Cybernetics and systems theory

This book highlights the presentation of methods for studying oscillations under external periodic influence and random changes in parameters in dynamic systems with nonlinearities that have discontinuities and kinks.

This book provides a complete round-up of developments concerned with the application of partial moments in system identification and data-driven modelling; it captures the essence of work carried out at the Laboratoire d'Informatique et d'Automatique pour les Systemes for more than 40 years.

The aim of this text is to provide an account of the fundamentals of thermodynamics which is accessible at graduate level to physicists, mathematicians and philosophers of physics.

As the Internet of Things (IoT) continues to evolve and integrate more deeply into various industries, the IoTCIT 2024 conference is emerging as a critical platform for sharing insights and advancements in IoT and its symbiotic technologies.

This book offers an exploration of the historical and philosophical aspects of the field of control systems engineering.

This book collects selected contributions from the 10th Recurrence Plot Symposium, held at the University of Tsukuba, Japan (August 28-30, 2023).

This is a book about computation, something which is ubiquitous in the modern world.

This book presents Analytical and Approximate Methods for Complex Dynamical Systems and introduces ideas of discontinuous mapping treated as complex dynamical systems.

This 2nd edition of the book focuses on the properties of stationary states in chaotic systems of particles or fluids, setting aside the theory of how these states are achieved.

This book offers a comprehensive dive into the rapidly evolving world of autonomous vehicles and their pivotal role in modern data collection and mission-critical operations.

Data-Driven, Nonparametric, Adaptive Control Theory introduces a novel approach to the control of deterministic, nonlinear ordinary differential equations affected by uncertainties.

Data-Driven, Nonparametric, Adaptive Control Theory introduces a novel approach to the control of deterministic, nonlinear ordinary differential equations affected by uncertainties.

This textbook presents a concise and comprehensive treatment of the theory of elasticity.

Control Systems Benchmarks helps control engineers, researchers, and students to evaluate and compare control system performance across a range of critical applications by offering a collection of real-world benchmarks.

This textbook presents a concise and comprehensive treatment of the theory of elasticity.

This book constitutes the referred proceedings of the 7th International Conference on Attacks and Defenses for Internet-of-Things, ADIoT 2024, held as an hybrid event, in Hangzhou, China, during December 13–14, 2024.

This book presents selected, peer-reviewed contributions from the International Symposium on Mathematical Analysis of Fractals and Dynamical Systems-2023 (ISMAFDS - 2023), held at the Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Tamil Nadu in India during August 24-25, 2023.

This book examines the problems of causal determinism and limited completeness in systems theory.

Dynamic Programming for Impulse Feedback and Fast Controls offers a description of feedback control in the class of impulsive inputs.

This book develops a coherent and quite general theoretical approach to algorithm design for iterative learning control based on the use of operator representations and quadratic optimization concepts including the related ideas of inverse model control and gradient-based design.

This comprehensive textbook presents a self-contained guide to bioinformatics, defined in its broadest sense as the application of information science to biology.

This book develops the understanding and skills needed to be able to tackle original control problems.

Structured Controllers for Uncertain Systems focuses on the development of easy-to-use design strategies for robust low-order or fixed-structure controllers (particularly the industrially ubiquitous PID controller).

Stock Market Modeling and Forecasting translates experience in system adaptation gained in an engineering context to the modeling of financial markets with a view to improving the capture and understanding of market dynamics.

The book comprises a rigorous and self-contained treatment of initial-value problems for ordinary differential equations.

Mathematical models can be used to meet many of the challenges and opportunities offered by modern biology.

Finite-time stability (FTS) is a more practical concept than classical Lyapunov stability, useful for checking whether the state trajectories of a system remain within pre-specified bounds over a finite time interval.

Sampled-data Models for Linear and Nonlinear Systems provides a fresh new look at a subject with which many researchers may think themselves familiar.

This second edition of the popular textbook contains a comprehensive course in modern probability theory, covering a wide variety of topics which are not usually found in introductory textbooks, including: * limit theorems for sums of random variables* martingales* percolation* Markov chains and electrical networks* construction of stochastic processes* Poisson point process and infinite divisibility* large deviation principles and statistical physics* Brownian motion* stochastic integral and stochastic differential equations.

This book shows how the use of S-variables (SVs) in enhancing the range of problems that can be addressed with the already-versatile linear matrix inequality (LMI) approach to control can, in many cases, be put on a more unified, methodical footing.

The subject of this brief is the application of linear parameter-varying (LPV) control to a class of dynamic systems to provide a systematic synthesis of gain-scheduling controllers with guaranteed stability and performance.

Regulation of the Power Sector is a unified, consistent and comprehensive treatment of the theories and practicalities of regulation in modern power-supply systems.

Advanced Topics in Control and Estimation of State-Multiplicative Noisy Systems begins with an introduction and extensive literature survey.

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.