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 model-unknown scenarios. Each method is rigorously demonstrated for the convergence, and its effectiveness is validated through simulations and physical experiments. By integrating computational intelligence with control theory, this book provides a comprehensive framework for solving time-varying and noise-perturbed problems in robotics, making it a valuable resource for researchers and practitioners in the field.
