Dr.ir. Tamás Keviczky · 2018. 11. 23. · Reconfigurable Control Design in Aerospace Applications...
Transcript of Dr.ir. Tamás Keviczky · 2018. 11. 23. · Reconfigurable Control Design in Aerospace Applications...
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Networked Cyber-Physical Systems
Dr.ir. Tamás Keviczky
Delft Center for Systems and ControlDelft University of TechnologyThe Netherlands
[email protected]://www.dcsc.tudelft.nl/~tkeviczky/
September 4, 2018 Systems & Control MSc Information Days
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NCPS Team
Dr. Ir. Tamas Keviczky(Group head)
Dr. Ir. Manuel Mazo Jr.
Dr. Ir. Peyman Mohajerin Esfahani
Dr. Ir. Giulia Giordano
6 PhD students2 Post Doctoral researchers
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Perspective on CPS
�= f(�, υ)
Cyber-Physical
Collision Avoidance
Combustion Control
ABS/ESP
CAN/FlexRay
dx/dt = f(x,u)
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Networked CPS
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September 4, 2018 5
Zoo of Systems
Interconnection
(Sub)systemdynamics
COMPLEXITY
CO
MP
LEX
ITY
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The Challenge of Distributed Control
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The Challenge of Distributed Control
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The Challenge of Distributed Control
?????
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Optimization and Team Decision Making
How can we distribute optimal team decision-making?
How can this work in a real-time control system?
What information should be exchanged and when?
What is the impact of the communication topology?
…
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Pick an MSc project
in Networked Cyber Physical Systems
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1. Increased autonomy via embedded, real-time optimization-based solutions for control and estimation
Research Agenda
Utilize problem structure, parallel computing,for real-time solutions in a closed feedback loop
Subsystem interactions(dynamics, objective, constraints)
Componentsubsystems
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2. Distributed decision-making for interconnected systems in an optimization-based framework
Research Agenda
Distributed optimization as a paradigm fordistributed control and decision-making
Subsystem interactions(dynamics, objective, constraints)
Componentsubsystems
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3. Decision-making under uncertainty
Research Agenda
Scalability/ComputationUncertainty/InformationDynamic nature
Control Theory
Applied Probability
Optimization
Interdisciplinary solutions
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MSc projects
Security-aware Control Synthesis in Networks
• Your are a white-hat hacker!
• Game between the system operator and the attacker
• Mimic natural system behaviors while maximally damage
the system!
• How to mitigate such a damage
Sustainable Buildings: an Advanced Diagnosis Approach
• Modeling the building dynamics
• Design filter to identify certain abnormalities
• Validate the performance through the real measurements
Joint project with DWA (www.dwa.nl)
Dynamical System Filter
©DWA
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4. Control under communication constrains
Research Agenda
Event-based control advances: Modelling of traffic, Security, Implementations, …
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5. Correct-by-design synthesis of controllers
Research Agenda
Population
+
b
x
cb
a+a
Genetic operatiors
Fitness assessmentand parent selection
Controller
User-defined grammar
Formal verification
#1
#2
Temporal logicproperties
Formal methods in control: Abstraction/refinement, Symbolic regression, SMT…
Talk onThursday
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6. Networked systems
Research Agenda
Network-decentralised control, coordination and estimation
Network-decentralized height/position estimation for cartography: infer a global map based on local information exchanges.
Network-decentralised coordination of robots with collision avoidance.
Interacting dynamic subsystems with local information and local action
Talk onMonday
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6. Networked systems
Research Agenda
Structural analysis of biological systems
Natural and biological systems global behaviour (complex interplay of local interactions) astoundingly robust to environmental changes, fluctuations and perturbations.
• “Verification” of structural properties: given a graph structure, generate random functions that satisfy the assumptions and check that the property always holds.
• Simulation-based/analytical study of biological systems and chemical reaction networks.
Structural analysis: assess whether a class of systems always enjoys a given property, due to its structure (topology of the interaction graph) and not to specific parameter values.
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Automotive and Mobile Robot Applications• Vehicle platooning
– Nonlinear trajectory planning (driver assist, traffic constraints)
– Optimize fuel economy, ride comfort– Optimize communications– Verifiable Safety
• Driving on the limits of handling
– Autonomous driving, complex vehicle dynamics
– Exploit problem structure to enable real-time computations
• Distributed Robotic Networks
– Connectivity maximization– Distributed estimation,
SLAM in active sensor networks– Emergent behaviours, swarms
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Aeronautical and Space Applications
• Reconfigurable fault-tolerant predictive flight control
– Real-time, verifiable computation of constrained optimal control laws
– Online optimization-based robust fault estimation for constrained nonlinear systems
• Satellite proximity operations
• Distributed control of spacecraft formations and swarms
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Large-Scale Infrastructure Applications
• CPS security and privacy
• Wireless efficient control
• Demand side management
• Distributed control of water/power/heat networks
• Distributed stochastic MPC of Aquifer Thermal Energy Storage smart grids
• Power-to-X
• Smart building control
• Cooperative control of offshore wind farm power networks
Canal networkHydro power network
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September 4, 2018 22
Zoo of Systems
Interconnection
(Sub)systemdynamics
COMPLEXITY
CO
MP
LEX
ITY
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Real-Time Nonlinear Trajectory PlanningFor Long Heavy Truck Combinations
• Exploit problem structure to enable real-time computations
• Serve as a driver assist system and in future autonomous driving solutions
MScProject
• Respect vehicle dynamics and constraints imposed by the truck and the environment
• Optimize fuel economy, ride comfort, etc.
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September 4, 2018 24
Zoo of Systems
Interconnection
(Sub)systemdynamics
COMPLEXITY
CO
MP
LEX
ITY
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Reconfigurable Control Design inAerospace Applications
• Integrated fault detection, isolation, and predictive control design
• Real-time and verifiable computation of constrained optimal control laws
• Improve penetration of state-of-the-art methods into industrial practice
MScProject
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September 4, 2018 26
Zoo of Systems
Interconnection
(Sub)systemdynamics
COMPLEXITY
CO
MP
LEX
ITY
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September 4, 2018 27
Multi-Agent Systems
Different agents (e.g. robots, spacecraft) have to collaborate
autonomously in order to reach a common goal
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Organic Air Vehicle Formation Flight
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September 4, 2018 30
Distributed Control and FDIR Design for Satellite Formations
MScProject
Delfi-C3
• Individual satellites are tightly coupled by performance requirement
• FDIR is critical for mission success
• Single central computing unit is undesirable
• Collaboration with ongoing space projects at Aerospace Faculty (e.g. Delfi nanosat)
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September 4, 2018 31
Cooperative IndoorFormation Flight Control
• Cooperating quadrotor fleet
• Sensor network-based indoor localization scheme
• Coupled dynamics and control task of lifting and transporting a hung mass
• Optimal synchronization problem with constrained subsystem dynamics
• Implementation in Networked Embedded Robotics Lab
MScProject
In collaboration with LR, Micro Air Vehicle Group (Bart Remes, Christoph De Wagter)
Check out the recent IMAV competitions!!!
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Distributed Control of Multi-Agent Systems on a Mobile Robot Testbed
MScProject
iRobot CreateCricket RF-USrange sensor
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Networked Embedded Robotics Lab
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September 4, 2018 34
Zoo of Systems
Interconnection
(Sub)systemdynamics
COMPLEXITY
CO
MP
LEX
ITY
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September 4, 2018 35
Distributed Optimization and MPC for Large-Scale Infrastructures
• Decomposition methods in optimization and dynamic programming
• Application to Distributed MPC schemes
• Study of performance versus uncertainty in DMPC schemes
• Achievable performance bounds
MScProject
Canal network
Traffic network
Hydro power network
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September 4, 2018 36
Distributed Optimization and MPC for Large-Scale Infrastructures
• Decomposition methods in optimization and dynamic programming
• Application to Distributed MPC schemes
• Study of performance versus uncertainty in DMPC schemes
• Achievable performance bounds
MScProject
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Distributed Optimization and MPC forHigh-Performance Buildings
MScProject
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September 4, 2018 38
Exploit Problem Structure toReduce Complexity of Control Design
• Enable control design for large-scale systems by exploiting symmetry in
– interconnection– subsystem dynamics
• Construct distributed (structured) controllers, estimators
• Respect global objectives and analyze performance of distributed solution
MScProject
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September 4, 2018 39
Exploit Problem Structure toReduce Complexity of Control Design
• Enable control design for large-scale systems by exploiting symmetry in
– interconnection– subsystem dynamics
• Construct distributed (structured) controllers, estimators
• Respect global objectives and analyze performance of distributed solution
MScProject
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September 4, 2018 40
Distributed Constraint Fulfillment
• Design methods for coupled constraints (e.g. collision avoidance)
• Guaranteed feasibility in distributed MPC schemes
• Approximation schemes, controlled invariant sets and reachability
• Robust constraint fulfillment with negotiation
• Reducing conservativeness
MScProject
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September 4, 2018 41
Consensus inDistributed Predictive Control
• Interplay between consensus seeking and MPC
• Incremental subgradient methods
• Optimal synchronization problems with constrained subsystem dynamics
• Application to multi-vehicle coordination, oscillator networks, etc.
MScProject
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September 4, 2018 42
Distributed Moving Horizon Estimation
• Linear and nonlinear distributed solutions for dynamic systems
• Approximation of arrival cost with local particle filters
• Respecting physical constraints
• Local unobservability, information exchange with neighboring sensors
MScProject
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September 4, 2018 43
Some Recommended Courses
• SC42095 Digital Control
• SC42010 Robust and Multivariable Control Design
• SC42040 Adaptive and Predictive Control
• SC42055 Optimization in Systems and Control
• WI4218 Convex Optimization and Semi-Definite Programming
• SC42100 Networked and Distributed Control Systems
• SC42075 Modeling and Control of Hybrid Systems
• SC42060 Modeling and Nonlinear Systems Theory
• AE4-305 (Spacecraft Attitude Dynamics and Control)
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September 4, 2018 44
Preparation for Control Theory Course
• Refresh linear algebra knowledge(see material also on Brightspace)
• Order textbook(B. Friedland, Control System Design – An Introduction to State-Space Methods)