Distributed Cooperative Control and Optimization · 2015. 7. 28. · CDC 2014, Los Angeles Number...
Transcript of Distributed Cooperative Control and Optimization · 2015. 7. 28. · CDC 2014, Los Angeles Number...
Distributed Cooperative Control and Optimization
presented by
Lihua Xie
School of EEE, Nanyang Technological University, Singapore
Email: [email protected]
11 December 2014
Invited Session MoA08 Networked Controls and Games
Regular Session MoB08 Cooperative Systems
Regular Session MoC04 Nonlinear Control of Multi-Agent Systems
Regular Session MoC21 Distributed Robust Design
Regular Session TuA04 Distributed Control and Estimation
Panel Session TuB19 Collaborative Networked Organizations Principles; Decentralized and Distributed Control; Holonic Manufacturing Systems
Invited Session TuB20 Sustainable Networked Enterprises & Eco-Industrial Networking
Regular Session TuC04 Synchronization in Networked Systems
Regular Session WeA04 Decentralised Techniques for Estimation and Identification
Regular Session WeA11 Decentralized Control
Regular Session WeB01 Control of Microgrids
Regular Session WeB04 Networked Control with Time Delay
Regular Session WeB09 Networked Robotic Systems
Regular Session WeC04 Stochastic Approaches to Distributed Systems
Regular Session WeC16 Semantics in Enterprise Integration and Networking
Invited Session WeC17 Energy Management and Grid Interaction for Plug-In Electric Vehicles
Regular Session WeC23 Multi-Vehicle Systems
Regular Session ThA04 Advances in Consensus
Regular Session ThB04 Coordination of Multiple Vehicle Systems I
Regular Session ThB20 Telematics: Control Via Communication Networks
Invited Session ThB21 New Developments in Control and Optimization of Complex Systems
Regular Session ThC04 Coordination of Multiple Vehicle Systems II
Regular Session ThC13 Networked Systems
Regular Session ThC16 Control of Complex Systems
Regular Session FrA04 Control Over Networks
Invited Session FrA15 Data Acquisition and Processing: Bad Data and Cyber-Security in Smart Grids
Regular Session FrA18 Optimal Control of Distributed Systems
Regular Session FrB04 Adaptive Methods for Muti-Agent Systems
Regular Session FrB11 Distributed Control for Power Systems
2014 IFAC World Congress, Cape Town Number of sessions related to networked and distributed control (29)/ number of total sessions (341)=8.5%
CDC 2014, Los Angeles
Number of sessions related to networked and distributed control (35)/ number of total sessions (190)=18.42%
Invited Session MoA05 Large Scale and Distributed Optimization Invited Session TuB12 Mean Field Games II
Invited Session MoA06 New Control Approach for Power Networks Regular Session TuB17 Network Analysis and Control II
Invited Session MoA17 Controllability and Stability of Networked Control Systems I
Regular Session TuB18 Cooperative Control II
Regular Session MoA20 Consensus I Invited Session TuB20 Networked Control Systems: Consensus, Estimation and Security
Invited Session MoB05 Decentralized Coordination and Control Invited Session TuC05 Topics in Decentralized and Distributed Control
Invited Session MoB17 Controllability and Stability of Networked Control Systems II
Invited Session TuC17 Social and Economic Networks
Regular Session MoB20 Consensus II Regular Session TuC18 Cooperative Control III
Regular Session MoC05 Decentralized Control Regular Session TuC20 Synchronization
Invited Session MoC17 Dynamics in Social Networks: Opinions, Games and Optimization
Regular Session WeA17 Networked Control Systems I
Regular Session MoC20 Consensus III Regular Session WeA18 Computer Networks
Regular Session TuA05 Distributed Control I Regular Session WeB17 Networked Control Systems II
Invited Session TuA06 Smart Grid Solutions with Innovative Communication and Control Technologies
Invited Session WeB18 Coordination and Consensus Algorithms in Distributed Control Systems
Invited Session TuA12 Mean Field Games I Regular Session WeB20 Agents and Autonomous Systems II
Regular Session TuA17 Network Analysis and Control I Invited Session WeB21 Epidemics in Networks: Analysis and Control
Regular Session TuA18 Cooperative Control I Regular Session WeC07 Transportation Networks
Regular Session TuA20 Multi-Agent Systems Regular Session WeC17 Networked Control Systems III
Regular Session TuB05 Distributed Control II Regular Session WeC18 Sensor Networks
Why Distributed Control? -offer extended capabilities/force multiplier
Accomplish tasks not possible for single UAV/UGV
Complete tasks more efficiently and effectively
System of systems – more complex
Autonomy and collaboration are the key
Networked Multi-UAV Teaming: Challenges
Intelligent: Autonomous mission planning and execution
Collaborative: Efficient multi-UAV collaboration and teaming
Aware: Comprehensive, shared and predictive situational awareness
Responsive: Holistic contingency management
Robustness: Robust control against failures
(UAV, comm, wind, etc)
Steve et al.
Distributed task
assignment
Formation, coverage control, etc
Distributed estimation, fusion, etc
• Determinism of data transmission, interferences, fading and
time-varying throughput, packet drops, etc
• Protocols: Contention based, time based control, hybrid,
event-triggered?
• Whom, when and what to communicate (topology and
bandwidth limitation)?
• Security
Some Challenges Relating to Control
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Dynamics and disturbances
• Heterogeneity, uncertainties, disturbances
Communications
Control and optimization
• Coupling between communications and control
• Constraints in control, computation and communications
• Global vs. local
Formation Control
UAV 1
Robust MPC
UAV 2
Robust MPC
UAV 3
Robust MPC
… Robust MPC
Tracking control
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Indoor
Outdoor..\..\Videos\UAV\Outdoor UAV\2UAV-Circle.mp4
1 1q u
2 2q u
3 3q u
n nq u
1 1,max| |iu u ,1 ,1 ,| |i j i jq q r ,1iq
Communication Channel Models
AWGN Channel (satellite
comm, air-to-air, optical comm)
1log(1 )
2C
2/ . (constant)nP
Fading Channel (urban,
underwater comm)
n(t) is white Gaussian with
variance
Power of channel input is
bounded by
Signal-to-noise ratio (SNR):
2.n
n(t) is white Gaussian with
variance
Power of channel input is
bounded by
Channel side information:
Instantaneous SNR:
2.n
.P
2
2
( ). (time-varying)
n
P t
( ).t
.P
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Network Topology and Data Rate
• Communication network is essential for MAS.
• Both network topology and data rate are to be considered.
• Controllability and observability of networked systems
• Relation between topological structure of network and
global control objectives is largely unknown (Xiao and
Boyd, 2004; Chen and Zhang, 2011).
• Optimal topology search often leads to computationally
intractable solutions.
• Limited research on data rate for MAS except simple lower
order dynamics.
– Ahlswede, “Network information flow,” IEEE Trans. Information Theory, vol. 46, no. 4, 2000.
– R. Koetter and Medard, “An algebraic approach to network coding,” IEEE/ACM Trans. Networking, vol. 11, no.
5, 2003
i encoding transmission decoding j
States are real-valued,but only
finite bits of information are
transmitted at each time-step
( )ijx t( )ix t
{ , , }G V E A
Data Rate and Network Topology
Key issue: Joint design of coder/decoder and control protocol to
minimize data rate for distributed consensus
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Existing work:
• n – bit sufficient for distributed consensus of n-th order
integrator dynamics (Li et al., 2011; Li and Xie, 2012; Qiu
and Xie (2013))
• Convergence rate
• For general dynamics, network topology (You and Xie
(2012)
2
2exp ,2
NN
N
KQQ
N
Limitations:
• Synchronization between transmitter and receiver
• Generation of data rate to general dynamics remains
challenging
• Topology in relation to and scalability
• Topology and data rate are dealt with separately
• Performance control
1( )
1
u Nj
j N
QA
Q
2log | ( ) |u
iR A
NQ
SISO Stabilizability (Elia, 2005):
MIMO case corresponds to block-diagonal uncertainties and is
difficult
MIMO with Comm-Control Co-design (Xiao, Xie, Qiu, 2012):
Consensus over identical fading channels (Xu, Xiao, Xie, 2014)
2
MS 22
1log 1 log ( ).
2C M A
Consensus over AWGN and Fading Channels
• Consensus over AWGN channels can be achieved by
decaying gains (Li and Zhang, Liu and Xie, 2011)
• Consensus over fading remains largely open
2
MS 2 221
1log 1 log ( ).
2
i
i
m
i
C M A
22
2 2
1 11 1 ,
1 1 ( )
N
N
Q
Q M A
Distributed Optimization - Coverage Control and Search
Motivation
• Cooperative surveillance and search
• Air-net communication coverage
video1 video2
Problem Formulation
• F: Device-specific, location-
orientation –dependent, overlap
• : Density function
• How to solve this nonlinear
optimization in a distributed way.
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Distributed Optimization - Separable Cost Function
1
1
min ( ), s.t. ...m
i i m
i
f x x x X
( ) ( ) ( )( ) ( ) ( ) ( )ii i ij j i X i i i ij
x t u t a t x x P x x t f x Distributed localization
Rendezvous
Algorithm:
• Convergence to global optimum can be
guaranteed by properly choosing
• How to handle local constraints such as
obstacles
• How to handle control input constraints
• Locally coupled cost functions such as
overlapping in sensing
( )t
Control Research in the Future
Demand- driven To address challenges facing mankind; clean &
sustainable energy, energy efficiency, transportation,
healthcare, environment: Interdisciplinary, high
complexity
Technology- driven Leverage om ultra-rich information arising from
extraordinary sensing, communication, and computing
capabilities (Internet of Things, CPS, data analytics) as
well as advances in devices
Curiosity - driven
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