Outliers play an important though underestimated role in control engineering. Traditionally
they are unseen and neglected. In opposition industrial practice gives frequent examples of
their existence and their mostly negative impacts on the control quality. The origin of
outliers is never fully known. Some of them are generated externally to the process (exogenous)
like for instance erroneous observations data corrupted by control systems or the effect of
human intervention. Such outliers appear occasionally with some unknow probability shifting
real value often to some strange and nonsense value. They are frequently called deviants
anomalies or contaminants. In most cases we are interested in their detection and removal.
However there exists the second kind of outliers. Quite often strange looking data
observations are not artificial data occurrences. They may be just representatives of the
underlying generation mechanism being inseparable internal part of the process (endogenous
outliers). In such a case they are not wrong and should be treated with cautiousness as they
may include important information about the dynamic nature of the process. As such they cannot
be neglected nor simply removed. The Outlier should be detected labelled and suitably treated.
These activities cannot be performed without proper analytical tools and modeling approaches.
There are dozens of methods proposed by scientists starting from Gaussian-based statistical
scoring up to data mining artificial intelligence tools. The research presented in this book
presents novel approach incorporating non-Gaussian statistical tools and fractional calculus
approach revealing new data analytics applied to this important and challenging task. The
proposed book includes a collection of contributions addressing different yet cohesive subjects
like dynamic modelling classical control advanced control fractional calculus statistical
analytics focused on an ultimate goal: robust and outlier-proof analysis. All studied problems
show that outliers play an important role and classical methods in which outlier are not taken
into account do not give good results. Applications from different engineering areas are
considered such as semiconductor process control and monitoring MIMO peltier temperature
control and health monitoring networked control systems and etc.
