This presentation is given at a NASA DLR (German Aerospace Center) meeting at NASA ARC on March 14, 2017. The presentation provides an overview of the Advanced Control and Evolvable Systems (ACES) group at NASA ARC and the research areas in UAS autonomy, stall recovery guidance, and flexible aircraft flight control

Ippolito, Corey

Lombaerts, Thomas

Nguyen, Nhan

Swei, Sean

NASA Technical Reports Server

1 
National Aeronautics and Space Administration
Advanced Control and Autonomy Research
Dr. Nhan Nguyen
Technical Group Lead
Advanced Control and Evolvable Systems (ACES) Group
Intelligent Systems Division
NASA Ames Research Center
Moffett Field, CA
 
 
NASA – DLR Meeting at NASA Ames 
March 14, 2017 
https://ntrs.nasa.gov/search.jsp?R=20170010165 2019-08-31T01:49:25+00:00Z
2 
 
Outline  
 
•  Overview of ACES Group – Dr. Nhan Nguyen
•  UAS Autonomy Research – Dr. Corey Ippolito
•  Stall Recovery Guidance Research – Dr. Thomas Lombaerts
•  Flexible Aircraft Flight Control Research – Dr. Sean Swei
3 
Advanced Control and Evolvable Systems Group 
•  Advanced Control and Evolvable Systems (ACES) Group (21 people) 
within the Intelligent Systems Division (code TI) conducts advanced GNC 
research and multidisciplinary vehicle dynamic modeling and simulations
•  > 90% of research are 
aeronautics with some 
space-related GNC
•  Collaborate with other NASA centers (AFRC, LaRC, GRC), other 
government agencies (FAA, DHS), industry (Boeing and small business 
companies), and academia (U.S. universities and TU Delft)
4 
Intelligent Adaptive Flight Control 
•  Core expertise of ACES group
Intelligent Flight Control System (IFCS) 
2003 – 2006 
•  Sigma-Pi neural network MRAC (Model-
Reference Adaptive Control) 
•  Team: NASA AFRC, NASA ARC, IV&V, 
Boeing 
Integrated Resilient Aircraft Control 
(IRAC) 2007 – 2011 
•  NASA simplified MRAC / optimal control 
modification  
•  Team: NASA AFRC, NASA ARC 
Intelligent Adaptive Flight Control 
Adaptive Control & Guidance for Vehicle Autonomy 
Adaptive Control 
& Guidance 
Mission Planning 
& Scheduling 
Decision-Making / 
Full Autonomy 
In
cr
ea
si
ng
 
A
ut
on
om
y 
Adaptive control as enabler 
for vehicle autonomy 
 
•  Adaptation through closed-loop 
control and mission management 
•  Integration with vehicle adaptive 
physical hardware & software 
•  Interactions with other domains 
(human-machine interactions, 
prognostics, etc.) 
Intelligent Flight Planning UAS Decision-Making 
Autonomy 
Overview		
Conduct	mul+disciplinary	research	to	develop	
advanced	technologies	for	wing	shaping	control	
to	reduce	fuel	consump+on	and	improve	safety	of	
high	aspect-ra+o	ﬂexible	wing	transport	aircra<	
Impacts	
•  1%	–	6%	drag	reduc+on	which	could	translate	
into	as	much	as	$0.2B	–	$1.3B	fuel	savings	
(www.transtats.bts.gov/fuel.asp)	
•  Reduce	aircra<	weight	and	gust	loads	
•  Improve	passenger	comfort	and	enable	safe	
opera+on	of	ﬂexible	wing	transport	aircra<	
Collabora3on	
NASA	AFRC,	NASA	LaRC,	Boeing,	Scien+ﬁc	
Systems	Company	Inc.,	U	of	Washington,	Wichita	
State	U,	Technical	University	Del<	
Variable	Camber	Con3nuous	Trailing	Edge	
Flap	(VCCTEF)	Drag	Reduc3on	Technology	
Mul3-Objec3ve	Wing	Shaping	Flight	Control	System	
Flexible	Wing	Modern	
Transport	AircraI	
Integrated	Vehicle	Mul3disciplinary	Research	Capabili3es	
Wind	Tunnel	Valida3on	
X-56A	Collabora3on	with	NASA	AFRC	Aeroservoelas3city	(ASE)	Modeling	
Performance Adaptive Aeroelastic Wing (PAAW) 
Mission	Adaptive	Digital	Composite	Aerostructure	
Technologies	(MADCAT)	
Objective:		Develop	a	revolutionary	aerostructure	concept	by	combining	the	lattice-based	discrete	construction	and	the	multi-objective	optimal	8light	controls	to	realize	mission	adaptive	and	aerodynamically	ef8icient	future	air	vehicles.		
Approach:	To	leverage	emerging	digital	composite	manufacturing	and	fabrication	methods	and	utilize	the	“building-block”	strategy	to	build	high	stiffness-to-density	ratio,	ultra-light	aerostructures	that	can	provide	mission	adaptivity	for	varying	8light	conditions.		
Funding	&	Duration:	This	project	is	funded	by	ARMD	CAS	Project.	FY16-FY17.			
Status:	A	scaled	UAV	model,	capable	of	wing	morphing,	was	built	and	8light	tested	successfully.	
! N719NU	
Safe, Autonomous, and Routine 
Operation of Small-UAS in 
High-Density Low-Altitude Urban Environments 
Corey Ippolito, Kalmanje Krishnakumar 
December 5, 2016 
SAFE50 Project Overview 
Purpose 
•  Investigate onboard vehicle autonomy for safe, autonomous and routine 
small-UAS operations in high-density low-altitude urban environments, from 
the viewpoint of regulatory stakeholders and traffic system operators such as 
UTM. 
Goal 
•  Develop vehicle-centric autonomy requirements allowing safe operations 
•  Investigate the trade-space (including capabilities, challenges, implications, 
and alternatives) 
•  Incorporate and disseminate into UTM for TCL-3, TCL-4 
Approach 
•  Top-down analysis, requirements-driven approach 
•  Develop prototypes, perform simulation and flight testing experiments, work 
with external partners 
 
9 
Motivating Scenario and Characteristics 
10 
Through out the day, thousands of operators and vehicles utilize the 
airspace above the city.  UAS requests appear randomly throughout 
the day, requesting navigation between random location/address within 
in the city. 
•  Urban canyons 
•  Constrained 
spaces 
•  High-density 
•  High-demand 
•  Large-scale 
concurrent 
operations 
•  Operating over 
high-valued assets 
Scope and Definitions 
•  Routine Operations 
•  Small Unmanned Aerial Systems (UAS) 
–  Mass up to 25 kg to 150 kg * 
–  Airspeed up to 40 m/s * 
•  Low-Altitude Operations 
–  Altitude up to 200m or 400m ** 
•  High-Density Urban Environments 
–  “Non-trivial density” of humans, human structures, infrastructure, and competing air 
traffic 
–  Includes residential/commercial/high-rise buildings, towers, roads, bridges, 
railways, manned aircraft (particularly rotorcraft), and other UAS 
–  Density metrics to be defined, may include 
•  Population density greater than 1,000 people per square mile *** 
•  Perhaps a minimum threshold UAS capacity per city block, such as 10 UAS 
per city block 
11 
NASA UAS Classification Matrix* 
Type	 Model or sUAS	 sUAS	 UAS	
Category	 I (1)	 II (2)	 III (3)	
Weight Limit	 ≤ 25 kg	 25-150 kg	 > 150 kg	
Airspeed Limit	 ≤ 40 m/s	 ≤ 100 m/s	 > 100 m/s	
* per NASA NPR 7900.3C, Appendix I 
** per U. S. Class G Airspace, typ. below 700ft/1200ft 
*** per Geographic Areas Reference Manual (GARM), U.S. Census Bureau, 1994 
Challenges for High-Density Urban Operations 
•  Flight operations occur almost entirely beyond RF communications line-of-sight from 
ground operators to the vehicle. Limited point-to-point or satellite line-of-site. Need for 
Autonomy. 
•  Atmospheric uncertainties may have major impact on safety. 
•  Flight operations occur in a GPS-denied (or at-best a GPS-degraded) environment. 
Limited satellite line-of-site. 
•  Obstacles and hazards are not known with certainty ahead of time.  Autonomous 
onboard see-and-avoid may an onboard vehicle requirement. 
•  Vehicle system failures has major impact on safety (high failure rates, single-string 
architectures). 
•  Real-time ground-based surveillance is not easily accomplished.  Separation 
assurance may be an onboard vehicle autonomy requirement. 
 
12 
SAFE50 Vehicle Autonomy Requirements 
13 
Dynamic Ground 
Objects (DGO) 
Static Ground 
Objects (SGO) 
Other Aircraft Detect, Operate-
Near, and Avoid-
Endangering 
SGOs 
 Detect, Operate-Near, 
and Avoid-Endangering 
DGOs 
Hazard Footprint Awareness,  
Risk Minimization/Avoidance, 
Health Monitoring 
Detect, Operate-Near, 
Avoid-Endangering 
Other Aircraft 
UAS 
Environment 
Challenges 
Atmospheric 
Uncertainty 
Failures and 
Contingencies 
Degraded RF, SAT-
COM, GPS 
 
Winds and 
microbursts 
Avoid endangering 
objects in environment. 
•  Detect : equivalent to ‘see’ or ‘sense’, cooperative or noncooperative, technology limits, SWaP 
constraints, cost implications 
•  Operate-Near : more stringent than ‘avoid’ 
•  Avoid-Endangering : responsibility of risk and damage assigned to vehicles and operators 
Tall-Poles and Research Focus 
•  Detection of objects (static, dynamic, other air vehicles) 
•  Classification of objects (as needed to satisfy requirements) 
•  Relative control to objects 
•  Decision making under uncertainty 
•  Resilience to wind gusts and micro-weather effects  
•  Resilience to onboard failures 
•  Risk minimizing nominal/off-nominal control 
•  Alternative/augmentations to GNSS-derived position, navigation, 
and timing 
14 
Approach 
16 
sUAV incident 
High velocity 
region 
1. UrbanScape Wind Uncertainties 
2. GPS Denied/Degraded 
3. Static/Dynamic Obstacles 
Research Challenges 
17 
UrbanScape Wind Uncertainties 
Urban Architecture and CFD Simulation of Wind Profiles. 
18 
UrbanScape Wind Uncertainties 
GPS Denied/Degraded Navigation 
19 
20 
Powerline Identification and Reconstruction.  Raw LiDAR point clouds (left), voxel processing (middle), reconstructed powerlines (right), at 
20m (top) 
LiDAR Data and Voxel Representation 
Static/Dynamic Obstacles 
Test Environments 
Ames Roverscape Ames DART Facility Ames NUARC Facility 
21 
Stall Recovery Guidance
NASA-DLR	mee3ng	on	Unmanned	AircraI	research	topics	
Stefan	Schuet,	John	Kaneshige,	Thomas	Lombaerts,	
Kimberlee	Shish,	Vahram	Stepanyan,	Gordon	Hardy,				
Peter	Robinson		
NASA	Ames	Research	Center,	Moﬀe_	Field	
Structure
•  Introduc+on	
• Guidance	strategies	
• Display	
•  Simula+on	results	
•  Two	implementa+on	stages	
23/9	Intro	–	Guidance	strategies	–	Display	–	Results	–	Implementa+on		
Introduc4on
•  Loss of control in flight remains the most 
frequent primary cause of accidents 
•  Stall related accidents: Colgan Air 3407, 
AirAsia 8501, XL Airways Germany 888T, 
Air Algerie 5017, Air France AF447,… 
CAST studies:  
•  Effective Upset Prevention and Recovery Training 
•  Airplane State Awareness by aircrew (SE207) 
•  Algorithms and display strategies to provide control guidance for 
recovery from approach-to-stall or stall 
 
 
24/9	Intro	–	Guidance	strategies	–	Display	–	Results	–	Implementa+on		
Introduc4on
Research	subtopics,	based	on	CAST	direc+ves	on	safety	
enhancements:	
1.   Upset	preven3on	
•  Adap+ve	safe	ﬂight	envelope	es+ma+on	
•  Autoﬂight	trajectory	predic+on	and	aler+ng	
•  Adap+ve	envelope	protec+on	
2.   Upset	recovery	
1.  Stall	recovery	guidance 							 	current	research 		
2.  Unusual	abtude	recovery	
	
Previous	work	published	in	the	literature:		
adap+ve	envelope	es+ma+on	(AIAA-2013-4618/AIAA-2014-0268/
AIAA-2015-1546)	and	protec+on	(AIAA-2015-1113/AIAA-2016-0093)	
	
or
de
r	o
f	p
rio
ri+
es
	
previous	
research	
(2012-2015)	
25/9	Intro	–	Guidance	strategies	–	Display	–	Results	–	Implementa+on		
Sequence of events for stall recovery
aural	warning,	
s+ck	shaker,	
low	speed	buﬀe+ng	
∆h	
onset	to	stall	 stall	occurrence	 stall	recovery	
accelera+ng	dive	 pitch	up	 out	of	stall	
Decreasing	airspeed,	
increasing	angle	of	a_ack	
Speed	below	stall	speed,	
alpha	exceeds	stall	value	
Trade	al+tude	for	speed,	
poten+al	→	kine+c	energy	
Transi+on	to	level	ﬂight,		
avoiding	secondary	stalls	
or	overstressing	structure	
Establish	level	ﬂight	or	
climb	
FAA	stall	recovery	
template:	
1.  Disconnect	autopilot	
and	autothro_le/	
autothrust	
2.  Nose	down	un+l	stall	
indica+ons	eliminated,		
3.  Bank	wings	level,	
4.  Apply	thrust	as	needed	
5.  Retract	speed	brakes	
and	spoilers	
6.  Return	to	the	desired	ﬂightpath	
26/9	Intro	–	Guidance	strategies	–	Display	–	Results	–	Implementa+on		
Guidance strategies
Display
27/9	
3	strategies	under	considera+on:	
•  Fast	model	predic+ve	control	(AIAA-2017-1513)	
•  Energy	based	guidance	(AIAA-2017-1021)	
•  Constrained	control	approach	(AIAA-2016-0878)	
Intro	–	Guidance	strategies	–	Display	–	Results	–	Implementa+on		
Simula4on results
28/9	
Angle	of	a_ack	and	calibrated	airspeed	 Energy	transfers	
Intro	–	Guidance	strategies	–	Display	–	Results	–	Implementa+on		
Two implementa4on stages
Advanced	Controls	
Technologies	(ACT)	lab	
Ver3cal	Mo3on	Simulator	
(VMS)	
29/9	
Fall	2016	 Spring	2017	
Intro	–	Guidance	strategies	–	Display	–	Results	–	Implementa3on		
Stall Recovery Guidance
NASA-DLR	mee3ng	on	Unmanned	AircraI	research	topics	
Stefan	Schuet,	John	Kaneshige,	Thomas	Lombaerts,	
Kimberlee	Shish,	Vahram	Stepanyan,	Gordon	Hardy,				
Peter	Robinson		
NASA	Ames	Research	Center,	Moﬀe_	Field	
31 
Generic Transport Model (GTM) with VCCTEF
 
  Milestones
1.  Complete an initial multi-objective optimization study with aeroelastic finite-
element wing model coupled with longitudinal flight dynamic model, and 
subject to appropriate constraints.  
2.  Complete an initial robust control and distributed parameter control system 
design for coupled aircraft dynamics with aeroelastic wing structures to 
suppress fluttering motion.  
Milestones: Aeroelastic Stability Augmentation/Flutter  
                         Suppression Control
National Aeronautics and Space Administration
Flight Controls for Flexible Air Vehicles
Prepared	by		Sean	Swei	and	Kenneth	Cheung	NASA	Ames	Research	Center	
 in	collaboration	with	NASA,	MIT,	Michigan	State	U.,	UCSC,	U.	of	Alabama		March	14,	2017	
32 
     Outline
u Mission Adaptive Digital Aerostructure Technologies 
(MADCAT)
u Linear Parameter Varying (LPV) Modeling & Controls
Ø  Model Alignment
Ø  Adaptive Grid Step Size Determination
N719NU	
MADCAT	v0	
!
!Coordinated	Turn	
Level	ﬂight	
OPTIMAL	WING	SHAPING	CONTROL	
kenny@nasa.gov	
33 
Mission Adaptive Digital Aerostructure  
Technologies  (MADCAT)
§  Develop	a	revolutionary		aerostructure	concept	for	future	air	vehicles	by	combining:	
Ø  lattice-based	discrete/digital	construction	approach	
Ø  multi-objective	optimal	8light	controls			
Component	Development	 Digital	Construction	 Performance	Assessment	
(wind	tunnel	tests)	
34 
Mission Adaptive Digital Aerostructure  
Technologies  (MADCAT)
Performance	Assessment:	MADCAT	v0	Flight	Tests	
N719NU	
35 
Linear Parameter Varying (LPV) Modeling
§  Flexible	Model	Alignment	
Scrambled	Mode	#	 Aligned	Mode	#	
NASA	Generic	Transport	
Model	(GTM)	
36 
Linear Parameter Varying (LPV) Modeling
§  Adaptive	Grid	Step	Size	Determination	
Gji (s) 2−σ
2
= Gji (s+σ ) 2
2
Note:	σ-shifted	H2-norm,	denoted	by	H2-σ	-norm,	is	de8ined	by			
37 
Switch LPV Controls
Scheduling	parameter	division	
Control	Design	Objectives:	
Sequentially	design	a	family	of	LPV	controllers,	such	that:	1)	the	closed-
loop	systems	are	stable;	2)	the	controllers	switch	smoothly	between	
neighboring	controllers;	3)	the	Clight	performances	are	improved.		
LMI	Characterization	
§  Sequential	design	of	hysteresis	switching	LPV	controllers	
38 
Switching LPV Controls
§  Sequential	hysteresis	switching	LPV	controllers	
Scheduling	parameter	division	
Control	Objective:	
Sequentially	design	a	family	of	LPV	controllers	such	that:	1)	the	
closed-loop	systems	are	stable;	2)	the	controllers	switch	smoothly	
between	neighboring	controllers;	3)	the	performances	are	improved.				
LMI	Characterization	
Thank You!	
Sean	Swei	
sean.s.swei@nasa.gov	
650-604-0314	
39 
Thank You


Advanced Control and Autonomy Research

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