SS Mohamed
Iterative learning control of multivariable plants
Mohamed, SS
Authors
Contributors
B Porter
Supervisor
Abstract
In recent years, many researchers have proposed different iterative learning
controllers, which unfortunately mostly require that the plants under control be
regular. Therefore, in order to remove this limitation, various analogue and digital
iterative learning controllers are proposed in this thesis.
Indeed, it is shown that analogue iterative learning controllers can be designed for
plants with any order of irregularity using initial state shifting or initial impulsive
action. However, such analogue controllers have to be digitalised for purpose of
implementation. In addition, in the synthesis of their control laws, such controllers
require some knowledge of the plants' Markov parameters. Ilerefore, new digital
iterative learning controllers are proposed. Such digital controllers circumvent the
need for detailed mathematical models of the plants in any form. Indeed, the
proposed digital iterative learning controllers rely on input/output data in the
synthesis of their control laws. It is shown that digital iterative learning controllers
can be readily designed for multivariable plants of any order or irregularity using only
such input/output data in the form of step-responsem atrices.
The learning rates achievable in both the analogue and digital iterative learning
control of linear multivariable plants are investigated. It is shown that the irregularity
and stability characteristics of the plants under control impose severe constrains on the
achievable learning rates. Indeed, it is shown that the learning parameter in the case
of digital iterative learning controllers increases as the order of plant irregularity
increases. This increase in the learning parameter affects the learning performance
and the speed of convergence adversely. This discovery led to the introduction of
compensators in the design of digital iterative learning controllers for irregular plants which help to improve the learning performance and convergence by reducing the
effective learning parameter. Since such digital iterative learning controllers use stepresponse
matrices in the synthesis of their control laws and since the step-response
characteristics can be identified in real time, it is shown in this thesis that iterative
learning controllers can readily be rendered adaptive in case plant dynamics are
initially unknown or time-varying.
In order to demonstrate the applicability of these results to the control of robotic
manipulators, both analogue and digital iterative learning controllers are designed for
a two-link manipulator in both joint and task spaces. Finally, digital iterative
learning controllers are designed and practically implemented in the real-time
positional control of a dc servo actuator.
Citation
Mohamed, S. Iterative learning control of multivariable plants. (Thesis). University of Salford, UK
Thesis Type | Thesis |
---|---|
Deposit Date | Jun 26, 2009 |
Publicly Available Date | Jun 26, 2009 |
Additional Information | Additional Information : PhD supervisor: Professor B. Porter |
Award Date | Apr 1, 1992 |
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