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Cooperative Control and Fault Diagnosis of

Networked Autonomous Unmanned Systems

K. Khorasani, Ph.D., P. Eng.

(Graduating Class of 1985)

Professor and Tier I University Research Chair

Department of Electrical & Computer Engineering

Concordia University

Montreal, Quebec Canada

October 15, 2015

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Outline

Review of the Research Areas of Interest (Past 10 Years)

Research Team Participants (Past 10 Years and Current)

Motivations of the Pursued Research

Cooperative Control of Unmanned Systems and Fault

Diagnosis

Overview of Projects in the Space Sector

Diagnosis, Prognosis, and Health Management (DPHM)

Overview of Projects in the Aerospace Sector

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Research Areas of Interest – Past 10 Years

Consortium for Aerospace Research and Innovation in Canada (CARIC), (Industrial collaborators: Thales Canada and GlobVision), 2015-2016, $1,820,640; Title: Diagnostic and Prognostic Schemes for Aircraft Systems

Qatar National Research Fund, National Priorities Research Program (NPRP), 2012-2015, $1,049,692. Title: Cooperative Control and Fault Diagnosis of a Team of Unmanned Underwater Vehicles and Robots for Oil and Gas Pipeline Inspection, Spill Containment, Recovery and Remote Operation

Qatar National Research Fund, National Priorities Research Program (NPRP), 2012-2015, $961,257. Title: Health Monitoring, Diagnosis, and Prognosis of Industrial Gas Turbines

NSERC Collaborative Research Development, (Industrial collaborators: Pratt & Whitney, GlobVision, CRIAQ), 2009-2012, $608,000. Title: Decision Support Systems for Aircraft Engine Fleet Monitoring and Prognostics

PRECARN/CRIM Alliance Program with GlobVision and Pratt & Whitney Canada, Research Contract, 2008–2009, $200,000. Title: Intelligent Data Validation and Qualification for Aircraft Engines

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Research Areas of Interest – Past 10 Years

NSERC Discovery Grant, 2009-2018, $530,000. Title: Issues in Cooperative Control and Diagnosis of Autonomous Networked Unmanned Systems

NSERC Strategic Projects Grant (Collaborators – Canadian Space Agency, DRDC, GlobVision), 2007–2008, $200,000. Title: Autonomous, Intelligent, and Distributed Diagnosis, Prognosis, and Health Management (DPHM) and Cooperative Control for a Communicating Networked Team of Unmanned Systems (UMS)

Department of National Defence, DRDC ValCartier, Research Contract, 2006-2007, $46,000. Title: Nonlinear Fault Detection, Isolation and Recovery (FDIR) Techniques For Unmanned Systems

PRECARN/CRIM Alliance Program with GlobVision and Telesat Canada, Research Contract, 2006–2007, $205,309. Title: Intelligent Fault Diagnosis, Isolation and Recovery for Attitude Control Subsystem of Satellites

NSERC Strategic Projects Grant (Collaborator- Canadian Space Agency), 2002–2005, $830,000. Title: Intelligent Autonomous Space Vehicles (IASV): Health Monitoring, Fault Diagnosis and Recovery

DaimlerChrysler Corporation (Michigan, USA), Research Contract, 2004-2005, $152,000. Title: Nonlinear State-of-Charge Estimation, Prediction and Identification Schemes for Hybrid Electric Vehicles (HEV’s)

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Research Team (Past 10 Years)Training of Highly Qualified Personnel (Graduated)

Post-Doctoral Fellows/Research Associates 7 (1 co-supervised)

Ph.D. 13 (2 co-supervised)

M.A.Sc. 45

Training of Highly Qualified Personnel (Current)Post-Doctoral Fellows/Research Associates 3 (1 co-supervised)

Ph.D. 8 (1 co-supervised)

M.A.Sc. 5

Training of Highly Qualified Personnel (Total)Post-Doctoral Fellows/Research Associates 10 (2 co-supervised)

Ph.D. 21 (3 co-supervised)

M.A.Sc. 50

Dissemination of Research ResultsRefereed Journal Publications 71 (plus 1 US Patent Filed)

Refereed Conference Proceedings 155

Books/Monograms 4

H-index = 39, Citation ≈ 5700, i-10 Index = 147

Industrial/Governmental Collaborators and Partners (UBC, SFU,UW, UWO):1) Canadian Space Agency 2) DaimlerChrysler 3) Thales Avionics Canada

4) GlobVision, Inc. 5) European Space Agency 6) Telesat Canada

7) Pratt & Whitney Canada 8) CRIAQ /CARIC 9) Precarn, Inc.

10) Department of National Defence –Valcartier 11) Transport Canada

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Motivations of the Research

Definition: Autonomous Unmanned Systems (UMS) are systems that consist of multiple agents or vehicles (i.e. unmanned aerial vehicles (UAV), unmanned ground vehicles (UGV), unmanned underwater vehicles (UUV) and unmanned ground sensors (UGS)) forming a network of multiple sensors/actuators/decision makers with the capability to communicate with one another to perform coordinated tasks without support from human operators.

Applications:

Automated highways

Satellite formations

Search and rescue operations (such as earthquakes and fire disaster relieves)

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Motivations of the ResearchThe envisaged cooperative autonomous entities (agents) offer:

Optimal information sharing,

Timely ISR and situation awareness,

Optimal decision makings.

Command & control and processing data for decision making is challenging due to presence of multiple objectives and constraints ( sheer number of UMS employed).

The proposed autonomous UMS allows the capability to dynamically network (connect, share, and collaborate)

Sensors (regardless of the platform)

Decision-makers (regardless of the architecture)

Actuators (regardless of the service)

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Objectives of the Research

The objectives are to develop, analyze, implement, and evaluate algorithms

designed as solutions for cooperative control, health monitoring, optimal

allocation & distribution of information and resources, such as bandwidth,

energy, fuel, and computing, subject to latencies and real-time constraints in

the network using control-based theory.

Our methodology is based on cooperative and coordination strategies by

using distributed and semi-decentralized control through evaluation and

optimization of team and agent-level performance/utility.

Challenges:

Complexity: System of systems.

Communication: Limited bandwidth and connectivity. What? When? To whom?

Arbitration: Multi-objective optimization problem. Team vs. Individual goals.

Computational/vehicle resources: Is always limited.

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Autonomous FDIR for Space Systems

Ground activities such as command and planning and

sequencing, fault detection, isolation and recovery (FDIR) can

potentially be autonomously handled onboard the

satellite/spacecraft/unmanned vehicle.

Need for more autonomy in the diagnostics and control

systems due to increasing complexity of unmanned systems,

and the cost reduction measures affecting satellite/spacecraft

operators.

On-board FDIR software can detect, identify, and remedy

faults, both minor and critical with reduced support from the

ground station.

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Autonomous FDIR for Space Systems

The envisaged FSW can respond to events when the ground

operators cannot deal due to:

(1) satellite is out of contact with the ground,

(2) response must be immediate,

(3) critical satellite H&S issues are concerned, and

(4) ground lacks key on-board info for formulating the best

response.

Key benefits: (a) operational cost reductions, (b) risk reduction, and (c) enhanced reliability and performance.

FDIR technologies that are able to process large volumes of data as well as real-time info are highly desirable Computationally intelligent-based solutions.

Desirable attributes: (a) early detection, (b) isolability, (c) robustness, (d) multiple fault identifiability, and (e) explanation facility.

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Overview of Past Projects in the Space Sector

Intelligent-based Fault Diagnosis of XIPS Thrusters of

Anik F1- A Boeing 702 Telecommunication Satellite

Objectives: To develop, using historical telemetry data, computationally intelligent FD

techniques for efficiently and timely detecting and isolating faults in the Xenon Ion Propulsion

System (XIPS) thrusters of Anik F1, a Boeing 702 telecommunication satellite operated by

Telesat Canada

Developed FD Techniques:

Dynamic neural network-based inverse mapping (DNNIM)

Neural network-based principal component analysis (NNPCA)

Fusion of DNNIM and NNPCA

Project Accomplishments:

Identified precursor signatures of out-of-spec behaviors five burns prior to the actual

identified initiation of the fault in a XIPS thruster of Anik-F1

Identified new findings and trends in the telemetry data

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Fault Diagnosis of RADARSAT-1 Attitude Control Subsystem

Objectives:

To develop computational intelligence (CI) techniques to detect, isolate, and determine

the root cause of anomalies in momentum wheels of the ACS of the RADARSAT-1

satellite using actual ACS telemetry data

To develop a s/w prototype that is able to integrate telemetry data and achieve fault

diagnosis capabilities without human intervention

Developed Techniques:

A bank of dynamic neural network-based (DNN) filters for fault detection

A machine learning-based fault synthesis algorithm to isolate faults and identify

potential causes of an anomaly or combination/sequence of events leading to a failure

Project Accomplishments:

Confirmed the a priori information on momentum wheel faults of RADARSAT-1 that was supplied by Canadian Space Agency (CSA)

Revealed new information pertaining to the health status of RADARSAT-1 momentum wheels

The behavioural changes due to Earth-Sun geometry (seasonal changes) and Lunar

eclipse were filtered out in the FD scheme, preventing the generation of false alarms

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Autonomous Distributed & Decentralized Formation Keeping Flight

and Reconfiguration Strategies

Motivation: Trend to replace conventional monolithic satellites with multiple

micro- or nano-satellites flying in formation to enable new science missions,

enhance mission performance, reduce operational cost and personnel

requirements, while increasing the overall system fault

tolerance

Objectives: To develop algorithms and technologies for:

Autonomous, collaborative, and cooperative formation

flight control

Investigation on the impact of nonlinearities and

perturbations on the formation

Development of optimization techniques for formation keeping

utilizing fuel-optimal trajectories and optimal Bang-Bang control for

maintaining satellite formation

Development of advanced optimal and time-varying control techniques for

orbital and attitude tracking control

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Distributed Fault Detection, Isolation & Recovery (FDIR)

and Formation Flight (FF) Algorithms for Proba-3

Motivation: To develop FDIR/FF technologies for Proba-3, ESA’s

formation flying advanced technology demonstration mission

Objectives: To design, develop, and implement autonomous FDI techniques

for the propulsion and optical metrology subsystems of the Proba-3 mission

Developed Techniques:

Propulsion system FDI:

Identified various potential failure modes in cold gas thrusters

Pressure regulation failure, stuck-open & stuck-closed failures, abnormal

leakage, and freezing at nozzle exit

Developed a highly reliable nonlinear geometric FDI for detecting and isolating

the above faults in the Occulter satellite

Optical metrology system FDI:

Developed a finite state machine (FSM) model for the operation of

the CLS system in possible presence of three types of failure modes

Developed a DES-based diagnoser based on the FSM model that was able to

detect the presence of any of the three failure modes

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Hierarchical FDIR for Precision Formation Flight (PFF)

A hierarchical FDIR is imperative to efficiently and reliably achieve the

overall FF mission autonomy.

GNC

Local Planning &

Resource Alloc.

RA2

RA4

S/AR1

S/AR2

S/AR3

Power

S/S

AOCS

ACSOCS

L1 Component Level

L1 FDIRAOCS:

Actuatorts: OCS & ACS

Thrusters (Cold Gas), RWs/MWs

Sensors: GPS, Star Trackers,

Sun Sensors, IMU (Gyros)

Metrology

S/S

L2 Subsystem Level

L2 FDIR

L3 System Control Level

L3 FDIR

L4 System Safeguard Level

L4 FDIR

RA3

L5 Formation Control/Safeguard Level

L5 FDIR

Formation Management

Global Planning & Resource Alloc.

RA_x: Recovery Actions issued by L_x FDIR

S/AR_x: Status/Anomaly Report generated by L_x FDIR

GNC

Local Planning &

Resource Alloc.

Power

S/S

AOCS

ACSOCS

L1 Component Level

Comm.

S/S

L2 Subsystem Level

L3 System Control Level

L4 System Safeguard Level

L2 FDIR

L3 FDIR

L4 FDIR

S/AR1 RA2

S/AR2 RA3

S/AR3 RA4

S/AR4 RA5 RA5 S/AR4Occulter Coronagraph

Power S/S:

Battery, Solar Arrays, Power Bus

L1 FDIRAOCS:

Actuatorts: OCS & ACS

Thrusters (Cold Gas), RWs/MWs

Sensors: GPS, Star Trackers,

Sun Sensors, IMU (Gyros)

Metrology S/S

Optical:

CLS,

FLS reciever,

DWI, FTS

Metrology S/S

R

F

Optical:

CCR,

FLS transmitter

R

F Power S/S:

Battery, Solar Arrays, Power Bus

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Development and Experimental Testing of Computationally Intelligent

and Model-Based Technologies for Fault Detection, Isolation, and

Recovery (FDIR) of RADARSAT Constellation

Motivation: The RADARSAT Constellation mission (designed and developed by CSA

and MDA) will provide more timely and comprehensive data than is currently available

by monolithic RADARSAT-1 and the recently launched RADARSAT-2 satellites.

Objectives:

Automating the monitoring and FD of actuators (reaction wheels and propulsion

system thrusters) of the AOCS of the three satellites of the RADARSAT-C mission.

Implementation and experimental testing of an embedded system prototype of the

developed actuator FD techniques using a FPGA hardware kit.

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Development and Experimental Testing of Computationally Intelligent

and Model-Based Technologies for Fault Detection, Isolation, and

Recovery (FDIR) of RADARSAT Constellation (Cont’d)

Envisioned Constellation Diagnosis Management System (CDMS)

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Overview of Projects in the Aerospace Sector:

Diagnosis, Prognosis & Health Management (DPHM)

Objective:

To increase the aerospace manufacturers competitiveness

through (a) cost reductions (reduced maintenance, operation, and

components costs), and (b) performance, reliability, safety, and

service availability enhancements.

Methodology:

Utilize advanced intelligent and autonomous technologies

developed in other sectors, e.g., automotive industry, to aircraft

systems diagnosis, prognosis and health monitoring

management.

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Why an Autonomous and Intelligent DPHM System?

An “ideal” diagnosis, prognosis, and health monitoring

system requires:

ability to handle large amounts of data

recognize patterns in both off-line and real-time

operational modes.

The DPHM system should remain interoperable among

the fleet of aircraft with minimal fine-tuning and operators

involvement.

Provide autonomous data analysis and validation

functionalities and capabilities.

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Why an Autonomous and Intelligent DPHM System?

The DPHM System will be an essential tool for aircraft components

and subsystems to perform:

(a) health condition/status assessment, (b) component maintenance

(RUL and TTF), and (c) retrofit management and post-flight

evaluation whereby

monitored measurements are used to detect, isolate, and

identify the safety critical conditions/faults (for diagnosis)

collected in-flight measurements are analyzed by an

integrated health management software (for condition-based

maintenance)

corrective actions are provided to limit damage to

system/actuators/sensors components or to prevent failure

(reconfiguration)

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Motivations of the Proposed Research

To overcome the disadvantages and limitations of standard

mostly ad-hoc, labor-intensive, error-prone trend analysis &

monitoring techniques as well as simple statistical estimation

models by developing and integrating in novel ways:

Computational intelligence-based (neural networks, fuzzy

systems, evolutionary computation) paradigms, model-based

approaches (nonlinear prediction & estimation), and hybrid

architectures for a unified solution by providing information

fusion efficiently; and

Strategies that are designed to operate efficiently with a large

volume of aircraft data to derive solutions that are as accurate

as possible.

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Project Description

Fault Diagnosis

Data Based

Model Based

Statistical

MethodsNeural

NetworksFuzzy Systems

Multiple Model

Parameter

EstimationParity Equation

Linear KFSymbolic KF Nonlinear

(EKF & UKF)

Genetic

Algorithm

Particle

Filtering

Robust KF

Geometric

Method

Hybrid Methods

Data Based+Model

Based

Neural

Networks +

Kalman Filter

Based Methods

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Project Description

Develop health monitoring & prognosis algorithms by tracking the system health based on its

health signatures (heath tracking)

Predict the long-term behavior of the system health by propagation of its health signatures (health

prediction) and estimate the RUL based on the health prediction results

Continuous monitoring of data to reveal patterns of increasing frequency that should be brought

to the attention of service engineers.

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Hybrid Nonlinear

Diagnosis

(GA + NN)

Hybrid IMM

Diagnosis

(IMM-EKF/UKF +

NN)

Historical Data

(Failure rate, trending

information, etc.)

Aircraft

Component

Model

Fault

Features Feature extraction and query

case construction

Aircraft

Component

Data Textual Description

from Human Expert

Retrieve Stage-1

(Find match based

on fault features)

Retrieve Stage-2

(Find match based on engine

data and text features)

Case-Base for

Fleet of Aircraft

Case-Base

Reasoning

(Reuse, Revise,

Retain)Update

IMM Database

Health Monitoring/

Prognosis

(Project-II)

Condition-Based

Maintenance

Fusion

Model-Based

Prognosis

Neural Network

Prognosis

Fused/Hybrid

Prognosis

Information

(RUL, TTF, etc.)

Aircraft

Component

Data

Future Usage

Plans

Hybrid Diagnosis (Project-I)

Hybrid Prognosis (Project-II)

Decision Support

Environment

Human Expert

Current Health

Information

Current Health

Information

Integrated DPHM

Architecture (Project-III)