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. 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.
