Decentralized event-trigger-based predefined-time adaptive control of large-scale nonlinear systems

This research introduces a decentralized adaptive control method targeting large-scale nonlinear systems (LSNS) that takes external disturbances and subsystem connections into account. The unknown nonlinear function term is handled using the radial basis function neural network (RBFNN). For the first time, predefined-time control (PTC) and event-triggered control (ETC) are combined together to establish LSNS’ decentralized control. The event-trigger-based PTC approach is created by fusing the PTC theory with the dynamic surface control strategy. To lessen computing complexity and prevent control singularities, the predefined-time filter (PTF) and the tanh function are implemented, respectively. Through rigorous theoretical analysis, the predefined-time stability of the considered LSNS is established, and the Zeno phenomenon is guaranteed to be absent. Simulation tests are employed to showcase the effectiveness and progress in the suggested control mechanism.