Event-Based Prescribed-Time Adaptive Neural Network Control for Uncertain Nonlinear Systems

This paper investigates the event-triggered (ET) practical prescribed-time (PPT) adaptive neural network control design problem for uncertain nonlinear systems. Neural networks technology is used to deal with unknown nonlinear functions in nonlinear systems. Unlike traditional command filtering control approaches, a novel PPT error compensation mechanism is introduced to precisely regulate the settling time of filtering errors, rather than the upper bound of the convergence time after scaling, thereby effectively reducing the impact of filtering errors. Meanwhile, an ET mechanism utilizing the switching threshold strategy is designed to reduce the high update frequency and enhance the control efficiency. It can be proved that all signals in nonlinear systems meet the PPT stability requirement. Finally, the effectiveness of the proposed scheme is demonstrated through a numerical example.