Feedforward Control Design For Finite Time Transition Problems Of Nonlinear Systems With Input And Output Constraints

This volume presents a well balanced combination of state-of-the-art theoretical results in the field of nonlinear controller and observer design, combined with industrial applications stemming from mechatronics, electrical, (bio–) chemical engineering, and fluid dynamics. The unique combination of results of finite as well as infinite–dimensional systems makes this book a remarkable contribution addressing postgraduates, researchers, and engineers both at universities and in industry. The contributions to this book were presented at the Symposium on Nonlinear Control and Observer Design: From Theory to Applications (SYNCOD), held September 15–16, 2005, at the University of Stuttgart, Germany. The conference and this book are dedicated to the 65th birthday of Prof. Dr.–Ing. Dr.h.c. Michael Zeitz to honor his life – long research and contributions on the fields of nonlinear control and observer design.

Underactuated multibody systems are intriguing mechatronic systems, as they posses fewer control inputs than degrees of freedom. Some examples are modern light-weight flexible robots and articulated manipulators with passive joints. This book investigates such underactuated multibody systems from an integrated perspective. This includes all major steps from the modeling of rigid and flexible multibody systems, through nonlinear control theory, to optimal system design. The underlying theories and techniques from these different fields are presented using a self-contained and unified approach and notation system. Subsequently, the book focuses on applications to large multibody systems with multiple degrees of freedom, which require a combination of symbolical and numerical procedures. Finally, an integrated, optimization-based design procedure is proposed, whereby both structural and control design are considered concurrently. Each chapter is supplemented by illustrated examples.

In the 60's, control, signals and systems had a common linear algebraic background and, according to their evolution, their respective backgrounds have now dramatically differed. Recovering such a common background, especially in the nonlinear context, is currently a fully open question. The role played by physical models, finite or infinite dimensional, in this hypothetical convergence is extensively discussed in this book. The discussion does not only take place on a theoretical basis but also in the light of two wide classes of applications, among the most active in the current industrially oriented researches: - Electrical and Mechatronical systems; - Chemical Processes and systems appearing in Life Sciences. In this perspective, this book is a contribution to the enhancement of the dialogue between theoretical laboratories and more practically oriented ones and industries. This book is a collection of articles that have been presented by leading international experts at a series of three workshops of a Bernoulli program entitled “Advances in the Theory of Control, Signals and Systems, with Physical Modeling” hosted by the Bernoulli Centre of EPFL during the first semester of 2009. It provides researchers, engineers and graduate students with an unprecedented collection of topics and internationally acknowledged top-quality works and surveys.

Artificial neural networks possess several properties that make them particularly attractive for applications to modelling and control of complex non-linear systems. Among these properties are their universal approximation ability, their parallel network structure and the availability of on- and off-line learning methods for the interconnection weights. However, dynamic models that contain neural network architectures might be highly non-linear and difficult to analyse as a result. Artificial Neural Networks for Modelling and Control of Non-Linear Systems investigates the subject from a system theoretical point of view. However the mathematical theory that is required from the reader is limited to matrix calculus, basic analysis, differential equations and basic linear system theory. No preliminary knowledge of neural networks is explicitly required. The book presents both classical and novel network architectures and learning algorithms for modelling and control. Topics include non-linear system identification, neural optimal control, top-down model based neural control design and stability analysis of neural control systems. A major contribution of this book is to introduce NLq Theory as an extension towards modern control theory, in order to analyze and synthesize non-linear systems that contain linear together with static non-linear operators that satisfy a sector condition: neural state space control systems are an example. Moreover, it turns out that NLq Theory is unifying with respect to many problems arising in neural networks, systems and control. Examples show that complex non-linear systems can be modelled and controlled within NLq theory, including mastering chaos. The didactic flavor of this book makes it suitable for use as a text for a course on Neural Networks. In addition, researchers and designers will find many important new techniques, in particular NLq emTheory, that have applications in control theory, system theory, circuit theory and Time Series Analysis.

Based on the authors’ recent advances, this book focuses on a class of nonlinear systems with mismatched uncertainties/disturbances and discusses their typical control problems. It aims to provide a comprehensive view of the nonrecursive control theory and application guidelines. Various applications on the nonrecursive synthesis of complex nonlinear systems not only greatly simplify the control design process, weaken the system assumptions, and reduce the conservatism of gain selection, but also realize the essential detachment of control law design and Lyapunov function-based stability analysis. Therefore, different from the classical recursive control design methods, it is of significance to study the synthesis of nonlinear systems from the perspective of a new nonrecursive control framework. This book discusses the following typical control problems: theoretical background, homogeneous systems theory review, nonrecursive robust control design, nonrecursive adaptive control design, nonrecursive general dynamic predictive control, disturbance estimation and attenuation, nonrecursive stability analysis, implementation theory and real-life applications to series elastic actuators, DC microgrids, and permanent magnet synchronous motor (PMSM) systems under the proposed nonrecursive synthesis framework. This book will be a great reference for scholars and students in the field of automation and control. It will also be a useful source for control engineers and those working on anti-disturbance control, nonlinear output regulation, nonsmooth control, and other related topics.