Sliding mode control based on RBF neural network for a class of underactuated systems with input quantization and event-triggering

In network control, signal transmission between each system component is carried out through the communication network. Since the bandwidth of the network is limited, quantization is a vital and fundamental technology used to convert the continuous signal to an approximate signal with a finite number of discrete value levels. Furthermore, event-triggering is an effective method to reduce signal transmission frequency in network control. Input signal quantization and event-triggering are considered simultaneously in this study for a class of underactuated systems. First, the logarithmic quantizer is used to quantize the input signal, and then the quantized input signal is further processed by the event-triggered mechanism based on the fixed threshold strategy. Adopting the proposed adaptive control scheme with the aid of radial basis function (RBF) neural network-based sliding mode control, the states of the closed-loop system are guaranteed to be bounded, and the control goals can be achieved. Finally, the control effect is shown through the numerical simulations.