Networked Embedded Control System
- Integration of control and computing
Moonju Park
Dept. of Computer Science & Engineering
University of Incheon
1
Introduction

Challenges in embedded systems design
• Time to market puts pressure on design time
• The increased complexity (# of components/lines of code, hetereogeneity,
distributed/networked) demands increased system design productivity
• Quality of new predictable, dependable designs has to improve.
• Moving from feasible to optimal systems requires new radical design
processes and tools.

Control system constitutes an important subclass of embedded
computing systems.
Environmental change:
Networked Embedded Systems
• Embedded systems are
becoming increasingly
networked
• Controller-area-networks
(CAN) bus in automobiles
• Services in large buildings
are now run across networks
• e.g. heating, lighting, security
Control view of NECS
Plant
Node
Node
Actuating
Sensing
Actuating
Sensing
Network
Controller
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Control systems in computing
system’s perspective

Due to economic considerations, in spite of the fast development of
the computing hardware, embedded systems are resource
constrained
• Limitations on: speed, memory size, communication bandwidth, etc.
• Use of additional resource (CPU, RAM) is not economically justified.
• Cost favors general-purpose computing components over specially designed
hardware and software.

Quality of control (QoC) not only depends on the control theoretic
methods but also efficient management of resources.
• The key to manage resources is “scheduling” resources.
• Conventional design of control systems does not consider resource scheduling.
• Resulting in “implementation-aware control systems.”
5
Computing systems in control
system’s perspective

Computing systems (H/W & S/W) are inherently control systems.
• Computing systems are designed with assumptions, so there are uncertainties in
resource utilization that affect the performance.
• Programs’ execution time
• Users’ requests
• Input data
• Etc.
• Regarding complex computing systems as controlled dynamics with defined error
terms.
• Use of feedback control method to computing systems can increase the flexibility.
• Virtually any of computing applications can be considered.
6
Co-Design of Control and
Scheduling

Given a set of processes to be controlled and a computer with
limited computational resource, design a set of controllers and
schedule them as real-time tasks such that the overall control
performance is optimized.
• K-E. Årzén & A. Cervin. Control and embedded computing: survey of research
directions.
Uni-processor case.

•
Alternative view
• Design and schedule a set of controllers such that the least expensive
implementation platform can be used while still meeting the performance
specifications.
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Control of computing systems

Conventional approach
• Generation of static schedule
– Problem
• High complexity – longer design time
• Longer response time
• Hard to use in general-purpose computers
• Use of periodic task model
– Problem
• Low utilization due to polling
• Complexity in programming due to resource
scheduling
Applying control theory to
scheduling

Feedback controlled scheduling system
e.g. PID control
Feedback Controlled EDF
Problem: Only applicable to control relative delay
Applying control theory to
computing systems - Example

Web-based applications
• Web Application Server or HTTP server
•
provides services upon requests from network
Users expect real-time response from server
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Control of dynamic computing
system
Utilization bound for non-periodic tasks:

Implementation: Apache server on Linux (AMD-based PC), HTTP 1.1
From “Schedulability Analysis and Utilization Bounds for Highly Scalable Real-Time Services” by T.F. Abdelzaher and C. Lu,
presented at RTAS 2001
Implementation-aware control
- effects of computing system


A set of digital control loops
Each controller is realized as a separate period task
• The primary goal of co-design approaches becomes optimizing
QoC (Quality of Control) under CPU resource constraints
- maximize
Quality of control
- subject to
Schedulability
• Optimize sampling period, input-output latency subject to performance
specification and schedulability.
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Effects of sampling period

Sampling frequency affects the system performance
• High frequency  high control performance, but high network utilization  Less
•


number of controllers  high cost
Low frequency  low network utilization and low cost, but low control
performance
The upper bound of sampling period
• Sampling period guaranteeing the stability of the system
• Stability constraint
The lower bound of sampling period
• Scheduling period from schedulability constraint
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Control loop timing


Main parts of control loop
• Data collection
• Control algorithm computation
• Output transmission
Timing constraints
• Sampling period
• I/O latency (control delay)
• Though sampling frequency at sensor node is fixed, sampling
period at controller may have jitter depending on
implementations
• Scheduling theory can be used to analyze the time variations
and delays in control loops.
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Example

Three control tasks
• T1=12ms, T2=8ms, T3=5ms
• Control loop
t=current time
LOOP
END
A/D conversion
ControlAlgorithmExecution
D/A conversion
t=t+h
WaitUntil(t)
• Priorities are given rate-monotonic.
• Execution time is 2ms.
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Implementation awareness
Preemptive scheduling
T3
T2
T1
I/O latency
Sampling period
Non-preemptive scheduling
Sampling period
T3
T2
T1
I/O latency
Sampling period
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Preemptive vs. non-preemptive
Preemptive scheduling
• Responsiveness
• Favor high priority tasks
• (generally) Higher utilization
 Non-preemptive scheduling
• Introduce blocking time
• (generally) Lower utilization
• Shorter I/O latency  Control applications’

preference

It is hard to say which one is better
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Application of computing to
control
• Networked
Embedded Control
System (NECS)
• Feedback control
system wherein the
control loops are
closed through RTN
• Aviation system,
automotive system,
surveillance system,
etc
Networked Control Systems

Competing shared network
• Network bandwidth = mi/hi
• mi = Tc + Tca + Tsc
•
•
•
•
Hi = Transmission period of each control system
Tc : Computation time
Tca : Controller to actuator transmission time
Tsc : Sensor to controller transmission time
mi scheduling


bound
i 1 hi
n
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Preemptiveness revisited
(short execution time, long network delay)
Preemptive scheduling
Tsc
Tc
(Network is non-preemptive)
Tca
Tsc
Tc
Tca
Error in previous works
Non-preemptive scheduling
Tsc
Tc
Tca
Tsc
Tc
Tca
Error in previous works
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Preemptiveness revisited
(long execution time)
Preemptive scheduling
Tsc
Tc
Tsc
Tca
Tc
Tca
Higher priority
(Network is non-preemptive)
I/O latency
Non-preemptive scheduling
Tsc
Tca
Tc
Tsc
Tc
Tca
I/O latency
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Control approach - compensation

Develop compensation method for jitter
• Building up control functions for irregular
•
sampling interval
Offline calculation + online control
w/o compensation
With compensation
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Scheduling approach

Use of relative deadline different from
period
Jitter is reduced

Drawbacks
• Analysis is complex.
• There can be utilization loss.
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Use of non-preemptive scheduling

Utilization can be (virtually) arbitrarily
small
• Example:
1 (C1  10, T1  10,000)  2 (C2  2T1  2C1  1, T2 )
19991
0 10
10000
20000
T2  ∞
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Thread-X package

Dual priority scheme
• Two fixed priorities are assigned to a task.
• Priority
• Threshold: run-time priority
• Only tasks with priority higher than the threshold
of the currently running tasks can preempt.
It can achieve higher utilization than
premptive and non-preemptive scheduling
 Threshold calculation requires complex
calculation – done offline (design time)

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Quantum-based fixed priority
scheduling

Combination of priority-based and
quantum-based scheduling
• Enhances utilization
• Adopts non-preemptiveness in preemptive
•
scheduling
Can be easily implemented on generalpurpose computers
Quantum-based scheduling vs.
Thread-X
Achieves higher utilization than Thread-X
 Shorter period can be employed
Reducing power consumption

Energy-limited variable voltage microprocessor on which N
independent control tasks run concurrently.
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Voltage scaling

System Model
• Nominal execution time Ci, sampling period hi
Ci
 h 
i
•  is normalized processor speed

Energy Model
E ( ) ~  2

~ f
f ~V
Dynamic voltage scaling can be employed to reduce the
power consumption.
• By lowering the voltage, the processor can run slowly.
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Idea of DVS

Sampling period = 50ms, Computation time = 10ms
10ms
50ms
P ~ 3.52 10  122.5
P ~ 0.7 2  50  24.5
50ms
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Low-power control

Find a balanced engineering solution for a
control system.
• Shorter sampling period  Higher control
•

performance, Higher power consumption
Longer sampling period  Poorer control
performance, Lower power consumption
Work in progress currently
32
Conclusion and Future works

Integration of control and scheduling is an emerging field of
research.
• Application of control theory to computing systems.
• Design of implementation-aware control systems.

Future research directions
• Control perspective
• Event-driven control
• Dynamic models of computing systems
• Modeling of embedded control systems
• Computing perspective
•
•
•
•
Event-driven control
Providing temporal determinism of control tasks
Supporting tools development
Practical implementation
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