THESIS
2016
viii, 156 pages : illustrations (some color) ; 30 cm
Abstract
The main theme of this thesis is the design of optimization-based iterative learning control
(ILC) with guaranteed stability for constrained batch processes. By combining information
from previous cycles together with knowledge of a system model, it is known that
optimization-based ILC is a good tool for the control of batch processes. It has a fast
convergent rate and good ability to handle system constraints. However, stability of the
systems controlled by optimization-based ILC has been a critical issue. For many years,
the system stability could only be guaranteed by tuning the controller parameters based
on trial and error. Experiments which are expensive and time-consuming need to be
conducted many cycles over to verify the system stability.
The objective of this thesis i...[
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The main theme of this thesis is the design of optimization-based iterative learning control
(ILC) with guaranteed stability for constrained batch processes. By combining information
from previous cycles together with knowledge of a system model, it is known that
optimization-based ILC is a good tool for the control of batch processes. It has a fast
convergent rate and good ability to handle system constraints. However, stability of the
systems controlled by optimization-based ILC has been a critical issue. For many years,
the system stability could only be guaranteed by tuning the controller parameters based
on trial and error. Experiments which are expensive and time-consuming need to be
conducted many cycles over to verify the system stability.
The objective of this thesis is to provide systematic ways for controller design, such that
the system stability is theoretically guaranteed and the parameter tuning is avoided.
The thesis is composed of two parts. The first part studies how to combine a model
predictive control with an iterative learning control in the local time domain. Here, a
batch process controlled by ILC is formulated as a two-time-dimensional (2D) system.
Stable model predictive control algorithms are designed based on the 2D system. Three
types of invariant sets are developed to ensure recursive feasibility.
The second part looks into how to design optimization-based ILC in the trial domain.
For stable systems, an adaptive robust ILC is designed based on set-membership identification. For unstable systems with non-repetitive disturbances, a new framework to
combine ILC with a state feedback controller is proposed. This framework is then applied
to control batch processes with stochastic disturbances and unknown nonlinear terms. All
of the proposed methods can theoretically guarantee the system stability and constraint
fulfillments. Numerical simulations are conducted to verify these theoretical results.
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