ACRL Research

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Naira Hovakimyan

http://naira.mechse.illinois.edu/

e-mail: nhovakim@illinois.edu

Advanced Controls Research Laboratory

Department of Mechanical Science and Engineering

University of Illinois at Urbana-Champaign

Urbana, IL :: September 2013

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Outline

Advanced Controls Research Lab

Ongoing Projects and Future Research Directions

•L1 Adaptive Control

•Aerospace Projects

•Other Research Directions

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Outline

Advanced Controls Research Lab

Ongoing Projects and Future Research Directions

•L1 Adaptive Control

•Aerospace Projects

•Other Research Directions

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MS Graduate Researchers

Our Group

Prof. Naira Hovakimyan

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PhD Graduate Researchers

Postodoctoral Researcher

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Visiting Scholars

•N. Tekles (TUM, Germany)

•M. Bichlmeier (TUM, Germany)

Affiliated with:

AE, ECE, ISE, CSL

Affiliated with:

AE, ECE, ISE, CSL

E. Xargay

J. Vervoorst

S. Snyder

D. Sun

Y. Ding

H. Lee

V. Cichella

R. Choe

B. Mehdi

X. Li

H. Jafarnejadsani

J. Chongvisal

W. Hao

K. Ackerman

T. Marinho

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Research

Math, Technology Development, Technology Transition:

•“We focus on open problems in mathematical control theory and we work withcollaborators from industry and government laboratories on the transition ofour solutions, to the extent possible, to real-world engineering problems.”

Interdisciplinary Research:

•“Our work often involves interdisciplinary collaborations with otherdepartments at University of Illinois and other universities in the US and Europe.We also intensively work with various industries and government agencies tofacilitate the quick transition of our basic research results to applicationowners.”

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Collaborators (incomplete list)

Prof. Pedro Aguiar

Prof. Karl J. Åström

Prof. Giulio Avanzini

Prof. Kira Barton

Prof. Tamer Başar

Prof. Carolyn Beck

Prof. Chengyu Cao

Prof. Ramon Costa

Prof. Vladimir Dobrokhodov

Prof. Geir Dullerud

Prof. Oleg Gasparyan

Prof. Tryphon Georgiou

Dr. Reza Ghabcheloo

Dr. Irene M. Gregory

Dr. Rick Hindman

Prof. Florian Holzapfel

Prof. Isaac Kaminer

Dr. Evgeny Kharisov

Prof. Alex Kirlik

Prof. Vishwesh Kulkarni

Prof. Cédric Langbort

Dr. Dapeng Li

Mr. Srinath Mallikarjunan

Prof. Bob Mulder

Prof. Steve Nesbitt

Prof. António Pascoal

Dr. Brett Ridgely

Prof. Dusan Stipanović

Prof. Lui Sha

Prof. Petros Voulgaris

Prof. Xiaofeng Wang

Prof. Matthew West

Dr. Kevin Wise

…

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Current Funding

NASA

•Integration of UASs into the NAS

•Loss-of-Control Prevention and Upset Recovery

AFOSR

NSF

Seagate

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Advanced Controls Research Lab

Ongoing Projects and Future Research Directions

•L1 Adaptive Control

•Aerospace Projects

•Other Research Directions

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Ongoing and Future Projects

L1 Adaptive Control

•Theory

•Applications

•Toolbox

Aerospace-related Projects

•iReCoVeR

•Autonomy

•Very Flexible Aircraft

•Control of unconventional AC configurations

Other Research Directions

•Cyber-Physical-Human Systems

•V&V of Fault-Tolerant Controllers

•Robotic Surgery

•Hard-disk Drive Control

•Repetitive & Adaptive Control

•Information Theory

•Boat autopilots, oil-well drilling, and others…

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L1 Adaptive Control

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L1 Adaptive Control

The theory of L1 adaptive control enables the design of

robust adaptive control architectures using fast adaptation schemes

The theory of L1 adaptive control enables the design of

robust adaptive control architectures using fast adaptation schemes

Theoretical Developments:

•Output feedback (nonlinear reference systems, peaking phenomenon…)

•Unmatched uncertainties

•Optimal design (nonconvex constrained optimization, randomized algorithms…)

•Connections to Robust Control and IMC (DOB, L1 Robust Control…)

•Shaping of the prediction-error dynamics

Applications & Technology Transition

•Flight control systems (TUDelft, TUM, NASA, Raytheon…)

•Hard-disk drives (Seagate)

•Oil-well drilling (Statoil, NTNU)

•Boat autopilots (Raymarine)

•Wind turbines

L1 Toolbox (Matlab, Simulink, C++)

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Aerospace-related Research

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LoC Prevention and Upset Recovery (I)

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iReCoVeR: Integrated Reconfigurable Control for Vehicle Resilience

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LoC Prevention and Upset Recovery (II)

Resilient Flight Control Law

Baseline Controller

L1 Augmentation

Flight Envelope Protection

Dynamic inversion w/ reference models

Upset Onset Detection

Dynamic envelopes (hard vs. soft constraints)

Design of test scenarios

Fault Detection and Isolation

Unknown Input Observers

Sensor failures (hardware redundancy, analytical redundancy)

Icing Effects

Modeling and flight envelope determination

Human-in-the-Loop Flight Simulator

Real-time implementation

Display development and implementation

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LoC = Excursionoutside 3 envelopes

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Wilborn & Foster 2004

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LoC Prevention and Upset Recovery (& III)

Platforms:

TCM (RT simulation)

BAT4 (Sim & FT)

GMAT (Sim & FT)

Autonomy:

Autonomous taxiing, take-off, up-and-away flight, and landing;

Pilot-in-the-loop FCLs for researchtasks.

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BAT4

GMAT (15%)

TCM

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Cooperative Motion Planning & Control (I)

Cooperative Trajectory Generation

Nonlinear constrained optimization

Bezier polynomials

Pythagorean-hodograph curves

Pseudospectral methods

Code development (C++)

Distributed optimization

Cooperative Path Following

Communication-limited environments

Online collision avoidance

GPS-denied environments

Vision-based navigation

Algebraic graph theory, topology control, estimation…

Cooperative Soaring

Guidance for energy harvesting

Atmospheric modeling (PDEs)

Development of a simulation environment

Teams of Cooperating Multirotors

Design of navigation filters

Code development (C++)

Communication systems (hardware integration)

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Cooperative Motion Planning & Control (II)

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+100 flights

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Mosaic of 4 consecutive

high-resolution images

Mosaic of 4 consecutive

high-resolution images

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Cooperation ensures satisfactory overlap ofthe field-of-view footprints of the sensors,increasing the probability of target detection

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Cooperative Motion Planning & Control (& III)

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Basic idea:

•Integration of traditional trajectory-gene-ration and guidance-control algorithmswith weather analysis and forecastingtechniques;

•Power-management policies for improvedenergy harvesting;

•Virtually unlimited endurance for unpow-ered flight is achieved.

Multidisciplinary research:

•meteorology and oceanography;

•PDEs (evolution of atmospheric structures);

•control and trajectory planning.

Developing cooperative autonomous gliders that harvest energy to achieveextended endurance capabilities characteristic of much larger systems

Objective

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Thermal-seeking soaring gliders areused as flying antennas to extendcommunication range

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Modeling & Control of Very Flexible Aircraft (with Avanzini, Uni Salento)

Developing a minimum complexity model of a very flexible aircraft

and proposing approaches and architectures for GNC of this type of vehicles

Developing a minimum complexity model of a very flexible aircraft

and proposing approaches and architectures for GNC of this type of vehicles

Objective

Basic idea:

•Faster, lighter, more efficient aircraftdesigns result into slender and light highlydeformable structures, where couplingbetween aerodynamic loads, deformationstates and aircraft motion is more sig-nificant with respect to more conventionaldesigns;

•Traditional tools for flight mechanicanalysis of rigid aircraft do not representadequately dynamic behavior and per-formance of flexible aircraft.

Tools:

•Mixed Newtonian-Lagrangian modeling;

•Multi-body dynamics;

•Model validation.

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Control of Unconventional Aircraft Configurations

X-48B Blended Wing Body

Augmentation of a dynamic inversion controller

AFRL/Boeing

Grumman X-29

Augmentation of an LQR-PI

NASA Dryden

GL10

(under development)

NASA Langley

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Other Research Directions

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Cyber-Physical-Human Systems (with Kirlik, Sha, & Beck, UIUC)

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Challenges:

•Develop a framework and a systematic approach for the design of complex systems withco-stability guarantees;

•Formulate new V&V protocols for reduced-complexity architectures;

•Use quantitative human performance and situation awareness modeling techniques toensure that the ASA display provides sufficient information about the behavior ofautomated systems;

•Current target applications: aviation and anesthesiology.

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Advanced Robotic Surgery (with Sha, UIUC)

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Source: http://campar.in.tum.de

Idea of robotic surgery:

•Interface between surgeon and patient;

•Visual feedback from patient can be augmented by virtual reality;

•Surgeon action can be altered/automated.

Surgeon

Patient

Robot

Design Opportunities:

Idea of robotic surgery:

•Highly integrated environment – surgery instruments, sensors, bed, anesthesia are operatedfrom a single control center;

•Sensor information is processed, overlaid and presented optimally for improved awareness;

•Actions of the surgeon are monitored and safety control is employed;

•Simple operations and surgeon assist is automated.

•Trauma robots for field operations, e.g., locating and stopping internal bleeding from liver.

Robotic Surgery

Robotic Surgery

Mechanical Assist

Robotic interface

Mechanical Assist

Robotic interface

Information processingand presentation

AutomationAugmentation

Safety

Surgery emulation

Training

Surgery emulation

Training

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Hard-Disk Drive Control

Developing control architectures for external vibration compensation

Objective

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Tasks:

•System identification;

•Uncertainty identification;

•Performance improvement (disturbancerejection);

•Use of adaptive architectures forimproved robust performance;

•Dynamic control allocation.

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Repetitive & Adaptive Control (with Barton, UMich, & Costa, UPC)

Integrate an L1 controller into a Repetitive Control Framework

Periodic disturbances vs. non-periodic disturbances and uncertainties

Redesign of the control objectives

Internal Model Principle

Applications

Distribution grids / Microgrids (Repetitive, UPC)

•Nonlinear uncertain loads

•Harmonic rejection

•Source uncertainty

Manufacturing (ILC, UMich)

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Information Theory

Stochastic Systems with Communication Constraints:

•Analyze the interplay of control and communication in the closed-loop feedbackarchitecture;

•Investigate the fundamental limitations of feedback control in the presence ofcommunication constraints;

•Quantify Bode-like performance limitations for continuous-time systems in the presenceof limited information in terms of mutual information rates.

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Keywords: stochastic process, probability density, mutual information, entropy, conditional entropy,Kolmogorov entropy rate equality, Bode’s integral formula, channel capacity, SNR…