Huimin Zhang – författare

The Intelligent Cockpit Design in the Automotive Industry is a comprehensive analysis of the principles and architecture of multiple subsystems, basic software, and applications/services of the intelligent cockpit. It also reviews the principles and practices of the underlying technology of SoC or System on a Chip (SoCs integrate multiple components onto a single chip, offering advantages like reduced power consumption, smaller form factors, and enhanced performance). The first part of the book focuses on the origins, background and system architecture of the intelligent cockpit. The second part discuses the architecture, principles, and core technologies of the software and hardware technologies required for the intelligent cockpit. It covers vehicle buses, high-speed video transmission technology, data connection technology, display subsystems, visual subsystems, audio subsystems, basic software, applications, and services. The final part mainly focuses on the technical principles, design examples, and development trends of intelligent cockpit SoCs: covering cockpit SoC computing power evaluation standards, SoC design principles, automotive-grade chip evaluation standards, functional safety design principles, and future evolution pathsProvides an up-to-date overview of the rapidly changing field of automotive electrical and electronic architecture in automotive cockpit designCombines the characteristics of automotive electronics and consumer electronics, integrating the experiences of the mobile phone industry and the automotive industryThe author has wide and varied experience in the intelligent cockpit architecture and chip design industries

The discrete-time adaptive iterative learning control (DAILC) is discussed as a cutting-edge of ILC and can address random initial states, iteration-varying targets, and other non-repetitive uncertainties in practical applications.

This book belongs to the subject of control and systems theory. The discrete-time adaptive iterative learning control (DAILC) is discussed as a cutting-edge of ILC and can address random initial states, iteration-varying targets, and other non-repetitive uncertainties in practical applications. This book begins with the design and analysis of model-based DAILC methods by referencing the tools used in the discrete-time adaptive control theory. To overcome the extreme difficulties in modeling a complex system, the data-driven DAILC methods are further discussed by building a linear parametric data mapping between two consecutive iterations. Other significant improvements and extensions of the model-based/data-driven DAILC are also studied to facilitate broader applications. The readers can learn the recent progress on DAILC with consideration of various 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.