Neural network-based event-triggered data-driven control of disturbed nonlinear systems with quantized input

This paper is devoted to design an event-triggered data-driven control for a class of disturbed nonlinear systems with quantized input. A uniform quantizer reconstructed with decreasing quantization intervals is employed to reduce the quantization error. A neural network-based estimation strategy is proposed to estimate both the pseudo partial derivative and disturbances. Consequently, an input triggering rule for single-input single-output systems is provided by incorporating the estimated disturbances, the quantization error bound and tracking errors. Resorting to the Lyapunov method, sufficient conditions for synthesized error systems to be uniformly ultimately bounded are presented. The validity of the proposed scheme is demonstrated via a simulation example.