Book 168

These lecture notes focus on the synthesis of robust con-
trollers for feedback systems, in the presence of unstruc-
tured perturbations. It is assumed, as a prerequisites, that
the reader is familiar with the basic linear system and au-
tomatic control concepts. In part I interpolation theory is
used to solve various single-input-single-output (SISO) ro-
bust control problems. While the interpolation approach is
awkward for multivariable systems, it provides a very natu-
ral and simple approach for SISO systems. In particular the
interpolation approach requires only elementary knowledge
of complex variables, and provides a great deal of physical
insight into various robust control problems. The required
interpolation theory is developed in some detail. Part II
is devoted to multivariable systems. Two approaches are out-
lined: the Hankle-norm approach and the two-Riccati-equa-
tion approach. In this part only a limited number of results
are proven. However MATLAB software is presented for nu-
merical solution. The book is addressed to researchers,
practicing engineers, and students who wish to get an intro-
duction to robust control theory for unstructured plant
perturbations. The organization of the book as lecture notes
and the presence of examples and of exercises at the end
of many chapters allow to use the book as an introductory
text in Robust Control courses.

Book 234

In this monograph, new structures of neural networks in multidimensional domains are introduced. These architectures are a generalization of the Multi-layer Perceptron (MLP) in Complex, Vectorial and Hypercomplex algebra. The approximation capabilities of these networks and their learning algorithms are discussed in a multidimensional context. The work includes the theoretical basis to address the properties of such structures and the advantages introduced in system modelling, function approximation and control. Some applications, referring to attractive themes in system engineering and a MATLAB software tool, are also reported. The appropriate background for this text is a knowledge of neural networks fundamentals. The manuscript is intended as a research report, but a great effort has been performed to make the subject comprehensible to graduate students in computer engineering, control engineering, computer sciences and related disciplines.