OGI Model-Relative Control of Autonomous Vehicles A project of DARPA’s Software-Enabled Control program John Bay, Program Manager Principal Investigator:

OGI

Model-Relative Control of Autonomous Vehicles

A project of DARPA’s Software-Enabled Control program

John Bay, Program Manager

Principal Investigator: Richard KieburtzCo-PI’s: Eric Wan, Antonio BaptistaOGI School of Science & Engineering,Oregon Health & Science University

Contracting Agency: AFRLContract No. F33615-98-C-3516

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Collaborators and Subcontractors

Collaborators Boeing Phantom Works, UC Berkeley, Georgia

Tech, Honeywell Operational requirements of the OCP

University of Washington/Cornell University, MIT State and parameter estimation

Subcontractor — MIT will furnish an instrumented, flight-ready model helicopter,

enabling OGI to conduct experimental flight tests of SDRE and MPNC control

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Project ObjectivesDevelop environmentally-informed (EI) control algorithms suitable for automated aircraft avionics and flight control under all-weather conditions

Host control algorithms on OCP middleware to achieve platform independence and portability (de-emphasized)

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Project Status (Update) -Technical

Environmental Scenario: Atmospheric Microburst Incorporating realistic simulated wind fields Effects of wind approximations on MPNC

Environmental Scenario: Urban flight example High-resolution modeling

Landing on a moving platform Simulated motion and trajectory optimization

Landings on a simulated ship’s flight deck FlightLab version upgrade and ship modeling.

Progress towards flight experiments with a small helicopter (X-Cell60)

Configuration and assembly

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Environmental Scenarios: Atmospheric microburst

Atmospheric Large-Eddy Simulation (LES) Model Designed for the study of small-scale atmospheric flows

(e.g. cumulus convection, entrainment, turbulence) Calculates wind velocity fields from physical model and

boundary conditions, non-hydrostatic, fully 3-dimensional, quasi-compressible

Microburst Simulation Grid resolution: 20 m

Subgrid-Scale Turbulence Finer resolution: down to 1 cm.

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Gust front: horizontal velocity (vertical cross-section)

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FlightTrajectory (75 ft/s)

Horizontal windVelocity(ft/s)

Simulated Flight through Gust Front with MPNC

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Test of approximations for incorporating wind information for MPNC training

Training scenario Vehicle speed

60 ft/s 80 ft/s

1 (no-wind info) 49.46 178.02

2 (constant wind) 28.32 57.16

3 (resolved) 26.51 38.61

4 (actual) 24.23 48.75

SDRE 111.49 162.69

(straight flight through the gust front, wind speed 25-65 ft/s)

MPNC Cost comparisons

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Environmental Scenarios: Urban Flight

Large-Eddy Simulation Use of compressible formulation allows for inclusion of flow obstructions Grid resolution: 2 m Applied wind accelerated to approximately 6 m/s in free air

Maximum reversal velocity of approx 6 m/s between buildingsMaximum absolute velocity of approx 12 m/s

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Horizontal (E-W) velocity (horizontal cross-section at z=30m)

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Horizontal (E-W) velocity (vertical cross-section at y=160m)

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SDRE, cost = 46.61

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MPNC, cost = 15.34

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Landing on a moving platform

Platform motion (lift and roll):

Desired Trajectory: Linear superposition of standard landing trajectory with position and

attitude of platform

Aerodynamic modeling: Current model assumes ground forces associated with a horizontal

(moving) platform

MPNC training for soft landing: Added NN inputs associated with vertical force in landing gear and its

deviation from desired curve. Quadratic cost associated with the force deviation from the desired

curve is minimized

sin 2 cos 2deck z deckz A t A t

5 ., 11.54 ., 0.1zA ft A deg

(details)

(details)

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SDRE landing, cost = 3858.1

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Vertical forces in landing gear and suspension travel

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MPNC landing, cost = 1645.3

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Vertical forces in landing gear and suspension travel

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Ship landing simulation in Flightlab

Recent Flightlab enhancements

Ship airwakeRotor induced flow

Ground vortex

Rotorcraft/ship interaction

• ship dynamics modeling• excludes complex wave forcing

• ship airwake model• empirical or panel method

Aerodynamic interaction

• wing: enhanced horseshoe model• fuselage/wing: panel model• ground: panel model

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Ship landing simulation in Flightlab

Approaches to helicopter-ship airwake interactions

A. Closely coupled model the ship airwake is modeled with panel method

allows proper boundary conditions on the ship deck and helicopter does not allow flow separation limits accuracy near the ship superstructure

approach is computationally expensive for real time simulation

B. Loosely coupled model ship airwake is computed from an accurate numerical model (e.g., LES model) ship airwake data is then applied for rotor/fuselage airloads calculation (e.g., via

table look-up or our own customized turbulence module) the effect of rotor on the ship airwake is neglected (a one-way aerodynamic

interaction)

approach is computationally efficient for real time simulation

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Flight Experiment with X-Cell60 Platform

MIT Subcontract to assemble and test Assembly currently in progress May 3: delivered simulator to OGI. July 15: expected completion and delivery to OGI

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Project Status - Next Milestones

Evaluate robustness of SDRE and MPNC algorithms by simulated flights through “microburst” wind fields (31 Mar 2002)

Simulate landing of a helicopter under autonomous control on a moving deck Without atmospheric disturbances (30 Apr 2002) With modeled, turbulent wind field (15 Jun 2002)

Assembly of X-Cell helicopter and avionics package Subcontract to MIT (31 May 2002 -> 15 July 2002)

Hardware-in-the-loop tests of autonomous SDRE control system (30 Jun 2002 -> 15 Aug 2002)

Flight test model helicopter to gather data for off-line parameter estimation (31 Jul 2002 -> 1 Sept 2002)

Flight simulation with X-Cell .60 flight dynamics model (31 Jul 2002 <- 1 June 2002)

Initial Flight test maneuvers with SDRE control (31 Aug 2002 -> 31 Sept 2002)

Flight test aggressive maneuvers with SDRE control (30 Sep 2002 -> 1 Nov 2002)

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Project PlansNext 6 Months:

FlightLab/Ship Simulation: Evaluation of closely and loosely coupled models

Determine impact on control algorithms

External specification of ship motion under complex wave forcing Empirical data for ship motion anticipated

Simulations of Helicopter landing on Ship Determination of optimal trajectories and control archtecture.

Modification of Control Algorithms to work with MIT X-Cell Helicopter Model

X-CELL Preparation Assemply, Flight-Test, Paramater ID, HWIL Sim, (etc)

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Project Plans

“Mid-Term” flight experiments with onboard SDRE control of an X-Cell .60 helicopter will demonstrate

Automatic control of maneuvers: Hover in fixed position Recover from instability to hover. Takeoff, translation and landing Sharp 90° and 180° turns at various airspeeds “Elliptic” turn in straight line flight Tracking a commanded flight path

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Project Plans (cont’d)

(Contract Option)

“Final Exam” demonstration Demonstrate model-predictive, SDRE Control of complex

maneuvers on GaTech flight platform (Yamaha R-Max helicopter)

Import R-Max flight dynamics model for use with OGI control design suite

Design SDRE controller with R-Max sensors and actuators Host SDRE control software on OCP Simulate specified maneuvers (takeoff, path following, landing) Flight tests with the R-Max will duplicate basic maneuvers

demonstrated in the X-Cell 60 flight tests Host MPNC control software on OCP

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Project Plans (cont’d)Additional experiments with the X-Cell flight platform

SDRE: Landing on a 15° inclined ramp SDRE: 360° roll in horizontal flight MPNC: control of pre-planned maneuvers (offline training)

Additional simulation experiments Robustness of control

Introduce errors in airframe mass and other model parameters Continued evaluation of wind approximations on control optmization

Integrating atmospheric modeling with a FlightLab helicopter model If necessary: consider a closely coupled LES model for environment,

ship, and helicopter Robustness of maneuvers in turbulent wind fields Landing on a ship’s deck in rough sea and weather conditions

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Project Schedule

0 5 10 15 20 25 30 35 40

Control Algorithms

Software platform

Environmental models

Maneuver design

Flight dynamics models

Flight Experiment

MIT subcontract

MonthsMay 1

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Technology TransitionValidating the control technology

Flight tests to evaluate SDRE and MPNC control technology on a high-performance rotorcraft

Portability of the software technology Phased transition to a middleware base on OCP/Build 2

ONR FNC UAV Autonomy program Contract: “Sigma Point Kalman Filter Based Sensor

Integration, Estimation and System Identification for Enhanced UAV Situational Awareness & Control”, PI:Wan

Uses SPKF (UKF) for state and model estimation. OGI’s helicopter simulator (FlightLab) and control system to be used for

testing prior to transitioning to ONR’s VTAUV vehicle.

“Final Exam” demonstration (Contract Option) Demonstrate model-relative, SDRE Control of complex maneuvers

on GaTech flight platform (Yamaha R-Max helicopter)

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Program Issues

Probable 6-week delay of Mid-term experiment results until October 31, 2002 caused by late arrival of FY2002 funding increment and delay in MIT subcontract.

End

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Extras

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Wind Approximations in MPNC Training

Approximations for incorporating wind information for MPNC training:1. MPNC trained with no information available about the wind.

2. MPNC trained using constant wind fields as measured from the start of each horizon.

3. MPNC trained using resolved wind field (turbulence is assumed unknown and neglected for training purposes).

4. MPNC trained using knowledge of both resolved and actual turbulent wind flow (ideal case).

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Desired trajectory generation (landing phase)1. Smooth desired approach generated as for landing on a fixed flat

surface, 2. Desired descent curve is generated taking into account platform

vertical motion (assuming Z pointing downwards):

where and are annealed from 0 to 1. This allows gradual smooth transition to the landing phase and approach to the moving platform.

3. To provide leveled landing by matching the platform attitude, desired pitch and roll are generated as functions of the platform’s pitch and roll and the aircraft attitude. Given coordinates of the normal vector to the platform

4. To eliminate discontinuity in desired roll and pitch, they are annealed using :

des z des deck zz A z z A

( )desz t( )desz t

Tdeck x y zN n n n

cos sin

arctancos sin sin cos

y xdes

x y z

n n

n n n

cos sinarctan x y

desz

n n

n

des des des des

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MPNC training

Direct signal flow diagram

NeuralNetwork

1

( )kK x

Helicopterdynamics

q 1

Neural control

SDRE control

VehicleTarget

Tk ke Q e

overT over Tk sat k k ku R u u Ru

t NV x

kxke

kx

kx

1kx

nnku

sdku

kx

deskx

tarkx ke

kT x

ku

sin( )

cos( )

, ,k k k

1kF

deskF kF q 1

Fke

FT Fk F ke Q e

FT Ft N F t N e Q e

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MPNC training

Adjoint system

NeuralNetworkJacobian

1

HelicopterJacobians

1q

Neural control

SDREJacobian

Vehicle

1k

k

( )kk

k

K x

ex

,t N t N

t N

V

x e

x

2 overT Tk sat ku R u R

kdx

kdunnkdu

nnkdx

nnkde

nnkde

kde

sdkdu

deskdx

nnkdx

2 Tke Q

k tar

k k

k

T x x

x

cos( )

sin( )

( )kK x

1q

2 FTt N Fe Q

1Fk

Fk

2 FTk Fe Q

Fkde kdF

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SDRE, cost = 46.61

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Horizontal (E-W) velocity (horizontal cross-section at z=30m)

Documents

OGI Model-Relative Control of Autonomous Vehicles A project of DARPA’s Software-Enabled Control program John Bay, Program Manager Principal Investigator: