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In this monograph the authors develop a theory for the robust control of discrete-time stochastic systems, subjected to both independent random perturbations and to Markov chains. Such systems are widely used to provide mathematical models for real processes in fields such as aerospace engineering, communications, manufacturing, finance and economy. The theory is a continuation of the authors’ work presented in their previous book entitled "Mathematical Methods in Robust Control of Linear Stochastic Systems" published by Springer in 2006. Key features: - Provides a common unifying framework for discrete-time stochastic systems corrupted with both independent random perturbations and with Markovian jumps which are usually treated separately in the control literature; - Covers preliminary material on probability theory, independent random variables, conditional expectation and Markov chains; - Proposes new numerical algorithms to solve coupled matrix algebraic Riccati equations; - Leads the reader in a natural way to the original results through a systematic presentation; - Presents new theoretical results with detailed numerical examples. The monograph is geared to researchers and graduate students in advanced control engineering, applied mathematics, mathematical systems theory and finance. It is also accessible to undergraduate students with a fundamental knowledge in the theory of stochastic systems.
This book is based on a workshop entitled "Robust Control workshop 2000". The workshop was held in Newcastle, Australia, from the 6th to the 8th December 2000. Chapters of the book are written by some of the leading researchers in the field of Robust Control. They cover a variety of topics all related to Robust Control and analysis of uncertain systems.
In this book quantitative approaches are proposed for production planning problems in automated manufacturing. In particular, techniques from operations research provide ways to tackle these problems. Special attention is given to the efficient use of tools in automated manufacturing systems. The book presents models and tests solution strategies for different kinds of production decision problems. A case study in the manufacturing of printed circuit boards highlights the methodology. The book will help to understand the nature of production planning problems in automated manufacturing and show how techniques from operations research may contribute to their solution.
Top researchers in optimization and control from around the world gathered in Detroit for the 18th annual IFIP TC7 Conference on Systems Modelling and Optimization held in July 1997. The papers presented in this volume were carefully selected from among the 250 plenary, invited, and contributed works presented at the conference. The editors chose these papers to represent the myriad and diverse range of topics within the field and to disseminate important new results. It includes recent results on a broad variety of modelling and control applications, particularly automotive modelling and control, along with recent theoretical advances.
Decentralized Control and Filtering provides a rigorous framework for examining the analysis, stability and control of large-scale systems, addressing the difficulties that arise because dimensionality, information structure constraints, parametric uncertainty and time-delays. This monograph serves three purposes: it reviews past methods and results from a contemporary perspective; it examines presents trends and approaches and to provide future possibilities; and it investigates robust, reliable and/or resilient decentralized design methods based on a framework of linear matrix inequalities. As well as providing an overview of large-scale systems theories from the past several decades, the author presents key modern concepts and efficient computational methods. Representative numerical examples, end-of-chapter problems, and typical system applications are included, and theoretical developments and practical applications of large-scale dynamical systems are discussed in depth.