Adaptive NN Control of Input-Delayed Stochastic Nonlinear Systems With States and Input Quantization

In this article, the adaptive neural control problem is studied for the input-delayed stochastic nonlinear system that has both states and input quantization. The main difficulty is to deal with the discontinuity of the virtual control laws caused by states quantization, and to compensate the influence of the input quantization by building the relation between the control input and quantized input. Moreover, this paper considers a sector bounded quantizer and proposes a new adaptive neural network (NN) control algorithm to handle the problem of quantization errors cannot be automatically bounded. For the unknown function in stochastic strict-feedback systems, the radial basis function neural network (RBFNN) is utilized to approximate it. The Pade approximation technique is employed to tackle the issue of input delay. All the closed-loop signals can maintain semi-globally uniformly ultimately bounded (SGUUB) in probability under the constructed controller and quantizer. Lastly, the efficiency of the developed control method is evaluated by a simulation experiment.