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:sarangap@umr.edu
&
Site Director, NSF I/UCRC on Intelligent Maintenance Systems
1
Outline
• Overview of Research at my Lab/Center
• Energy Aware Protocols
– Adaptive Congestion Control
– Routing Protocol
• Conclusions
2
Network of Autonomous Robots
Requirement: Continuous Communication among 10 robots/unattended sensors
OBJECTIVE
UMR Mote
• Cooperative decision making and control of multiple unattended
formation of robots (mobile adhoc networks) with continuous RF
communication (UMR mote)
BACKGROUND
SENSOR
“ARRAY”
Wireless
Point-to-point
Mission Head
Quarters
Cluster
Heads
Sensor
nodes
• 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.
APPROACH
COST AND SCHEDULE
• Milestone 1: Develop distributed embedded hybrid architecture
MILESTONES
Qtr 1
Qtr 2
Qtr 3
Qtr 4
• Milestone 2: Develop wide band communication-based UMR Mote
Milestone 1
• Milestone 3: Integrate sensors and RF hardware on the robot
Milestone 2
• Milestone 4: Implement the energy efficient communication
protocols using energy delay metric
Milestone 3
• Milestone 5: Implement control strategy and demonstrate
DELIVERABLES
Milestone 4
• UMR RF Mote
Milestone 5
• Energy Efficient Protocols
• Demonstration and Reports
Total Cost
$250K
($K)
75
100
50
25
POINT OF CONTACT
Dr. J. Sarangapani, Electrical and Computer Eng Dept., UMR
Email: sarangap@umr.edu; Phone: 573-341-6775;
URL: www.umr.edu/~sarangap
3
3
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.
4
Prognostics on a Chip
Wireless
UMR Mote
Service
History
Database
Distributed Sensors
Wireless
Degradation
Multivariable
Analysis
with
Learning
Trending
Confidence
Remaining
Useful Life
Confidence
Severity
UMR Mote
Wireless
Reliability
Information
5
Prognostic Agent Methodology
6
Monitoring Using
Wireless Sensor Networks
Sensor Node
Base Station
Cluster Head
Self Organizing Network
Detect Damage Progression
Diagnosis/Prognosis
Emission Control
7
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.
Process Modeling
Auto-ID Solutions Development
Research Highlight:
SYSTEMS
Process
• Develop techniques for effective
use of real-time data provided by
Auto-ID technology.
All aircraft
assembled
at Boeing
• Demonstrate integration of AutoID technology with the aircraft
manufacturing practice.
Re-Engineer
Re-Engineer
Application Potential:
1) Inventory control of time and
temperature sensitive materials
2) Receiving & shipping operations
3) Aircraft assembly line flow
Map
System
Simulation
(before)
Manufacturing R&D Assembly Lab.
Analysis:
amount of waste
no of man-hours
Embedded Systems and
Networking Laboratory (EE)
Integrated Systems Facility (Emgt)
Auto-ID
Solutions
Synthesis
Synthesis
System Simulation
(after)
Tag
Simulation
Analysis:
amount of waste
no of man-hours
Simulation
Prototypes
Simulation with
hardware-in-the-loop
Prototypes
Hardware
ROADMAP
* 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.
8
Networking Infrastructure for
Adaptive 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.
9
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
10
Nonlinear Discrete-time System
x1  k  1  f1  x1  k  , x2  k    g1  x1  k  , x2  k   x2  k   d1  k 
x2  k  1  f 2  x1  k  , x2  k    g 2  x1  k  , x2  k   u  k   d 2  k 
y  k  1  f3  x1  k  , x2  k  
Back
Stepping
Nonlinear
NonStrict
11
Comparison of Experimental and
Simulation Data
Experimental data (Sutton, 2000)
Simulation result
12
NN Output Feedback Lean Emission Control
Neural Network (NN) Controller
VT
 (.)
1
 (.)
2
 (.)
3
x1
WT
x2
xn
Inputs
Engine
 (.)
y1
 (.)
y2
 (.)
L
Hidden Layer
 (.)
ym
outputs
Control
Inputs
Measurements
NN Observer
Heat release return map without control,  = 0.705
Heat release return map with output control,  = 0.705
12
9
Heat release
Fiexed point
8
10
Heat release
Fiexed point
8
Heat Release (i+1)
Heat Release (i+1)
7
6
4
6
5
4
3
2
2
1
0
0
2
4
6
Heat Release (i)
8
10
12
0
0
1
2
3
4
5
Heat Release (i)
6
7
8
9
13
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
14
NN Control Book in 2006
15
Outline
• Overview of Research at ESNL
• Energy Aware Protocols
– Congestion Control
– Routing
• Conclusions
16
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
17
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”
18
Ways of Alleviating Congestion
• Energy aware congestion control
• Adaptive Back off Interval scheme
• Adaptive energy delay routing
* Cross Layer Design is needed
19
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
20
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:
W f (t1 , t 2 )
f

W m (t1 , t 2 )
m
0
(1)
where W – throughput, φ – weight
• Congestion control using backpressure – a set of three
proposed schemes (described later)
21
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
22
Buffer occupancy and Error Dynamics
• Consider buffer occupancy at a particular node
qi k +1 = Satp qi k + T  ui k   fi ui 1 (k )  d (k ) (2)
– 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
ebi ( k  1)  qi ( k )  T  u i ( k )  f i (ui 1 ( k ))  d ( k )  qid
(3)
23
Rate Selection
• Define the traffic rate input, ui(k) as
ui (k )  T 1 qid  qi (k )  T  f i (k )  k v  ei (k ) 
where kv is a gain parameter.
• Unknown outgoing traffic is estimated
using adaptive scheme
fˆi (ui 1 (k ))  ˆ(k ) f i (k  1)
(4)
(5)
where the parameter vector is updated
• Selected incoming rate is divided fairly (φj)
among incoming flows
24
Theorem: Ideal Case
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
ˆi (k  1)  ˆi (k )    ui (k )  e fi (k  1)


provided: (a)  ui k  2 < 1 and (b) K fvmax < 1 δ , where δ = 1 1    ui k  2 , K fvmax is
the maximum singular value of K fv ,  is the adaptation gain, and
e fi (k )  f i (k )  fˆi ( k )
is the error between the estimated value and the actual one.
25
Simulation Results for
Rate-based Buffer Control
qi
estimated fout
15
Queue utilization and
estimation of the outgoing flow.
fout
10
10
5
0
0
5
10
15
Time [iteration]
20
Queue utilization error
and outgoing traffic
estimation error.
25
Buffer occupancy error [packets]
Throughput estimation error
[packets/iteration]
Queue level [packets]
Throughput [packets / iteration]
20
ebi
ef
5
0
-5
-10
0
5
10
15
Time [iteration]
20
25
26
Adaptive Back-off Selection Algorithm
• Goal
Select 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
VRi = 1 / BOi
(6)
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.
27
Outgoing Rate Selection
• The actual rate of an i-th node is a fraction of the
channel bandwidth B(t) defined as
Bt   VRi t  Bt   VRi t 
Ri t  =
=
TVRi t 
 VRl t 
(7)
lSi
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
Ri k +1 = Ri k αi k + βi k vi k 
(8)
αi k  = 1 TVRi k +1 TVRi k 
βi k  = Ri k  VRi k 
vi k  = VRi k +1 = 1 BOi k +1
(9)
28
Closed-loop Control of Backoff
Selection
• Now consider feedback equation for closed-loop
controller
vi (k )  i (k )
1
 f (k )  R (k ) (k )  K e k  (10)
i
ij
i
v Ri
where âi(k) is estimate of ai(k), and
ei(k)=qi(k)-qid is the throughput error
• Parameters updates taken as
aˆi k +1 = αˆi k +  Ri k  eRi k  1
• The closed loop throughput error system with
estimation error, ε(k), as
ei k  1  K v ei k    i (k ) Ri (k )   (k )
(11)
(12)
29
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 εk   ε N , with
εN a known constant.
– Then the mean error in throughput and the estimated
parameters are bounded provided
and σ R k  2 < 1
hold
K
<1 δ
vmax

– where δ = 1 1  σ  Ri k 
,K
i
2
is the maximum
singular values of Kv, and σ is the adaptation gain.
vmax
30
Simulation Results
Proposed
scheme w/o
weight adap -
600
Proposed
scheme
DPC
IEEE802.11
500
400
CODA
300
200
1.4
100
1.2
0
1
1
2
3
4
5
flow ID
Performance for unbalanced
tree topology.
Weight * Delay
Throughput / Weight [kbps]
700
Proposed
scheme w/o
weight adapProposed
scheme
DPC
CODA
0.8
0.6
0.4
0.2
Weighted delay with equal
flow weights (const=0.2).
0
1
2
3
Flow ID
4
5
31
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
32
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
Ei
LCFi 
Di  xi
– Where Ei is the energy in the next node, Di is the delay, and xi
is the distance 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 hardware
33
OEDSR: Optimal Relay Node
CH 1
1
1
2
3
4
5
6
7
3
1
6
CH 1 CH 2
5
6
7
6
7
12
4
5
1
2
3
4
11
2
7
15
2
BS
8
9
3
10
13
14
CH 2 CH 3
6
7
7
“HELLO” packet
“RESPONSE” packet
“HELLO_CH” packet
Node ID
Distance from
BS
Node ID
End-to-End Delay
Energy Available
Distance from BS
List of all nodes in Range
“RELAY_SELECT” packet
34
OEDSR: Optimal Relay Node
CH 1
1
1
2
3
4
5
6
7
3
1
6
CH 1 CH 2
5
6
7
6
7
7
15
2
9
CH 2 CH 3
10
13
14
7
7
Link _ cos t _ factor 
BS
8
3
6
7
12
4
5
1
2
3
4
11
2
4
12
15
8
Ern
Delay  Dist
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
35
Modification to OEDSR
• Link Preference Factor (LPF)
Ei
LPFi 
i
Di  xi
– Load factor i is for balancing load by distributing
traffic between several nodes
 i  Fi 1  f i 
– 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)
36
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
Sources
3 sources
Relay
nodes
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)
37
Demonstration
<Play video>
38
Contributions
• Novel adaptive schemes based on
control theory developed
• Analytically Guaranteed
• Many of these are demonstrated on
Mote Hardware
• Deployed in various industrial
environments
39
New Book to appear in 2007
• Wireless Ad hoc and Sensor Networks:
Protocols, Performance, and Control
• CRC Press, 471 pages
40
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
41
Questions?
42