Luigi Fortuna

This book deals with locomotion control of biologically inspired robots realized through an analog circuital paradigm as cellular nonlinear networks. It presents a general methodology for the control of bio-inspired robots and several case studies, as well as describes a new approach to motion control and the related circuit architecture. Bio-inspired Emergent Control of Locomotion Systems provides researchers with a guide to the fundamentals of the topics. Moreover, neuro-biologists and physiologists can use the book as a starting point to design artificial structures for testing their biological hypotheses on the animal model.

In the last decade new artificial intelligence methods for the modelling and control of complex systems, namely neural networks, fuzzy logic and probabilistic reasoning have drawn the interest of researchers and engineers. Recently, the advantages achievable by using combinations of these methods, which have independent origin and evolution, have been pointed out, generating a new paradigm which is now termed "soft-computing". This new methodology subsumes the capabilities of neural networks for modelling non-linear systems and for solving classification problems, the power of fuzzy-logic to represent approximate or heuristic reasoning and the large capabilities of evolutionary computation for problem optimisation. The book presents a clear understanding of a new type of computation system, the cellular neural network (CNN), which has been successfully applied to the solution of many heavy computation problems, mainly in the fields of image processing and complex partial differential equations. CNNs' computation-based systems represent new opportunities for improving the soft-computation toolbox. The application of soft computing to complex systems and in particular to chaotic systems with the generation of chaotic dynamics by using CNN is also described. These aspects are of particular interest owing to their growing interest for research and application purposes. Specific topics covered in the text include:- fuzzy logic, control and neural networks;- artificial neural networks and their application in the modelling and control of dynamical systems;- evolutionary optimisation algorithms;- complex dynamics and cellular neural networks;- applications in urban traffic noise monitoring, robot control and rapid thermal process systems.

In this book, the reader will find a theoretical introduction to noninteger order systems, as well as several applications showing their features and peculiarities. The main definitions and results of research on noninteger order systems and modelling of physical noninteger phenomena are reported together with problems of their approximation. Control applications, noninteger order CNNs and circuit realizations of noninteger order systems are also presented. The book is intended for students and researchers involved in the simulation and control of nonlinear noninteger order systems, with particular attention to those involved in the study of chaotic systems and the modelling of nonlinear spatiotemporal phenomena.

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.

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.