RBFNN-based event-triggered MFAC for discrete-time nonlinear systems with disturbances and data quantization

This parper concentrates on neural network-based event-triggered MFAC for discrete-time nonlinear systems with disturbances and data quantization. First, the logarithmic quantizer is adopted to quantify the input signal. By uti-lizing the radial basis function neural network, an estimation method is presented to estimate the unknown pseudopartial derivative and disturbances. To reduce the influence of disturbances, an improved control law is built on the estimated disturbances. By using the tracking error and the estimated disturbance, an event-triggered mechanism is designed to save the communication resources. Conditions are presented to guarantee that the tracking error is uniformly ultimately bounded. The validity of the proposed method is verified by simulation example.