This book presents a comprehensive and detailed study on iterative learning control (ILC) for systems with iteration-varying trial lengths. Instead of traditional ILC, which requires systems to repeat on a fixed time interval, this book focuses on a more practical case where the trial length might randomly vary from iteration to iteration. The iteration-varying trial lengths may be different from the desired trial length, which can cause redundancy or dropouts of control information in ILC, making ILC design a challenging problem. The book focuses on the synthesis and analysis of ILC for both linear and nonlinear systems with iteration-varying trial lengths, and proposes various novel techniques to deal with the precise tracking problem under non-repeatable trial lengths, such as moving window, switching system, and searching-based moving average operator. It not only discusses recent advances in ILC for systems with iteration-varying trial lengths, but also includes numerous intuitive figures to allow readers to develop an in-depth understanding of the intrinsic relationship between the incomplete information environment and the essential tracking performance. This book is intended for academic scholars and engineers who are interested in learning about control, data-driven control, networked control systems, and related fields. It is also a useful resource for graduate students in the above field.

Random fading communication is a type of attenuation damage of data over certain propagation media. Establishing a systematic framework for the design and analysis of learning control schemes, the book studies in depth the iterative learning control for stochastic systems with random fading communication. The authors introduce both cases where the statistics of the random fading channels are known in advance and unknown. They then extend the framework to other systems, including multi-agent systems, point-to-point tracking systems, and multi-sensor systems. More importantly, a learning control scheme is established to solve the multi-objective tracking problem with faded measurements, which can help practical applications of learning control for high-precision tracking of networked systems. The book will be of interest to researchers and engineers interested in learning control, data-driven control and networked control systems.

The Intelligent Systems Series comprises titles that present state of the art knowledge and the latest advances in intelligent systems. Its scope includes theoretical studies, design methods, and real-world implementations and applications."

Service Science, Management, and Engineering" presents the latest issues and development in service science. Both theory and applications issues are covered in this book, which integrates a variety of disciplines, including engineering, management, and information systems. These topics are each related to service science from various perspectives, and the book is supported throughout by applications and case studies that showcase best practice and provide insight and guidelines to assist in building successful service systems.

Presents the latest research on service science, management and engineering, from both theory and applications perspectives
Includes coverage of applications in high-growth sectors, along with real-world frameworks and design techniques
Applications and case studies showcase best practices and provide insights and guidelines to those building and managing service systems