NN-based Output Tracking for More General Stochastic Nonlinear Systems with Unknown Control Coefficients

This paper considers the output tracking problem for more general classes of stochastic nonlinear systems with unknown control coefficients and driven by noise of unknown covariance. By utilizing the radial basis function neural network approximation method and backstepping technique, we successfully construct a controller to guarantee the solution process to be bounded in probability. The tracking error signal is 4th-moment semi-globally uniformly ultimately bounded (SGUUB) and can be regulated into a small neighborhood of the origin in probability. A simulation example is given to demonstrate the effectiveness of the control scheme.