Decentralized Adaptive Synchronization of a Stochastic Discrete-Time Multiagent Dynamic Model
SIAM Journal on Control and Optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Nonlinear adaptive control using neural networks and multiple models
Automatica (Journal of IFAC)
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In this paper, adaptive control is investigated for a class of discrete-time nonlinear multi-agent systems (MAS). Each agent is of uncertain dynamics and is affected by other agents in its neighborhood. An agent is able to sense the outputs of the agents inside its neighborhood but is unable to sense those outside its neighborhood. Among all the agents, there is a hidden leader, which knows the desired tracking trajectory, but it is affected by and can only affect those agents inside its neighborhood while all other agents are not aware of its leadership. The decentralized adaptive control is designed for each agent by using the information of its neighbors. Under the proposed decentralized adaptive controls, both rigid mathematical proof and simulation studies are provided to show that all the agents are guaranteed to reach their common goal, i.e., following the desired reference.