The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Typesetting: Digital data supplied by author. Data-conversion by PTP-Berlin, Stefan Sossna e.K. Cover-Design: design & production GmbH, Heidelberg Printed on acid-free paper 62/3020Rw -5 4 3 2 1 0 TO OUR WIVES, BELOVED CHILDREN AND DEAR PARENTS Preface Modern technological systems rely on sophisticated control functions to meet increased performance requirements. For such systems, Fault Tolerant Control Systems FTCSs need to bedeveloped. FTCSs can bebroadly classi ed into passive and active.A Passive FTCS PFTCS can tolerate a prede ned set of faults while accomplishing its mission satisfactory without the need for control recon guration.Active FTCS AFTCS, on the other hand, relies on a Fault Detection and Identication FDI process to monitor system performance, and to detect and isolate faults in the system. Accordingly, the control law is recon gured on-line. The dynamic behavior of AFTCS can be modelled by S t o c hastic Di erential Equations SDE, due to the fact that faults are random in nature, and the FDI decisions are non-deterministic.In general, SDE can beclassi ed into two categories: SDE perturbed by white Gaussian noise Ito di erential equations, and SDE whose coe cients vary randomly with Markovian characteristics hybrid systems.The dynamic behavior of an AFTCS belongs to the class of hybrid systems. It is hybrid because it combines boththe Euclidean space for system dynamics and the discrete space for fault-induced changes. Stochastic stability of AFTCS is of prime importance. Substantial results for the stability of hybrid systems were obtained using the Lyapunov function approach and the supermartingale property.
The book deals with an emerging engineering discipline, viz, the Computational Intelligence that has rapidly found wide application in various branches of science and technology. The term Computational Intelligence is largely understood as a collection of intelligent computational methodologies, such as neuro-computing, fuzzy logic-based computing, and evolutionary computing that help in solving complex computational problems, not solvable or at least not easily solvable, using the conventional mathematical tools.The research activity in combined application of different intelligent approaches to problem solving was initiated by Zadeh in 1994 [1] who has introduced the term soft computing with fuzzy logic, neuro-computing, and probabilistic reasoning as its principal constituents. Later, this term was extended to include the evolutionary computation and learning strategies. Also, the statistical version of evolutionary computation was developed relying on randomized global search paradigms suitable for finding the optimal solution of multi-dimensional problems. Thereafter, the basic search strategies have been widely extended and diversified to include the novel search strategies, such as genetic algorithms, genetic programming, evolutionary strategies, evolutionary programming, differential evolution, etc.However, the most decisive step in formulating the term Computational Intelligence was made during the 1994 IEEE World Congress on Computational Intelligence (WCCI) [2]. At that time, R. J. Marks, in his Editorial Note to the IEEE Transactions on Neural Networks [3], pointed out that, although seeking similar goals, computational intelligence has emerged as a sovereign field distinct from artificial intelligence. Since that time the WCCI has become a regular event. In addition, in 2006, the IEEE Magazine on Computational Intelligence was launched.During the last decade, Computational Intelligence approaches have again and again proved their efficiency in solving complex scientific and engineering problems that are not easily solvable using conventional computational methods. This spans signal processing, multisensor data fusion, pattern recognition, performance monitoring, fault diagnosis, etc. Since the majority of such problems are based on experimental observations and on collection of experimental data, mainly structured and analysed in the form of time series, the book under review is mainly focussed on time series analysis and forecasting of experimental data in engineering.
A DTFTCS in noisy environments subject to delays and driven by a state feedback controller is developed. Second moment stability for the proposed delayed DTFTCS is investigated. A delay-independent sufficient condition that guarantee the H∞ second moment stability and achieve δ-level of disturbance rejection is derived and proved. Results are obtained using Lyapunov function approach and formulated as feasibility solution for a set of linear matrix inequalities (LMI). The theory is demonstrated by a numerical example.
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