This book provides a systematic and in-depth introduction to distributed optimal adaptive cooperative control for multiagent systems from a theoretical perspective. The major research topics include: adaptive neural networks-based control schemes under multiconstraints, adaptive optimal control, event-triggered adaptive optimal control and data-based reinforcement learning control. The comprehensive and systematic treatment of adaptive optimal control in multiagent systems is one of the major features of the book, which is particularly suitable for readers who are interested in learning principles and methods for dealing with control resource constraints in multiagent systems and designing energy-saving control protocols. The book can benefit researchers, engineers, and graduate students in the fields of complex networks, smart grids, applied mathematics, electrical and electronic engineering, computer engineering, etc.