Command Filtered Event-triggered Adaptive RBFNN Control for Uncertain Nonlinear System

With the aim of resolving the tracking issue in uncertain nonlinear systems with unknown functions, an adaptive event-triggered command filtered control approach that leverages radial basis function neural networks (RBFNNs) is explored. Firstly, the command filtering control technology is adopted to tackle "complexity of explosion" issue in the conventional backstepping approach, while an error compensation mechanism is incorporated to eliminate the errors arising from the filter. Secondly, RBFNNs are utilized for the approximation of unknown nonlinear functions, and a virtual controller that relies on neural networks is built. Then, with the aim of tackling the issue of frequent actuator updates, an enhanced event-triggered controller based on a time-varying threshold strategy is devised. Finally, a simulation shows operational efficacy of the designed control scheme.