Bøger i Intelligent Control and Learning Systems serien

This book intends to report new optimal control results with critic intelligence for complex discrete-time systems, which covers the novel control theory, advanced control methods, and typical applications for wastewater treatment systems. Therein, combining with artificial intelligence techniques, such as neural networks and reinforcement learning, the novel intelligent critic control theory as well as a series of advanced optimal regulation and trajectory tracking strategies are established for discrete-time nonlinear systems, followed by application verifications to complex wastewater treatment processes. Consequently, developing such kind of critic intelligence approaches is of great significance for nonlinear optimization and wastewater recycling. The book is likely to be of interest to researchers and practitioners as well as graduate students in automation, computer science, and process industry who wish to learn core principles, methods, algorithms, and applications in the field of intelligent optimal control. It is beneficial to promote the development of intelligent optimal control approaches and the construction of high-level intelligent systems.

This book describes the research progress of the control design about strict-feedback nonlinear systems. A novel gain control design method is proposed, which greatly simplifies the construction procedure of controller for strict-feedback nonlinear systems. The control design problem of strict-feedback nonlinear systems is converted into the determination problem of gain parameters or the construction of dynamic gain equations. Therefore, the tedious iterative design procedure is effectively avoided. This book can be used as a reference for researchers in the field of control theory and engineers seeking advanced methods in practical control applications.

This book investigates the disagreement behavior analysis problems for signed networks in the presence of both cooperative and antagonistic interactions among agents. Owing to the existing antagonistic interactions, signed networks exhibit a variety of disagreement behaviors subject to different topology conditions, especially in comparison with commonly considered unsigned networks involving only cooperative interactions among agents. Since signed networks are generally adopted to describe the dynamics of some practical network systems, they have attracted much attention in many areas, such as biology, sociology, economics, and politics. By focusing on agents with the first-order linear dynamics, the book establishes the systematic behavior analysis frameworks for signed networks, under which diverse disagreement behaviors have been disclosed, including both convergence and fluctuation behaviors, regardless of static or dynamic network topologies. In particular, a class of dynamic signed networks has been introduced, together with the associated dynamic distributed controller design and disagreement behavior analysis of agents. This book is intended for undergraduate and graduate students, engineers, and researchers who are interested in control of network systems, multi-agent systems, social networks, and so on.

This book belongs to the subject of control and systems theory. It studies a novel data-driven framework for the design and analysis of iterative learning control (ILC) for nonlinear discrete-time systems. A series of iterative dynamic linearization methods is discussed firstly to build a linear data mapping with respect of the system's output and input between two consecutive iterations. On this basis, this work presents a series of data-driven ILC (DDILC) approaches with rigorous analysis. After that, this work also conducts significant extensions to the cases with incomplete data information, specified point tracking, higher order law, system constraint, nonrepetitive uncertainty, and event-triggered strategy to facilitate the real applications. The readers can learn the recent progress on DDILC for complex systems in practical applications. This book is intended for academic scholars, engineers, and graduate students who are interested in learning control, adaptive control, nonlinear systems, and related fields.

This book provides up-to-date developments in the stability analysis and (anti-)synchronization control area for complex-valued neural networks systems with time delay. It brings out the characteristic systematism in them and points out further insight to solve relevant problems. It presents a comprehensive, up-to-date, and detailed treatment of dynamical behaviors including stability analysis and (anti-)synchronization control. The materials included in the book are mainly based on the recent research work carried on by the authors in this domain.The book is a useful reference for all those from senior undergraduates, graduate students, to senior researchers interested in or working with control theory, applied mathematics, system analysis and integration, automation, nonlinear science, computer and other related fields, especially those relevant scientific and technical workers in the research of complex-valued neural network systems, dynamic systems, and intelligent control theory.

This book provides recent theoretical results and applications of the consensus control for nonlinear leader-following systems. Combining adaptive control technique, fuzzy logic systems, neural networks with the other control techniques or approaches, this book investigates the consensus control problem of leader-following systems and proposes the corresponding control scheme. This book intends to provide the readers a good understanding on consensus control based on adaptive control technology. This book can serve as a reference for the main research issues and results on nonlinear multi-agent systems including leader-following systems for researchers devoting to various areas of control theory, as well as a material for graduate and undergraduate students interested in nonlinear multi-agent systems including leader-following systems and their applications. Some prerequisites for reading this book include nonlinear system theory, matrix theory, mathematics, basic graph theory, and soon.

This book delves into the complexities of fault estimation and fault-tolerant control for nonlinear time-delayed systems. Through the use of multiple-integral observers, it addresses fault estimation and active fault-tolerant control for time-delayed fuzzy systems with actuator faults and both actuator and sensor faults. Additionally, the book explores the use of sliding mode control to solve issues of sensor fault estimation, intermittent actuator fault estimation, and active fault-tolerant control for time-delayed switched fuzzy systems. Furthermore, it presents the use of H¿ guaranteed cost control for both time-delayed switched fuzzy systems and time-delayed switched fuzzy stochastic systems with intermittent actuator and sensor faults. Finally, the problem of delay-dependent finite-time fault-tolerant control for uncertain switched T-S fuzzy systems with multiple time-varying delays, intermittent process faults and intermittent sensor faults is studied. The research on fault estimation and tolerant control has drawn attention from engineers and scientists in various fields such as electrical, mechanical, aerospace, chemical, and nuclear engineering. The book provides a comprehensive framework for this topic, placing a strong emphasis on the importance of stability analysis and the impact of result conservatism on the design and implementation of observers and controllers. It is intended for undergraduate and graduate students interested in fault diagnosis and tolerant control technology, researchers studying time-varying delayed T-S fuzzy systems, and observer/controller design engineers working on system stability applications.

This open access book assesses the potential of data-driven methods in industrial process monitoring engineering.