This book discusses the development and application of dynamic neural networks (DNNs) for solving complex motion control problems in redundant manipulators. Specifically, it presents a series of advanced DNNs, including noise-rejection DNNs, fuzzy-parameter DNNs, and so on, which are designed to optimize performance while ensuring robustness and computational efficiency. Based on the presented DNNs, this book further constructs a series of motion control schemes for redundant manipulators to address some key challenges such as cyclic motion, position and orientation tracking, and...

This book offers advanced iterative learning control (ILC) and optimization methods for industrial batch systems, facilitating engineering applications subject to time- and batch-varying process uncertainties that could not be effectively addressed by the existing ILC methods. In particular, advanced ILC designs based on the classical proportional-integral-derivative (PID) control loop are presented for the convenience of application, which could not only realize perfect tracking of the desired output trajectory under repetitive process uncertainties and disturbance, but also maintain...

This book provides a systematic overview and classification on modeling and control of different types of Distributed Parameter Systems (DPSs), and develops new methods to tackle some of these unsolved problems, facilitating a better understanding of DPSs and providing references for practical problem-solving in the relevant fields. All these methods presented in this book are verified by specific scenarios. It is worth mentioning that in the context of disciplinary integration and artificial intelligence, the research ideas, methods and models in this book can be further integrated and...