading tape

currentwaypoint marker

text area

HEAD UP DISPLAY SYMBOLOGY


HEAD UP DISPLAY IN FLIGHT


night and weather



NAV/MAP DISPLAY SYMBOLOGY


CONTROL EFFECTORS

commercial air transport

outboard aileron

flight spoilersleading edge flaps

stabilator

outboard flap

inboard flap

throttle

Boeing 777-300

upper and lower rudders

ground spoilers

inboard aileron

primary controlssecondary controls


LIFT AND DRAG FORCESdefinitions

Grumman F11F-1 Tiger

Lift

U1

Drag

XB

ZB


Angle-of-Attack

Sideslip Angle

Grumman F11F-1 Tiger

AERODYNAMIC ANGLES

XI

XB

VP

VP

XB

definitions

Note: All Angles Shown Are Positive


BODY AXIS

Note: Positive Signs Shown

component definitions

Linear and Angular VelocitiesAerodynamic and Thrust Moments

Aerodynamic and Thrust Forces Acceleration of Gravity

Reference 2-1


EULER ATTITUDE ANGLESdefinition

Reference 2-2

yaw attitude angle

pitch attitude angle

roll attitude angle


EQUATION SUMMARYLinear Motion

"")(

"")(

"")(

EquationLiftFFmgVPUQWm

EquationSideforceFFmgWPURVm

EquationDragFFmgWQVRUm

zTzAz

yTyAy

xTxAx

Drag Equation

Sideforce Equation

Lift Equation

Angular MotionAssuming the x-z plane is plane of symmetry, i.e., 0 yzxy II

TAxzxxyyxzzz

TAxzzzxxyy

TAyyzzxzxzxx

NNQRIPQIIPIRI

MMRPIPRIIQI

LLRQIIPQIRIPI

)(

)()(

)(

22

Rolling Moment Equation

Pitching Moment Equation

Yawing Moment Equation


FORCES AND MOMENTSshorthand notation

Dividing by mass, each term becomes a longitudinal linear or angular acceleration.

Letting , dimensional derivatives are the linear

or angular acceleration per change in the associated motion variable.

is the pitch angular acceleration imparted to the airplane as the result of a unit change in angle-of-attack.

There will be separate equations for the aerodynamic and thrust forces and moments.

FEqu

YY

FEquZ

FEquX

FE

FE

FE

MMqMMMuMmI

ZZqZZZuZfm

XXqXXXuXfm

1

1

1

M

variablemoment 1

momentvariable m


S & C DERIVATIVESwind tunnel testing

OPTIONS Static testing

Dynamic testing

DIFFICULTIES

Tests are not dedicated to estimation of performance

Reynold’s number effects on drag dependent terms

Cost of testing

Wind tunnel availability


208

20

20

20

50

40

10

10

1

9

6

5

10

1

7

Estimated Prediction

Accuracy**

Relative

Importance*Derivative

203

208

404

± 5%10

CDα

Cmα

CLα

CLα

Cmα

CDα

CLu

Cmu

CDu

qCL

qmC

qDC

* 10 = Major, 5 = Minor, 0 = Negligible** Using theoretical methods. With use of tunnel data, better accuracy can be achieved

relative importance and prediction accuracy

STABILITY DERIVATIVES

259

407

304

908

1510

504

604

602

602

1510

2010

±20%7

Estimated PredictionAccuracy**

RelativeImportance*Derivative

CyβClβ

Cnβ

Cn

Cl

yC

CypClp

nCp

nCp

lCp

yCp

Reference 2-1


EQUATIONS OF MOTIONobservations

The Equations of Motion are a set of 9 differential equations: First order

Nonlinear

Coupled

Ordinary

The Variables are:

The aerodynamic and thrust Forcing Functions:

are functions of: Velocity

Angle-of-attack

Sideslip angle

TATATATATATA NandNMMLLFFFFFFzzyyxx

,,,,,,,,,,

and , R, Q, P, W,V, U,

Time

Altitude

Configuration


Nonlinear

Coupled

Ordinary

The Variables are:



Angle-of-attack

Sideslip angle


,,,,,,,,,,



Nonlinear

Coupled

Ordinary

The Variables are:



Angle-of-attack

Sideslip angle


,,,,,,,,,,


Time

Altitude

Configuration


AIRCRAFT DYNAMIC MODESstandard longitudinal modes

SHORT PERIOD

The primary and most useful standard longitudinal dynamic mode. Second-order Stable, or unstable High frequency, well damped Exhibited mostly in angle-of-attack and body-axis pitch rate Specified in military flying qualities regulations Pitch maneuverability is based upon controlling and shaping this mode

speed remains constant,angle-of-attack and pitch attitude vary

Reference 3-1


AIRCRAFT DYNAMIC MODESstandard longitudinal modes

PHUGOID

The secondary standard longitudinal dynamic mode (nuisance mode). Second-order Stable, or unstable Low frequency, very lightly damped Exhibited mostly in velocity and pitch attitude angle Specified in military flying qualities regulations Name derived from the Greek word for fly: “phugos”

Reference 3-1

angle-of-attack remains constant,speed and pitch attitude vary


AIRCRAFT DYNAMIC MODESexample: Dutch roll mode

Reference 3-1


AIRCRAFT DYNAMIC MODESexample: roll mode

Reference 3-1


AIRCRAFT DYNAMIC MODESexample: spiral mode

Reference 3-1


FLIGHT CONTROL PROBLEM

statement

The aircraft flight control design problem is to develop and implement an algorithm that closes the loop between the sensors and actuators, such that the aircraft accomplishes its mission, as dictated in the requirements and mission specification, with flying qualities deemed acceptable to the pilot.


FLIGHT CONTROLLER DESIGN

methodology REQUIREMENTS DEFINITION

Based on intended mission and aircraft class military: specified entirely by Department of Defense; usually non-negotiable

civilian: specified jointly between customer and manufacturer; negotiable

Design iterations imply tradeoffs in requirements how do requirements translate into assumptions?

MODELING “Goodness”

origin

validity

assumptions

accuracy

suitability– dynamic order

– state-space or frequency domain?

Refinement


CLASSES OF CONTROLLERS

Notes: 1. Pilot inputs are outer-loop variables such as airspeed, heading, altitude, etc.2. Limited-authority system.

Autopilots

vehiclemotion

pilotcommanded

motionvariables

commandshaping

controlsvehicle

sensorscompensator

+-controller

Function: Provide pilot relief and special functions


TYPICAL SPECIFICATIONSG.A. autopilot

CONFIGURATIONS Clean: gear and flaps up

Climb: gear up, flaps down

Power Approach: gear down, flaps down

AIRSPEED COMMAND AND HOLD SYSTEM Input type: ramp

hold commanded airspeed: anywhere in the range 0 h 10,000 feet

maximum airspeed error: 2 KIAS

Maximum values: 70% of maximum Rockwell Commander C700

T

CONFIGURATIONS Clean: gear and flaps up

Climb: gear up, flaps down

Power Approach: gear down, flaps down

AIRSPEED COMMAND AND HOLD SYSTEM Input type: ramp

hold commanded airspeed: anywhere in the range 0 h 10,000 feet

maximum airspeed error: 2 KIAS

Maximum values: 70% of maximum Rockwell Commander C700Rockwell Commander C700

T


TYPICAL SPECIFICATIONSG.A. autopilot

PITCH ATTITUDE COMMAND AND HOLD Input type: ramp Maximum positive commanded change in : 10 degrees at SLS altitude Maximum negative commanded change in : -8 degrees at SLS altitude 90% rise time on : 5 seconds Maximum overshoot on : 15% Maximum values:

5 degrees

ALTITUDE COMMAND AND HOLD Input type: ramp Commanded change in h: anywhere in the range 0 h 15,000 feet Maximum climb rate: Standard Instrument Climb of 500 feet/minute Maximum error: 50 feet of commanded altitude Maximum values:

5 degrees

e

e


Purpose: Maintain commanded pitch attitudeFeedback Variable(s): Pitch rate inner-loop and pitch attitude outer-loopTypical Sensor(s): Rate gyro and vertical gyroPrimary Control Variable: Elevator Typical Compensators: Gain; and second-order pole-zero canceling compensator

AUTOPILOTSpitch attitude command and hold

( )

c

e

e

qK

erefcee

Ke

q

1

S

q

verticalgyro


0 2 4 6 8 100

2

4

6Response to 5 deg theta command

[d

eg]

0 2 4 6 8 10-4

-2

0

2

4

q [d

eg/s

ec]

0 2 4 6 8 10-2

0

2

4

e a

ctua

l [de

g]

Time (sec)

AUTOPILOTSpitch attitude command and hold

Response to commanded 5 degree ramp in pitch attitude angle


Feedback Variable(s): Altitude, Pitch Attitude RateTypical Sensor(s): Barometric Altimeter, Rate GyroPrimary Control Variable: ElevatorTypical Compensators: Gain, Lead-Lag, Proportional Derivative

AUTOPILOTSaltitude command and hold

K

lead-lag

PD

elevatorservo

aircraftdynamics

h

ee

eaerefhH

he RGe

hK


AUTOPILOTSaltitude command and hold

PD = 10S+1

-100

300

700

hcom

(ft

)

0 25 50 75 100-100

300

700

time (sec)

h (f

t)

hdo

t (f

t/s)

-100

300

700

0 25 50 75 100-12

0

12

time (sec)

elev

ator

(de

g)


Case 1: Operation about a Single Trim Condition

The following controller modes operate about a single trim condition:Yaw DamperPitch DamperRoll DamperWing LevelerHeading HoldVelocity Hold

Operational Procedure:The pilot establishes the desired trim condition and engages the mode. If he wishes to change the trim condition, he disengages the mode,establishes the new trim condition, and re-engages the mode.

IMPLEMENTATIONtrim


Case 2 Automatic Transition between Two Trim Conditions

Flight controllers that require an automatic transition between twotrim conditions include:

Velocity Command Rate of Climb CommandGlide Slope Capture and TrackingTurn Rate Control

Operating Procedures:The flight control system must be able to automatically handle trim changesas well as changes in the vehicle’s open-loop dynamics.

IMPLEMENTATIONtrim



SECTION I




SECTION II




SECTION III



SECTION IV




AGENT BASED HIERARCHICAL SYSTEM

Overall Structure of Hierarchical Agent System

Executive Agent

Traffic Agent

Weather Agent

Flight

PlanInfo.

WeatherRadar Data

ADS-B

Ground Weather Service

Other Weather Info. ...

ATCRadar

Other Traffic Info...


WEATHER AGENTobjective : safest and shortest route

End point

Starting point

L

L = segment length

= turning angle

OTHER CONSTRAINTS Minimum segment length

length of any segment in flight path cannot be less than this

Maximum turning angle turns of angles greater than this

are not allowed

Minimum number of turns

METHOD USED Modified A* Search

Regions with intensity greater

than 25 dBZ are set as inaccessible


Inputs are ADS-B state vectors of aircraft in immediate airspace

Detects potential traffic conflicts Non-Cooperative agent Only considers aircraft in alert zone Overlap of protected zones prohibited Size of zones determined by several spatial or temporal factors

Calculates evasion maneuvers to avoid other protected zones Knowledge based expert system and optimal control

TRAFFIC CD&R AGENT

Protected Zone

Alert Zone2 2

1 2 1 2

1 2

( ) ( )h

v

R x x y y

R z z

IF Rh <Rhp .AND. ABS(Rv)<Rvp

THEN avoidance=.TRUE.

ELSE avoidance =.FALSE.


EXECUTIVE AGENT

Arbitrator between lower-level agents

Intelligent behavior Fuzzy synthesis

evaluation method

Determines ultimate flight

guidance submitted to: FMS

Autopilot

Pilot

Traffic Conflict

Evaluation

Weather Agent

Traffic Agent

Flight Management System

Weather Conflict

Evaluation

Weather Radar Image

Traffic Situation

TC PathSelection

AgentsController

Rule-Based Arbitrator

Traffic Conflict

Evaluation

Weather Agent

Traffic Agent

Flight Management System

Weather Conflict

Evaluation

Weather Radar Image

Traffic Situation

TC PathSelection

AgentsController

Rule-Based Arbitrator


CONCLUSIONCONCLUSION

So, You Really Think …

That Software …

Can Fly …

An Airplane ?

Yeah, Right !!


FLIGHT SIMULATION LAB

points of contact

Director

John Valasek, Ph.D.

Aerospace Engineering Department

Texas A&M University

3141 TAMU

College Station, TX 77843-3141

(979) 845-1685

[email protected]

FSL Web Page http://flutie.tamu.edu/~fsl

Thomas R. Ioerger, Ph.D.

Computer Science Department

Texas A&M University

3112 TAMU

College Station, TX 77843-3112

(979) 845-0161

[email protected]

Web Page http://faculty.cs.tamu.edu/ioerger/

ISIC-2003-1 Valasek, Ioerger, Painter SOFT COMPUTING IN THE SMART COCKPIT A Workshop The 2003...

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Transcript of ISIC-2003-1 Valasek, Ioerger, Painter SOFT COMPUTING IN THE SMART COCKPIT A Workshop The 2003...