Inspired by the general configuration characteristics of automatic production lines, the author discusses the modelisation of important sectors of a factory. Typical topics such as parts feeders, part orienting devices, insertion mechanisms and buffered flows are analysed using random evolution models and non-linear dynamical systems theory.

An introductory chapter highlights basics concepts and practical models, which are then used to solve more advanced problems throughout the book. Included are many numerical examples and LMI synthesis methods and design approaches.

* Unique in its survey of the range of topics. * Contains a strong, interdisciplinary format that will appeal to both students and researchers. * Features exercises and web links to software and data sets.

Performance optimization is vital in the design and operation of modern engineering systems, including communications, manufacturing, robotics, and logistics. Most engineering systems are too complicated to model, or the system parameters cannot be easily identified, so learning techniques have to be applied. This book provides a unified framework based on a sensitivity point of view. It also introduces new approaches and proposes new research topics within this sensitivity-based framework. This new perspective on a popular topic is presented by a well respected expert in the field.

This unified treatment of linear and nonlinear filtering theory presents material previously available only in journals, and in terms accessible to engineering students. Its sole prerequisites are advanced calculus, the theory of ordinary differential equations, and matrix analysis. Although theory is emphasized, the text discusses numerous practical applications as well. Taking the state-space approach to filtering, this text models dynamical systems by finite-dimensional Markov processes, outputs of stochastic difference, and differential equations. Starting with background material on probability theory and stochastic processes, the author introduces and defines the problems of filtering, prediction, and smoothing. He presents the mathematical solutions to nonlinear filtering problems, and he specializes the nonlinear theory to linear problems. The final chapters deal with applications, addressing the development of approximate nonlinear filters, and presenting a critical analysis of their performance.

This book presents the general theory and basic methods of linear and nonlinear stochastic systems (StS) i.e. dynamical systems described by stochastic finite- and infinite-dimensional differential, integral, integrodifferential, difference etc equations. The general StS theory is based on the equations for characteristic functions and functionals. The book outlines StS structural theory, including direct numerical methods, methods of normalization, equivalent linearization and parametrization of one- and multi-dimensional distributions, based on moments, quasimoments, semi-invariants and orthogonal expansions. Special attention is paid to methods based on canonical expansions and integral canonical representations. About 500 exercises and problems are provided. The authors also consider applications in mathematics and mechanics, physics and biology, control and information processing, operations research and finance.

Advanced Mathematical Tools for Automatic Control Engineers, Volume 2: Stochastic Techniques provides comprehensive discussions on statistical tools for control engineers. The book is divided into four main parts. Part I discusses the fundamentals of probability theory, covering probability spaces, random variables, mathematical expectation, inequalities, and characteristic functions. Part II addresses discrete time processes, including the concepts of random sequences, martingales, and limit theorems. Part III covers continuous time stochastic processes, namely Markov processes, stochastic integrals, and stochastic differential equations. Part IV presents applications of stochastic techniques for dynamic models and filtering, prediction, and smoothing problems. It also discusses the stochastic approximation method and the robust stochastic maximum principle. Provides comprehensive theory of matrices, real, complex and functional analysis Provides practical examples of modern optimization methods that can be effectively used in variety of real-world applications Contains worked proofs of all theorems and propositions presented