Event-driven NN adaptive fixed-time control for nonlinear systems with guaranteed performance

Abstract This article investigates the adaptive neural network fixed-time tracking control for a class of strict-feedback nonlinear systems with prescribed performance demands, in which radial basis function neural network (RBFNN) is utilized to approximate the unknown items. First, an improved fractionalorder dynamic surface control (FODSC) technique is incorporated to address the issue of the iterative derivation, where a fractional-order filter is adopted to improve the filter performance. What's more, the error compensation signal is established to remove the impact of filter error. Furthermore, a fixed-time adaptive event-triggered controller is constructed to reduce the communication burden, where the Zeno-behavior can also be excluded. Stability results prove that the designed controller not only guarantees all the signals of the closedloop systems (CLS) are practically fixed-time bounded, but also the tracking error can be regulated to a predefined boundary. Finally, the feasibility and superiority of the designed control algorithm are verified by two simulation examples.