Iterative Learning Control With Passive Incomplete Information: Algorithms Design And Convergence Analysis
by Dong Shen /
2018 / English / PDF
13.3 MB Download
This book presents an in-depth discussion of iterative learning
control (ILC) with passive incomplete information, highlighting
the incomplete input and output data resulting from practical
factors such as data dropout, transmission disorder,
communication delay, etc.―a cutting-edge topic in connection with
the practical applications of ILC.
This book presents an in-depth discussion of iterative learning
control (ILC) with passive incomplete information, highlighting
the incomplete input and output data resulting from practical
factors such as data dropout, transmission disorder,
communication delay, etc.―a cutting-edge topic in connection with
the practical applications of ILC.
It describes in detail three data dropout models: the random
sequence model, Bernoulli variable model, and Markov chain
model―for both linear and nonlinear stochastic systems. Further,
it proposes and analyzes two major compensation algorithms for
the incomplete data, namely, the intermittent update algorithm
and successive update algorithm. Incomplete information
environments include random data dropout, random communication
delay, random iteration-varying lengths, and other communication
constraints.
It describes in detail three data dropout models: the random
sequence model, Bernoulli variable model, and Markov chain
model―for both linear and nonlinear stochastic systems. Further,
it proposes and analyzes two major compensation algorithms for
the incomplete data, namely, the intermittent update algorithm
and successive update algorithm. Incomplete information
environments include random data dropout, random communication
delay, random iteration-varying lengths, and other communication
constraints.With numerous intuitive figures to make the content more
accessible, the book explores several potential solutions to this
topic, ensuring that readers are not only introduced to the latest
advances in ILC for systems with random factors, but also gain an
in-depth understanding of the intrinsic relationship between
incomplete information environments and essential tracking
performance. It is a valuable resource for academics and engineers,
as well as graduate students who are interested in learning about
control, data-driven control, networked control systems, and
related fields.
With numerous intuitive figures to make the content more
accessible, the book explores several potential solutions to this
topic, ensuring that readers are not only introduced to the latest
advances in ILC for systems with random factors, but also gain an
in-depth understanding of the intrinsic relationship between
incomplete information environments and essential tracking
performance. It is a valuable resource for academics and engineers,
as well as graduate students who are interested in learning about
control, data-driven control, networked control systems, and
related fields.