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Predefined-Time Integral Reinforcement Learning for Saturated Unknown Nonlinear Multi-Agent Systems Under FDI Attacks and Disturbances
This paper addresses secure leader-follower formation of unknown nonlinear multi-agent systems under actuator constraints, external disturbances, and false-data-injection (FDI) attacks. The graph-coupled coordination-error dynamics are formulated as local zero-sum differential games, where a nonquad
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