Cybernetics and systems theory

Markov decision process (MDP) models are widely used for modeling sequential decision-making problems that arise in engineering, economics, computer science, and the social sciences.

There are many methods of stable controller design for nonlinear systems.

Robust Control Design with MATLAB® (second edition) helps the student to learn how to use well-developed advanced robust control design methods in practical cases.

The presence of uncertainty in a system description has always been a critical issue in control.

The relay feedback test (RFT) has become a popular and efficient in process identification and automatic controller tuning.

Robust and Adaptive Control shows the reader how to produce consistent and accurate controllers that operate in the presence of uncertainties and unforeseen events.

Stochastic Recursive Algorithms for Optimization presents algorithms for constrained and unconstrained optimization and for reinforcement learning.

Control of Discrete-event Systems provides a survey of the most important topics in the discrete-event systems theory with particular focus on finite-state automata, Petri nets and max-plus algebra.

Frequency Domain Criteria for Absolute Stability focuses on recently-developed methods of delay-integral-quadratic constraints to provide criteria for absolute stability of nonlinear control systems.

The theory of dynamical systems is a broad and active research subject with connections to most parts of mathematics.

Functional analysis owes much of its early impetus to problems that arise in the calculus of variations.

Stochastic Averaging and Extremum Seeking treats methods inspired by attempts to understand the seemingly non-mathematical question of bacterial chemotaxis and their application in other environments.

Arithmetic geometry and algebraic dynamical systems are flourishing areas of mathematics.

The intention of this book is not to add another technical work to the series of publications already available on matters connected with the relations between natural and artificial intelligence, nor to repeat the positions already well expressed in, for example, the debate between John Searle, Daniel Dennet and Hubert Dreyfus.

In the 70+ year history of control theory and engineering, few applications have stirred as much excitement as flow control.

Problems, methods and algorithms of decision making based on an uncertain knowledge now create a large and intensively developing area in the field of knowledge-based decision support systems.

Learning and Generalization provides a formal mathematical theory addressing intuitive questions of the type: How does a machine learn a concept on the basis of examples?

H-infinity control theory deals with the minimization of the H-infinity-norm of the transfer matrix from an exogenous disturbance to a pertinent controlled output of a given plant.

Pulse-coupled neural networks represent a new and exciting advance in image processing research.

This work is aimed at mathematics and engineering graduate students and researchers in the areas of optimization, dynamical systems, control sys- tems, signal processing, and linear algebra.

Concepts for Neural Networks - A Survey provides a wide-ranging survey of concepts relating to the study of neural networks.

Mobile Robotics: A Practical Introduction is an excellent introduction to the foundations and methods used for designing completely autonomous mobile robots.

This book has evolved from our combined experience of working in computing services at the University of London (for the last nine years at King's College, and before that eight years at Imperial College and seven at Chelsea College) in the teaching, advice and technical support of Fortran and related areas.

Search and Classification Using Multiple Autonomous Vehicles provides a comprehensive study of decision-making strategies for domain search and object classification using multiple autonomous vehicles (MAV) under both deterministic and probabilistic frameworks.

Stochastic reachability analysis (SRA) is a method of analyzing the behavior of control systems which mix discrete and continuous dynamics.

0 e This is the proceedings of the first annual symposium of the Safety-critical Systems Club (The Watershed Media Centre, Bristol, 9-11 February 1993), which provided a forum for exploring and discussing ways of achieving safety in computer systems to be used in safety-critical industrial applications.

Neural Network Applications contains the 12 papers presented at the second British Neural Network Society Meeting (NCM '91) held at King's College London on 1st October 1991.

Parallel Processing Applications for Jet Engine Control is a volume in the new Advances in Industrial Control series, edited by Professor M.

Professor Sluzalec is a well-known and respected authority in the field of Computational Mechanics, and his personal experience forms the basis of the book.

Not so if the book has been translated into Arabic.

Distributed-order differential equations, a generalization of fractional calculus, are of increasing importance in many fields of science and engineering from the behaviour of complex dielectric media to the modelling of nonlinear systems.

Effective decision making requires a clear methodology, particularly in complex, globally relevant situations.

The early 21st century has seen a renewed interest in research in the widely-adopted proportional-integral-differential (PID) form of control.

Real-time model predictive controller (MPC) implementation in active vibration control (AVC) is often rendered difficult by fast sampling speeds and extensive actuator-deformation asymmetry.

Complexity, Analysis and Control of Singular Biological Systems follows the control of real-world biological systems at both ecological and phyisological levels concentrating on the application of now-extensively-investigated singular system theory.

Increasing complexity and performance and reliability expectations make modeling of automotive system both more difficult and more urgent.

Distributed Decision Making and Control is a mathematical treatment of relevant problems in distributed control, decision and multiagent systems, The research reported was prompted by the recent rapid development in large-scale networked and embedded systems and communications.

Data Approximation by Low-complexity Models details the theory, algorithms, and applications of structured low-rank approximation.

Saturated Switching Systems treats the problem of actuator saturation, inherent in all dynamical systems by using two approaches: positive invariance in which the controller is designed to work within a region of non-saturating linear behaviour; and saturation technique which allows saturation but guarantees asymptotic stability.

Dynamical system theory has developed rapidly over the past fifty years.

Safety-related computer systems are those which may lead to loss of life, injury or plant and environmental damage.

As software is very complex, we can only test a limited range of the possible states of the software in a reasonable time frame.

In recent years, there has been a growing interest in applying neural networks to dynamic systems identification (modelling), prediction and control.

It is commonly believed that chaos is linked to non-linearity, however many (even quite natural) linear dynamical systems exhibit chaotic behavior.

Safety-critical systems, in the sense of software-based systems used in safety- critical applications, are 'high-tech'.

Joe Engelberger, the pioneer of the robotics industry, wrote in his 1989 book Robotics in Service that the inspiration to write his book came as a reaction to an industry-sponsored forecast study of robot applications, which predicted that in 1995 applications of robotics outside factories - the traditional domain of industrial robots - would amount to less than 1% of total sales.

Fractal analysis research is expanding into a variety of engineering domains.

Evolutionary Learning Algorithms for Neural Adaptive Control is an advanced textbook, which investigates how neural networks and genetic algorithms can be applied to difficult adaptive control problems which conventional results are either unable to solve , or for which they can not provide satisfactory results.