Bøger i Automation and Control Engineering serien

This book is based on the authors' recent research results on formation control problems, including time-varying formation, communication delays, fault tolerant formation, for multiple UAV systems with highly nonlinear and coupled, parameters uncertainties, and external disturbances.

This book presents innovative technologies and research results on adaptive control of dynamic systems with quantization, uncertainty and nonlinearity including theoretical success and practical development such as approaches for stability analysis, treatment of subsystem interactions, improvement of system tracking and transient performance.

The book explores the behavior of networks of electric and hybrid vehicles. Topics covered include: energy management issues for aggregates of plug-in vehicles; the design of sharing systems to support electro-mobility; context awareness in the operation of electric and hybrid vehicles, and the role that this plays in a Smart City context.

This book aims at information estimate methods when faults occur. It uses the model built from the plant or process, to detect and isolate failures, in contrast to traditional hardware or statistical technologies dealing with failures. It presents model-based learning and design methods for fault detection, isolation and identification.

This book is concerned with the development of design techniques for controlling motion of mechanical systems which are employed to execute certain tasks acting collaboratively. The book introduces unified control design procedure for functionally related systems.

Multi-Agent Systems: Platoon Control and Non-Fragile Quantized Consensus aims to present recent research results in designing platoon control and non-fragile quantized consensus for multi-agent systems. It investigates the platoon control issue by combining SMC technique with neural network and fuzzy logic system approximation methods.

The book focuses on how to design general PI/PD/PID controller with self-tuning gains for different systems, which includes SISO nonlinear system, SISO nonaffine system and MIMO nonlinear system. It provides an exposition of recently adaptive PI/PD/PID control design for nonlinear systems.

Illustrating the power, simplicity, and generality of the concept of flatness, this book explains how to identify, utilize, and apply flatness in system planning and design. It includes an assortment of exercises and models that range from elementary to complex classes of systems.

The book explores the behavior of networks of electric and hybrid vehicles. Topics covered include: energy management issues for aggregates of plug-in vehicles; the design of sharing systems to support electro-mobility; context awareness in the operation of electric and hybrid vehicles, and the role that this plays in a Smart City context.

The book presents recent advances in the theory of neural control for discrete-time nonlinear systems with multiple inputs and multiple outputs. It provides solutions for the output trajectory tracking problem of unknown nonlinear systems based on sliding modes and inverse optimal control scheme.

This work explains how to solve industrial system problems using a novel control system design theory. This easy-to-use theory does not require designers to choose a weighting function and enables the controllers to be designed or tuned for quantitative engineering performance indices such as overshoot. Equally emphasizing theoretical and application aspects of the subject, the book bridges classical control theory and the newly arriving generation of robust control theory. It includes numerous exercises and examples drawn from aeronautical, mechanical, and chemical engineering.

This book provides a systematic framework for designing distributed controllers for multi-agent systems with general linear agent dynamics, linear agent dynamics with uncertainties, and Lipschitz nonlinear agent dynamics. The proposed consensus region decouples the design of the feedback gain matrices of the cooperative protocols from the communication graph and serves as a measure for the robustness of the protocols to variations of the communication graph. By exploiting the decoupling feature, adaptive cooperative protocols are presented that can be designed and implemented in a fully distributed fashion.

Multiple intelligent agent systems are commonly used in research requiring complex behavior. Synchronization control provides an advantage in solving the problem of multi-agent coordination. This book focuses on the use of synchronization control to coordinate the group behavior of multiple agents. The author includes numerous real-world application examples from robotics, automation, and advanced manufacturing. Giving a detailed look at cross-coupling based synchronization control, the text covers such topics as adaptive synchronization control, synchronous tracking control of parallel manipulators, and minimization of contouring errors of CNC machine tools with synchronization controls.

Modeling fuzzy control has been a very active research and application area in control engineering. However, there are few books available that cover the area in any depth. Written by a well-known expert who has published more than 100 papers in top international journals, this book gives insight into the latest approaches for the analysis and synthesis of fuzzy systems. It systematically presents design techniques applied to the Takagi¿Sugeno class of fuzzy controllers widely used in successful practical applications. The book covers issues of robustness, stability, and conformity along with other state-of-the-art control techniques, such as model predictive control.

Using real-life examples to illustrate the performance of learning algorithms and instructing readers how to apply them to practical applications, this work offers a comprehensive treatment of subspace learning algorithms for neural networks. The authors summarize a decade of high quality research offering a host of practical applications. They demonstrate ways to extend the use of algorithms to fields such as encryption communication, data mining, computer vision, and signal and image processing to name just a few. The brilliance of the work lies with how it coherently builds a theoretical understanding of the convergence behavior of subspace learning algorithms through a summary of chaotic behaviors.

This book discusses controller design in discrete time for networked control systems (NCS). The authors apply several powerful modern control techniques in discrete time to the design of intelligent controllers for such NCS. Detailed derivations, rigorous stability proofs, computer simulation examples, and downloadable MATLAB® codes are included for each case. An ideal reference for graduate students, university researchers, and practicing engineers, this book instills a solid understanding of neural network controllers and how to build them.