Deterministic Control Of Uncertain Linear Discrete And Sampled Data Systems

My main goal in writing this monograph is to provide a detailed treatment of uncertainty analysis for sampled-data systems in the context of sys tems control theory. Here, sampled-data system refers to the hybrid sys tem formed when continuous time and discrete time systems are intercon nected; by uncertainty analysis I mean achievable performance in the pres ence of worst -case uncertainty and disturbances. The focus of the book is sampled-data systems; however the approach presented is applicable to both standard and sampled-data systems. The past few years has seen a large surge in research activity centered around creating systematic methods for sampled-data design. The aim of this activity has been to deepen and broaden the, by now, sophisticated viewpoint developed for design of purely continuous time or discrete time systems (e.g. J{oo or -I!l optimal synthesis, J1 theory) so that it can be ap plied to the design of sampled-data systems. This research effort has been largely successful, producing both interesting new mathematical tools for control theory, and new methodologies for practical engineering design. Analysis of structured uncertainty is an important objective in control design, because it is a flexible and non-conservative way of analyzing sys tem performance, which is suitable in many engineering design scenarios.

Following the publication of seminal papers by professors H. S. Gordon in 1954 and A. D. Scott in 1955, active research has led to rapid progress in our understanding of the economics of fisheries. Fishing, however, is a complicated activity involving intricate interactions between man-made fishing capital and naturally produced fish stocks in an inherently dynamic and stochastic setting. Consequently, in spite of significant advances, important sections of fisheries economics remain largely unexplored. One such area is the economics of migratory fish stocks. In 1985, the editors of this volume embarked on a research project concerned with the optimal utilization of common Nordic fish stocks. A fundamental feature of some of the most important of these fish stocks, such as the Atlanto-Scandian herring and the Icelandic capelin, is their migratory behaviour. Not only do the migrations continuously alter the economic conditions for harvesting these species. They also result in the periodical transfer of stock concentrations from one exclusive fisheries jurisdictions to another. It was readily apparent that this behaviour constituted a crucial determinant of the appropriate harvesting pattern of these stocks. More importantly, however, migrations are by no means a unique feature of Nordic fish stocks. In fact, it appears that migratory behaviour is exhibited, albeit in varying degrees, by all species of fish. It therefore stands to reason that migrations constitute an important aspect of the economics of fisheries in general.

Presents a state-of-the-art review of model error concepts, their characterization and compensation in estimation and control problems, with particular emphasis on error propagation, model order selection, performance guarantees, sensitivity and adaptive methods. Main topics covered include linear and nonlinear systems, identification, robotics, computer-aided design, signal processing, computers and communication in control, automation and real time control of processes.

The focus of this book is on the design of a specific control strategy using digital computers. This control strategy referred to as Sliding Mode Control (SMC), has its roots in (continuous-time) relay control. This book aims to explain recent investigations' output in the field of discrete-time sliding mode control (DSMC). The book starts by explaining a new robust LMI-based (state-feedback and observer-based output-feedback) DSMC including a new scheme for sparsely distributed control. It includes a novel event-driven control mechanism, called actuator-based event-driven scheme, using a synchronized-rate biofeedback system for heart rate regulation during cycle-ergometer. Key Features: Focuses on LMI-based SMC (sliding mode control) for uncertain discrete-time system using novel nonlinear components in the control law Makes reader understand the techniques of designing a discrete controller based on the flexible sliding functions Proposes new algorithms for sparsifying control and observer network through multi-objective optimization frameworks Discusses a framework for the design of SMC for two-dimensional systems along with analyzing the controllability of two-dimensional systems Discusses novel schemes for sparsifying the control network

This conference volume is a collection of over thirty refereed contributions in the areas of optimization and control. The volume is organized into the following sections: Mathematics of Operations Research and Global Optimization Linear and Combinatorial Programming Tours, Locations and Scheduling Dynamic Programming and Game Theory Control Theory Economic Models. There is a balance between papers dealing with theoretical aspects of the field and those discussing the respective areas of application.

Sampled-data Models for Linear and Nonlinear Systems provides a fresh new look at a subject with which many researchers may think themselves familiar. Rather than emphasising the differences between sampled-data and continuous-time systems, the authors proceed from the premise that, with modern sampling rates being as high as they are, it is becoming more appropriate to emphasise connections and similarities. The text is driven by three motives: · the ubiquity of computers in modern control and signal-processing equipment means that sampling of systems that really evolve continuously is unavoidable; · although superficially straightforward, sampling can easily produce erroneous results when not treated properly; and · the need for a thorough understanding of many aspects of sampling among researchers and engineers dealing with applications to which they are central. The authors tackle many misconceptions which, although appearing reasonable at first sight, are in fact either partially or completely erroneous. They also deal with linear and nonlinear, deterministic and stochastic cases. The impact of the ideas presented on several standard problems in signals and systems is illustrated using a number of applications. Academic researchers and graduate students in systems, control and signal processing will find the ideas presented in Sampled-data Models for Linear and Nonlinear Systems to be a useful manual for dealing with sampled-data systems, clearing away mistaken ideas and bringing the subject thoroughly up to date. Researchers in statistics and economics will also derive benefit from the reworking of ideas relating a model derived from data sampling to an original continuous system.

Most practical processes such as chemical reactor, industrial furnace, heat exchanger, etc., are nonlinear stochastic systems, which makes their con trol in general a hard problem. Currently, there is no successful design method for this class of systems in the literature. One common alterna tive consists of linearizing the nonlinear dynamical stochastic system in the neighborhood of an operating point and then using the techniques for linear systems to design the controller. The resulting model is in general an approximation of the real behavior of a dynamical system. The inclusion of the uncertainties in the model is therefore necessary and will certainly improve the performance of the dynamical system we want to control. The control of uncertain systems has attracted a lot of researchers from the control community. This topic has in fact dominated the research effort of the control community during the last two decades, and many contributions have been reported in the literature. Some practical dynamical systems have time delay in their dynamics, which makes their control a complicated task even in the deterministic case. Recently, the class ofuncertain dynamical deterministic systems with time delay has attracted some researchers, and some interesting results have been reported in both deterministic and stochastic cases. But wecan't claim that the control problem ofthis class ofsystems is completely solved; more work must be done for this class of systems.