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Iterative Feedback Tuning with Application to Robotics

Bindi, Alessandro (2003) In MSc Theses
Department of Automatic Control
Abstract
Many control objectives can be expressed in terms of a criterion function. Generally, explicit solutions to such optimization problem require full knowledge of the plant and disturbances and complete freedom in the complexity of the controller. In practice, the plant and the disturbances are seldom known, and it is often desirable to achieve the best possible performance with a controller of prescribed complexity such as for example a PID controller. The optimization of such control performance criterion typically requires iterative gradient-based minimization procedures. The major stumbling block for the solution of this optimal control problem is the computation of the gradient of the criterion function with respect to the controller... (More)
Many control objectives can be expressed in terms of a criterion function. Generally, explicit solutions to such optimization problem require full knowledge of the plant and disturbances and complete freedom in the complexity of the controller. In practice, the plant and the disturbances are seldom known, and it is often desirable to achieve the best possible performance with a controller of prescribed complexity such as for example a PID controller. The optimization of such control performance criterion typically requires iterative gradient-based minimization procedures. The major stumbling block for the solution of this optimal control problem is the computation of the gradient of the criterion function with respect to the controller parameters: it is a fairly complicated function of the plant and disturbance dynamics. When these are unknown, it is not clear how this gradient can be computed. Iterative Feedback Tuning (IFT) is a input-output data-based design method for the tuning of restricted complexity controllers. It does not depend on the plant model, utilizes I/O data only. Therefore IFT is robust against the plant model uncertainty. At each iteration, an update for the parameters of the controller is estimated from data obtained partly from the normal operation of the closed loop system and partly from a special experiment. No identification procedure is involved. In this thesis tuning of robot joint controllers using IFT is considered. The different IFT-schemes have been verified in simulation and in real experiments on an industrial robot manipulator ABB Irb-2000. (Less)
Please use this url to cite or link to this publication:
author
Bindi, Alessandro
supervisor
organization
year
type
H3 - Professional qualifications (4 Years - )
subject
publication/series
MSc Theses
report number
TFRT-5715
ISSN
0280-5316
language
English
id
8848104
date added to LUP
2016-03-19 17:33:40
date last changed
2016-03-19 17:33:40
@misc{8848104,
  abstract     = {Many control objectives can be expressed in terms of a criterion function. Generally, explicit solutions to such optimization problem require full knowledge of the plant and disturbances and complete freedom in the complexity of the controller. In practice, the plant and the disturbances are seldom known, and it is often desirable to achieve the best possible performance with a controller of prescribed complexity such as for example a PID controller. The optimization of such control performance criterion typically requires iterative gradient-based minimization procedures. The major stumbling block for the solution of this optimal control problem is the computation of the gradient of the criterion function with respect to the controller parameters: it is a fairly complicated function of the plant and disturbance dynamics. When these are unknown, it is not clear how this gradient can be computed. Iterative Feedback Tuning (IFT) is a input-output data-based design method for the tuning of restricted complexity controllers. It does not depend on the plant model, utilizes I/O data only. Therefore IFT is robust against the plant model uncertainty. At each iteration, an update for the parameters of the controller is estimated from data obtained partly from the normal operation of the closed loop system and partly from a special experiment. No identification procedure is involved. In this thesis tuning of robot joint controllers using IFT is considered. The different IFT-schemes have been verified in simulation and in real experiments on an industrial robot manipulator ABB Irb-2000.},
  author       = {Bindi, Alessandro},
  issn         = {0280-5316},
  language     = {eng},
  note         = {Student Paper},
  series       = {MSc Theses},
  title        = {Iterative Feedback Tuning with Application to Robotics},
  year         = {2003},
}