Discrete-Time Control Systems

This book presents Networked Control System (NCS) as a particular kind of a real-time distributed system (RTDS), composed of a set of nodes, interconnected by a network, and able to develop a complete

Stabilizing and Optimizing Control for Time-Delay Systems introduces three important classes of stabilizing controls for time-delay systems: non-optimal (without performance criteria); suboptimal

The focus of the present volume is stochastic optimization of dynamical systems in discrete time where - by concentrating on the role of information regarding optimization problems - it discusses the

This book presents multivariate time series methods for the analysis and optimal control of feedback systems. Although ships’ autopilot systems are considered through the entire book, the methods set

This book reports the results of exhaustive research work on modeling and control of vertical oil well drilling systems. It is focused on the analysis of the system-dynamic response and the eliminatio

This book presents up-to-date research developments and novel methodologies on fuzzy control systems. It presents solutions to a series of problems with new approaches for the analysis and synthesis o

This book provides an introduction to the analysis and control of Linear Parameter-Varying Systems and Time-Delay Systems and their interactions. The purpose is to give the readers some fundamental th

Analysis and control of time-delayed systems have been applied in a wide range of applications, ranging from mechanical, control, economic, to biological systems. Over the years, there has been a stea

Optimal control deals with the problem of finding a control law for a given system such that a certain optimality criterion is achieved. An optimal control is a set of differential equations describin

Linear, Time-varying Approximations to Nonlinear Dynamical Systems introduces a new technique for analysing and controlling nonlinear systems. This method is general and requires only very mild condit

This book presents the state-of-the-art in optimal filtering and control for polynomial and time-delay systems.

This book presents new methodologies for the design and analysis of adaptive control systems based on the backstepping approach. Our emphasis is on - namic uncertain systems with nonsmooth nonlinearit

The last thirty years have witnessed an enormous effort in the field of robust control of dynamical systems. The main objective of this book is that of presenting, in a unified framework, the main res

This monograph provides insight and fundamental understanding into the feedback control of nonlinear and hybrid process systems. It presents state-of-the-art methods for the synthesis of nonlinear fee

With recent powerful developments in processor and sensor technology, robotics and the closely related field of process control systems are experiencing rapid growth and attracting many new hardware

As control systems become increasingly complex, there is a growing need for software tools for use in their design and implementation. Introducing proven, free-to-use software from the NIST's real-tim

This thesis describes a method to control rare events in non-equilibrium systems, by applying physical forces to those systems, but without relying on numerical simulation techniques, such as copying

Originally published in 2000, this is the second volume of a comprehensive two-volume treatment of quadratic optimal control theory for partial differential equations over a finite or infinite time horizon, and related differential (integral) and algebraic Riccati equations. Both continuous theory and numerical approximation theory are included. The authors use an abstract space, operator theoretic approach, which is based on semigroups methods, and which unifies across a few basic classes of evolution. The various abstract frameworks are motivated by, and ultimately directed to, partial differential equations with boundary/point control. Volume 2 is focused on the optimal control problem over a finite time interval for hyperbolic dynamical systems. A few abstract models are considered, each motivated by a particular canonical hyperbolic dynamics. It presents numerous fascinating results. These volumes will appeal to graduate students and researchers in pure and applied mathematics and

Prevailing classification systems backed by the American Psychiatric Association (DSM) and World Health Organization (ICD) generally assume that mental health conditions are best represented by discrete entities that are qualitatively distinct from one another and from mental health. These diagnostic categories, such as major depression, antisocial personality disorder, and obsessive-compulsive disorder, are ubiquitous in research, clinical, training, legal, and public health contexts. Yet they are out of sync with a vast quantity of scientific data on the presentation, causes, and treatments of psychopathology. Empirically speaking, categorical diagnoses tend to be unreliable (over time and across reporters), have fuzzy boundaries with one another and mental health, do not capture many clinical presentations encountered in routine practice, and lack distinctive causes and recommended treatments. Dimensional perspectives recognize the same signs and symptoms as categorical rubrics, but

●Practical applications include examples, over 500 homework problems, and computer problems.●Describes techniques to convert analog signals, the analysis of linear time-invariant discrete-time systems and signals in the time domain, and bilateral and unilateral z-transform methods.●Analyzes signals and systems in the frequency domain, the Fourier series and transform in continuous- and discrete-time signals, and the sampling and reconstruction of continuous-time signals.●Examines realizations of IIR and FIR systems and techniques to design digital filters.●Looks at sampling-rate conversion and applications to multirate digital signal processing.●Discusses linear prediction, optimum linear filters and single-channel adaptive filers based on the LMS and RLS algorithms.

Application of Artificial Intelligence in Hybrid Electric Vehicle Energy Management presents the state-of-the-art in hybrid electric vehicle system modelling and management. With a focus on learning-based energy management strategies, the book provides detailed methods, mathematical models, and strategies designed to optimize the energy management of the energy supply module of a hybrid vehicle.The book first addresses the underlying problems in Hybrid Electric Vehicle (HEV) modeling, and then introduces several artificial intelligence-based energy management strategies of HEV systems, including those based on fuzzy control with driving pattern recognition, multi objective optimization, fuzzy Q-learning and Deep Deterministic Policy Gradient (DDPG) algorithms. To help readers apply these management strategies, the book also introduces State of Charge and State of Health prediction methods and real time driving pattern recognition. For each application, the detailed experimental process

The authors explain in this work a new approach to observing and controlling linear systems whose inputs and outputs are not fixed in advance. They cover a class of linear time-invariant state/signal system that is general enough to include most of the standard classes of linear time-invariant dynamical systems, but simple enough that it is easy to understand the fundamental principles. They begin by explaining the basic theory of finite-dimensional and bounded systems in a way suitable for graduate courses in systems theory and control. They then proceed to the more advanced infinite-dimensional setting, opening up new ways for researchers to study distributed parameter systems, including linear port-Hamiltonian systems and boundary triplets. They include the general non-passive part of the theory in continuous and discrete time, and provide a short introduction to the passive situation. Numerous examples from circuit theory are used to illustrate the theory.