adaptive control of network centric dynamic systems
DESCRIPTION
Adaptive Control of Network Centric Dynamic Systems. Jagannathan (Jag) Sarangapani Professor of Department of Electrical and Computer Engineering & Computer Science University of Missouri-Rolla Rolla, Missouri 65409. Tel: 573-341-6775; Email:[email protected] & - PowerPoint PPT PresentationTRANSCRIPT
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Adaptive Control of Network Centric Dynamic Systems
Jagannathan (Jag) SarangapaniProfessor of Department of Electrical and Computer Engineering
& Computer Science
University of Missouri-Rolla
Rolla, Missouri 65409.
Tel: 573-341-6775; Email:[email protected]
&
Site Director, NSF I/UCRC on Intelligent Maintenance Systems
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Outline
• Overview of Research at my Lab/Center
• Energy Aware Protocols
– Adaptive Congestion Control– Routing Protocol
• Conclusions
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OBJECTIVE
COST AND SCHEDULE APPROACH
• Cooperative decision making and control of multiple unattended formation of robots (mobile adhoc networks) with continuous RF communication (UMR mote)
Total Cost$250K
MILESTONES Qtr 1 Qtr 2 Qtr 3 Qtr 4
75 100 50 25
Milestone 1
($K)
Milestone 2
Milestone 3
Milestone 4
Milestone 5
BACKGROUND• Network communication among a formation of robots is necessary in order to perform any task. This communication must be present at all times. This communication must be reliable even when the robots are in motion and when energy is limited. This requires a novel distributed hybrid system architecture, reliable communication hardware and energy efficient protocols for communication that guarantees quality of service requirements.
• Milestone 1: Develop distributed embedded hybrid architecture
• Milestone 2: Develop wide band communication-based UMR Mote
• Milestone 3: Integrate sensors and RF hardware on the robot
• Milestone 4: Implement the energy efficient communication protocols using energy delay metric
• Milestone 5: Implement control strategy and demonstrate
DELIVERABLES
• UMR RF Mote
• Energy Efficient Protocols
• Demonstration and Reports
POINT OF CONTACT Dr. J. Sarangapani, Electrical and Computer Eng Dept., UMREmail: [email protected]; Phone: 573-341-6775;URL: www.umr.edu/~sarangap
Sensor nodesCluster
Heads
Mission HeadQuarters
SENSOR “ARRAY”
WirelessPoint-to-point
Network of Autonomous RobotsRequirement: Continuous Communication among 10 robots/unattended sensors
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UMR Mote
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Manipulation of Microscale/Nano Scale Objects using Micro/Nano Robots
• Manipulation of a micro-sphere– Pickup a micro-sphere from a
planar substrate• Sequence of operations
– Lower a probe– Pickup the micro-sphere– Retract the probe
• Novel intelligent controllers designed outperform available ones
• AFM is used as a feedback for manipulation and drift compensation of nano particles
Fig. 1. Image sequences taken at 256 sec intervals without drift compensation (first row) and with drift compensation (second row). The scanned area is 512×512nm2.
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Prognostics on a Chip
Database
RemainingUseful Life
Confidence
Severity
Distributed Sensors
Wir
eles
s MultivariableAnalysis
with Learning
TrendingConfidence
Degradation
U M R M o t e Service History
Wireless
U M R M o t e ReliabilityInformation
Wireless
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Prognostic Agent Methodology
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Monitoring Using Wireless Sensor Networks
Cluster Head
Sensor Node
Base Station
Self Organizing NetworkDetect Damage ProgressionDiagnosis/PrognosisEmission Control
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Auto-ID Solutions for Network-Centric Manufacturing Environments
Objective: Develop concepts, models, and prototypes for effective integration of Auto-ID technology in a Network-Centric Manufacturing Environment*, aimed at reducing delays and eliminating non-value added production activities and responding rapidly to unexpected events on the shop floor.
Application Potential: 1) Inventory control of time and temperature sensitive materials 2) Receiving & shipping operations3) Aircraft assembly line flow
ROADMAP
Re-EngineerRe-Engineer
Integrated Systems Facility (Emgt)Integrated Systems Facility (Emgt)Embedded Systems and Networking Laboratory (EE)Manufacturing R&D Assembly Lab.
Auto-ID Solutions Development
Auto-ID SolutionsAuto-ID
Solutions
System Simulation (after)
Analysis: amount of wasteno of man-hours PrototypesPrototypes
Tag Simulation
Simulation Simulation withhardware-in-the-loop
Hardware
SYSTEMS
All aircraft assembled at Boeing
Process Modeling
Process Map
Analysis: amount of wasteno of man-hours
System Simulation
(before)
SynthesisSynthesis
Research Highlight:
• Develop techniques for effective use of real-time data provided by Auto-ID technology.
• Demonstrate integration of Auto-ID technology with the aircraft manufacturing practice.
* Network-centric Manufacturing Environment incorporates a dynamic network of self-organizing, autonomous units that operate, collaborate, cooperate, and compete upon basic principles of decentralization, participation, and coordination in order to accomplish the goals set at system level.
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Networking Infrastructure forAdaptive Inventory Management
Objective: To demonstrate integration of decision making in a multi-technology environment.
Conclusions: (1)Modularity of the architecture facilitates expansion of the application domain and experimentation with complex models, and (2) the infrastructure allows for investigating different networking topologies, protocols, and alternative hardware.
Expeditor
Decision Maker – C++
(ES&NL)
Internet
2-BufferArena Model
(ISF)
F1 F2
Sockets
Wireless Access Port
Database -MySQL
CommandsResponse
PDA - Java
Buffers(Shop Floor)
Reader
XML
Antenna
Reader
XML
Antenna
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Neural Emission Control
• Lean operation and with high EGR levels in certain SI Engine can reduce emissions (HC, CO & NOx) by as much as 30% and also it improves fuel efficiency by as much as 5 ~ 10% (Inoue, 1993).
Heywood, 1988 Cyclic dispersion without control
Objective: Minimize the cyclic dispersion at lean engine operation by applying the NN controller
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Nonlinear Discrete-time System
Nonlinear
1 1 1 2 1 1 2 2 1
2 2 1 2 2 1 2 2
3 1 2
1 , ,
1 , ,
1 ,
x k f x k x k g x k x k x k d k
x k f x k x k g x k x k u k d k
y k f x k x k
Back Stepping
NonStrict
Back Stepping
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Comparison of Experimental and Simulation Data
Experimental data (Sutton, 2000) Simulation result
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NN Output Feedback Lean Emission Control
0 2 4 6 8 10 120
2
4
6
8
10
12
Heat Release (i)
Hea
t R
elea
se (
i+1)
Heat release return map without control, = 0.705
Heat releaseFiexed point
1x
2x
nx
1y
2y
my
TV TW
(.)
(.)
(.)
(.)
(.)
(.)
(.)
1
2
3
LInputsHidden Layer
outputs
Neural Network (NN) Controller
Measurements
ControlInputs
Engine
NN Observer
0 1 2 3 4 5 6 7 8 90
1
2
3
4
5
6
7
8
9
Heat Release (i)
Hea
t R
elea
se (
i+1)
Heat release return map with output control, = 0.705
Heat releaseFiexed point
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Contributions
• Nonstrict Feedback Nonlinear Discrete-time Systems introduced and optimal controllers designed
• Noncausal control problem is overcome• Separation Principle, Certainty Equivalence,
Persistency of Excitation Condition, and Linear in the Unknown Parameters are relaxed
• Fuel efficiency improvement by 5 to 10%, NOx by 98% and uHC by 30% was noted
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NN Control Book in 2006
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Outline
• Overview of Research at ESNL
• Energy Aware Protocols
– Congestion Control
– Routing
• Conclusions
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Performance Requirements
• Congestion causes– Reduced channel capacity – Energy wastage
• QoS suffers– Throughput, network efficiency– Delay , jitter– Fairness– Energy-efficiency
• Proposed scheme consists of– Congestion prediction and control
mechanisms to prevents buffer overflows– Fairness mechanism
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Congestion in Wireless Sensor Networks (WSN)
• Congestion can be a result of:– Channel fading– Traffic exceeding channel capacity
• Note: Typical WSN has only one “sink”
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Ways of Alleviating Congestion
• Energy aware congestion control• Adaptive Back off Interval scheme• Adaptive energy delay routing
* Cross Layer Design is needed
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Background
• Sensor versus ad hoc– Processing, memory communication and energy
constrains in WSN in contrast to ad hoc networks
• Previous works– End-to-end protocols (e.g. TCP)
• Drawback of large feedback latency (round-trip)
– CODA, Fusion• Small processing overhead• Congestion message is broadcasted to throttle
traffic, with sources reducing introduced traffic• Use fixed thresholds to initiate flow control• Dropping packets when the congestion occurs
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Objectives of Congestion Control
• Detection of the congestion and an onset of one– Channel estimation from Distributed Power Control
(DPC - our previous work) predicts severe fading and temporarily suspend outgoing and incoming flows
– Buffer occupancy used to control incoming/outgoing flows
• Quality of Service (QoS)– Weighted Fairness expressed as:
where W – throughput, φ – weight• Congestion control using backpressure – a set of three
proposed schemes (described later)
0),(),( 2121
m
m
f
f ttWttW
(1)
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Proposed Scheme
• Rate-based congestion control with weights changed adaptively– Rate selection to prevent buffer overflow– Rate allocation to flows according to weights (using
fair scheduling)– Selection of back-off interval to achieve the selected
rate (predictive, distributed, mathematically guaranteed)
• (OPTIONAL) Adaptive weights allocated to each packet to improve weighted fairness– Adaptive re-calculation of weights for each packet– Fair scheduling and rate allocation to flows based on
adopted weights
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Buffer occupancy and Error Dynamics
• Consider buffer occupancy at a particular node
– where T is the measurement interval, qi(k) is the buffer occupancy of node ‘i’ at time instant k, ui(k) is a regulated (incoming) traffic rate, and fi(k) is an outgoing traffic rate.
• Consider the desired buffer occupancy at node i to be qid. Then, buffer occupancy error defined as ei(k)=qi(k)-qid can be expressed as
(2)
(3)
)()(1 1 kdkufkuT+kqSat=+kq iiiipi
idiiiibi qkdkufkuTkqke )())(()()()1( 1
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Rate Selection
• Define the traffic rate input, ui(k) as
where kv is a gain parameter.• Unknown outgoing traffic is estimated
using adaptive scheme
where the parameter vector is updated
• Selected incoming rate is divided fairly (φj) among incoming flows
)()()()( 1 kekkfTkqqTku iviiidi
(5)
(4)
)1()(ˆ))((ˆ1 kfkkuf iii
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Given the incoming rate selection scheme above with variable i estimated
accurately (no estimation error), and if the incoming traffic is updated as (4),
then the mean estimation error of the variable i along with the mean error in
queue utilization converges to zero asymptotically, if the parameter i is
updated as
provided: (a) 1
2<kui and (b) δ<K fvmax 1 , where 2
11 ku=δ i , fvmaxK is
the maximum singular value of fvK , is the adaptation gain, and
)(ˆ)()( kfkfke iifi is the error between the estimated value and the actual one.
)1()()(ˆ)1(ˆ kekukk fiiii
Theorem: Ideal Case
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0 5 10 15 20 250
5
10
15
20
Time [iteration]
Que
ue le
vel [
pack
ets]
Thr
ough
put
[pac
kets
/ it
erat
ion]
qi
estimated fout
fout
0 5 10 15 20 25-10
-5
0
5
10
Time [iteration]
Buf
fer
occu
panc
y er
ror
[pac
kets
]T
hrou
ghpu
t es
timat
ion
erro
r[p
acke
ts/it
erat
ion]
ebi
ef
Queue utilization and estimation of the outgoing flow.
Queue utilization error and outgoing traffic
estimation error.
Simulation Results for Rate-based Buffer Control
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Adaptive Back-off Selection Algorithm
• GoalSelect back-off interval BOi at i-th transmitting node such
that the actual throughput meets the desired outgoing rate fi(k).
• Consider inverse of the back-off interval, and call it a virtual rate VRi
where VRi is the virtual rate at i-th node, and BOi is the corresponding back-off interval.
• NOTE: the virtual rate is not equal to the actual rate; instead, the virtual rate is proportional to the actual rate.
ii BO=VR /1 (6)
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Outgoing Rate Selection
• The actual rate of an i-th node is a fraction of the channel bandwidth B(t) defined as
where TVRi is the sum of all virtual rates for all neighbor nodes.
• Differentiating and transforming into discreet time domain the outgoing rate is equal
where kvkβ+kαkR=+kR iiiii 1
tTVR
tVRtB=
tVR
tVRtB=tR
i
i
iSll
ii
(8)
(7)
kTVR+kTVR=kα iii 11 kVRkR=kβ iii
111 +kBO=+kVR=kv iii
(9)
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Closed-loop Control of Backoff Selection
• Now consider feedback equation for closed-loop controller
where âi(k) is estimate of ai(k), and ei(k)=qi(k)-qid is the throughput error
• Parameters updates taken as
• The closed loop throughput error system with estimation error, ε(k), as
keKkkRkfkkv Riviijiii )()()()()( 1 (10)
(11) 1ˆ1ˆ kekR+kα=+ka Riiii
(12) )()()(1 kkRkkeKke iiivi
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Convergence and Stability
• Theorem (General case):– Given the back-off selection scheme above with an
interval updated as (10), and with uncertainties estimated by (11), with ε(k) as the error in estimation which is considered bounded above , with εN a known constant.
– Then the mean error in throughput and the estimated parameters are bounded provided
and hold
– where , Kvmax is the maximum singular values of Kv, and σ is the adaptation gain.
12
<kRσ iδ<Kvmax 1
Nεkε
211 kRσ=δ i
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Simulation Results
1 2 3 4 5
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Proposed scheme w/o weight adap-
Proposed scheme
DPC
CODA
Flow ID
Wei
ght *
Del
ay
Performance for unbalanced tree topology.
Weighted delay with equal flow weights (const=0.2).
1 2 3 4 5
0
100
200
300
400
500
600
700 Proposed scheme w/o weight adap -
Proposed scheme
DPC
IEEE802.11
CODA
flow ID
Thr
ough
put /
Wei
ght [
kbps
]
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Impact on Routing Protocol
• Hardware limitation explored– Memory limits queue size– Processing limits number of connections that can
be handled (also for information aggregation)
• Localized congestion – For example due to electro–magnetic interferences– Can be mitigated by selecting a alternative route
around the congested area
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OEDSR
• Optimal Energy Delay Sub-Network Routing (OEDSR) protocol– Maximizes Link Cost Factor (LCF) to perform optimal routing
based on network parameters
– LCF is given by
– Where Ei is the energyenergy in the next node, Di is the delaydelay, and xi is the distancedistance from the next node to the base station
• Cluster heads (CH) and relay nodes (RN) are used to route data from a data source to the base station (BS)
• Implemented on UMR hardwareImplemented on UMR hardware
ii
ii xD
ELCF
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OEDSR: Optimal Relay Node
9 10 13
8
14
11
12
BS
CH 1
1234
15
567
67
CH 2 CH 3
67
7
1
5
2
4
3
“HELLO_CH” packet
“HELLO” packet
“RESPONSE” packet Node ID End-to-End Delay Energy Available Distance from BS
Node IDDistance from
BS
List of all nodes in Range
CH 1 CH 2
567
1234
67
“RELAY_SELECT” packet
2
3
1
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OEDSR: Optimal Relay Node
9 10 13 14
11
BS
CH 1
1234
567
67
CH 2 CH 3
67
7
1
5
2
3
CH 1 CH 2
567
1234
6
2
3
1
DistDelay
ErfactortLink n
_cos_
Ern - energy available in the given node
Delay - average end-to-end delay between any two CHs
Dist - distance from the node to the BS
7
8
12
15
4
7
412158
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Modification to OEDSR
• Link Preference Factor (LPF)
– Load factor i is for balancing load by distributing traffic between several nodes
– where, Fi is the maximum designed capacity of a node i, and fi is the current load at node i (measured in number of flows routed at the node)
iii
ii xD
ELPF
iii fF 1
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Experimental Results for Routing Protocol
• Simple topology used to verify claim of balancing load between two relay nodes versus sending the traffic through one node only
BS
SourcesRelaynodes
3 sources Total Throughput
Average per source
Average per relay node
Through 1 relay
6.88 kbps 2.29 kbps 6.88 kbps (1 relay)
Through 2 relays
8.01 kbps 2.68 kbps 2.43 kbps (2 flows)5.69 kbps (1 flow)
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Demonstration
<Play video>
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Contributions
• Novel adaptive schemes based on control theory developed
• Analytically Guaranteed• Many of these are demonstrated on
Mote Hardware• Deployed in various industrial
environments
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New Book to appear in 2007
• Wireless Ad hoc and Sensor Networks: Protocols, Performance, and Control
• CRC Press, 471 pages
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Conclusions
• Analytical and simulation results show that the proposed scheme– Increases throughput– Guarantee desired QoS and weighted fairness – Also during congestion and fading channels.
• The proposed scheme mitigates congestion using a hop by hop mechanism for throttling packet flow rate
• The convergence analysis is demonstrated by using a Lyapunov-based analysis
• Experimental results show that Congestion-aware routing protocol improves performance of the resource constrained network
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Questions?