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CPU Resource Management and Noise Filtering for PID Control

Romero Segovia, Vanessa LU (2014) In PhD Theses TFRT-1100.
Abstract
The first part of the thesis deals with adaptive CPU resource management for multicore platforms. The work was done as a part of the resource manager component of the adaptive resource management framework implemented in the European ACTORS project. The framework dynamically allocates CPU resources for the applications. The key element of the framework is the resource manager that combines feedforward and feedback algorithms together with reservation techniques. The resource requirements of the applications are provided through service level tables. Dynamic bandwidth allocation is performed by the resource manager which adapts applications to changes in resource availability, and adapts the resource allocation to changes in application... (More)
The first part of the thesis deals with adaptive CPU resource management for multicore platforms. The work was done as a part of the resource manager component of the adaptive resource management framework implemented in the European ACTORS project. The framework dynamically allocates CPU resources for the applications. The key element of the framework is the resource manager that combines feedforward and feedback algorithms together with reservation techniques. The resource requirements of the applications are provided through service level tables. Dynamic bandwidth allocation is performed by the resource manager which adapts applications to changes in resource availability, and adapts the resource allocation to changes in application requirements. The dynamic bandwidth allocation allows to obtain real application models through the tuning and update of the initial service level tables.



The second part of the thesis deals with the design of measurement noise filters for PID control. The design is based on an iterative approach to calculate the filter time constant, which requires the information in

terms of an FOTD model of the process. Tuning methods such as Lambda, SIMC, and AMIGO are used to obtain the controller parameters. New criteria based on the trade-offs between performance, robustness, and attenuation of measurement noise are proposed for assessment of the design. Simple rules for calculating the filter time constant based on the nominal process model and the nominal controller are then derived, thus, eliminating the need for iteration. Finally, a complete tuning procedure is proposed. The tuning procedure accounts for the effects of filtering in the nominal process. Hence, the added dynamics are included in the filtered process model, which is then used to recalculate the controller tuning parameters. (Less)
Please use this url to cite or link to this publication:
author
supervisor
opponent
  • Dr. Dahl, Ola, Enea AB
organization
publishing date
type
Thesis
publication status
published
subject
keywords
resource management, embedded systems, real-time systems, multimedia, multicore, measurement noise, filtering, trade-offs, robustness, performance, SDU, noise gain, PID control
in
PhD Theses
volume
TFRT-1100
pages
207 pages
publisher
Department of Automatic Control, Lund Institute of Technology, Lund University
defense location
Lecture hall M:B, M-building, Ole Römers väg 1, Lund University Faculty of Engineering
defense date
2014-04-23 10:15
ISSN
0280-5316
ISBN
978-91-7473-969-5
project
PICLU
language
English
LU publication?
yes
id
7a4b94d7-ee4c-4413-9e38-8360aec74549 (old id 4362724)
date added to LUP
2014-03-28 08:51:45
date last changed
2016-09-19 08:44:45
@phdthesis{7a4b94d7-ee4c-4413-9e38-8360aec74549,
  abstract     = {The first part of the thesis deals with adaptive CPU resource management for multicore platforms. The work was done as a part of the resource manager component of the adaptive resource management framework implemented in the European ACTORS project. The framework dynamically allocates CPU resources for the applications. The key element of the framework is the resource manager that combines feedforward and feedback algorithms together with reservation techniques. The resource requirements of the applications are provided through service level tables. Dynamic bandwidth allocation is performed by the resource manager which adapts applications to changes in resource availability, and adapts the resource allocation to changes in application requirements. The dynamic bandwidth allocation allows to obtain real application models through the tuning and update of the initial service level tables.<br/><br>
<br/><br>
The second part of the thesis deals with the design of measurement noise filters for PID control. The design is based on an iterative approach to calculate the filter time constant, which requires the information in<br/><br>
terms of an FOTD model of the process. Tuning methods such as Lambda, SIMC, and AMIGO are used to obtain the controller parameters. New criteria based on the trade-offs between performance, robustness, and attenuation of measurement noise are proposed for assessment of the design. Simple rules for calculating the filter time constant based on the nominal process model and the nominal controller are then derived, thus, eliminating the need for iteration. Finally, a complete tuning procedure is proposed. The tuning procedure accounts for the effects of filtering in the nominal process. Hence, the added dynamics are included in the filtered process model, which is then used to recalculate the controller tuning parameters.},
  author       = {Romero Segovia, Vanessa},
  isbn         = {978-91-7473-969-5},
  issn         = {0280-5316},
  keyword      = {resource management,embedded systems,real-time systems,multimedia,multicore,measurement noise,filtering,trade-offs,robustness,performance,SDU,noise gain,PID control},
  language     = {eng},
  pages        = {207},
  publisher    = {Department of Automatic Control, Lund Institute of Technology, Lund University},
  school       = {Lund University},
  series       = {PhD Theses},
  title        = {CPU Resource Management and Noise Filtering for PID Control},
  volume       = {TFRT-1100},
  year         = {2014},
}