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Full Description
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.
Contents
Introduction.- Neural Network-based Adaptive Tracking Control for Nonlinear Multiagent Systems: The Observer Case.- Event-Triggered Cooperative Adaptive Neural Control for Cyberphysical Systems with Unknown State.- Event-triggered Adaptive Fault-tolerant Control for Nonlinear Multiagent Systems with Sensor and Actuator Faults.- Event-triggered Adaptive Finite-time Control for Switched Cyberphysical Systems with Uncertain Deception Attacks.- Adaptive Control for Uncertain Nonlinear Systems With Dynamic Full State Constraints: The SMDO Approach.- Observer-Based Event-Triggered Adaptive Control for Nonlinear Multiagent Systems with Unknown States and Disturbances.- Optimized Adaptive Finite-Time Consensus Control for Stochastic Nonlinear Multiagent Systems with Non-affine Nonlinear Faults.- Data-Driven Fault-Tolerant Reinforcement Learning Containment Control for Nonlinear Multiagent Systems.
