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Iterative Learning Control (ILC) differs from most existing control
methods in the sense that, it exploits every possibility to
incorporate past control informa tion, such as tracking errors and
control input signals, into the construction of the present control
action. There are two phases in Iterative Learning Control: first
the long term memory components are used to store past control
infor mation, then the stored control information is fused in a
certain manner so as to ensure that the system meets control
specifications such as convergence, robustness, etc. It is worth
pointing out that, those control specifications may not be easily
satisfied by other control methods as they require more prior
knowledge of the process in the stage of the controller design. ILC
requires much less information of the system variations to yield
the desired dynamic be haviors. Due to its simplicity and
effectiveness, ILC has received considerable attention and
applications in many areas for the past one and half decades. Most
contributions have been focused on developing new ILC algorithms
with property analysis. Since 1992, the research in ILC has
progressed by leaps and bounds. On one hand, substantial work has
been conducted and reported in the core area of developing and
analyzing new ILC algorithms. On the other hand, researchers have
realized that integration of ILC with other control techniques may
give rise to better controllers that exhibit desired performance
which is impossible by any individual approach.
Fuzzy technology has emerged as one of the most exciting new
concepts available. Fuzzy Logic and its Applications... covers a
wide range of the theory and applications of fuzzy logic and
related systems, including industrial applications of fuzzy
technology, implementing human intelligence in machines and
systems. There are four main themes: intelligent systems,
engineering, mathematical foundations, and information sciences.
Both academics and the technical community will learn how and why
fuzzy logic is appreciated in the conceptual, design and
manufacturing stages of intelligent systems, gaining an improved
understanding of the basic science and the foundations of human
reasoning.
Iterative Learning Control (ILC) differs from most existing control
methods in the sense that, it exploits every possibility to
incorporate past control informa tion, such as tracking errors and
control input signals, into the construction of the present control
action. There are two phases in Iterative Learning Control: first
the long term memory components are used to store past control
infor mation, then the stored control information is fused in a
certain manner so as to ensure that the system meets control
specifications such as convergence, robustness, etc. It is worth
pointing out that, those control specifications may not be easily
satisfied by other control methods as they require more prior
knowledge of the process in the stage of the controller design. ILC
requires much less information of the system variations to yield
the desired dynamic be haviors. Due to its simplicity and
effectiveness, ILC has received considerable attention and
applications in many areas for the past one and half decades. Most
contributions have been focused on developing new ILC algorithms
with property analysis. Since 1992, the research in ILC has
progressed by leaps and bounds. On one hand, substantial work has
been conducted and reported in the core area of developing and
analyzing new ILC algorithms. On the other hand, researchers have
realized that integration of ILC with other control techniques may
give rise to better controllers that exhibit desired performance
which is impossible by any individual approach."
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