Integrating the adaptive learning control, backstepping technology, and event-triggered mechanism, this book solve the problems of the strong nonlinearity, complex constraints and uncertainties that traditional methods cannot handle. This book breaks through the traditional control's reliance on mathematical model accuracy, providing more robust solutions for engineering scenarios such as unmanned systems and smart grids. This book provides a comprehensive constraint control theoretical framework for nonlinear multiagent systems, which provides modular design ideas for readers to flexibly apply. We provide the comparison tables to present the performance differences in convergence speed, robustness, and other indicators between traditional methods and methods in this book. This book shows how to integrate classic control with modern intelligent technologies such as adaptive learning and event-triggered, providing interdisciplinary ideas for readers to solve complex system problems. Studies on event-triggered control and constrain control have attracted academic researchers an engineers from various disciplines, such as electrical, computer science, mathematics,physics, and mechanical.The target audience of this book includes graduate students in artificial intelligence and related fields, technology workers in the field of intelligent control, and academic researchers interested in collaborative control of multiagent systems, event-triggered control, and constraint control.

Integrating the adaptive learning control, backstepping technology, and event-triggered mechanism, this book solve the problems of the strong nonlinearity, complex constraints and uncertainties that traditional methods cannot handle. This book breaks through the traditional control's reliance on mathematical model accuracy, providing more robust solutions for engineering scenarios such as unmanned systems and smart grids. This book provides a comprehensive constraint control theoretical framework for nonlinear multiagent systems, which provides modular design ideas for readers to flexibly apply. We provide the comparison tables to present the performance differences in convergence speed, robustness, and other indicators between traditional methods and methods in this book. This book shows how to integrate classic control with modern intelligent technologies such as adaptive learning and event-triggered, providing interdisciplinary ideas for readers to solve complex system problems. Studies on event-triggered control and constrain control have attracted academic researchers an engineers from various disciplines, such as electrical, computer science, mathematics,physics, and mechanical.The target audience of this book includes graduate students in artificial intelligence and related fields, technology workers in the field of intelligent control, and academic researchers interested in collaborative control of multiagent systems, event-triggered control, and constraint control.