Adaptive H-infinity tracking control for a class of stochastic nonlinear systems with completely unknown functions

In this paper, a novel approach based on backstepping technique is proposed to design an adaptive robust H-infinity tracking controller for a class of strict feedback stochastic nonlinear systems, in which all the functions are unknown. This method can guarantee that the controlled system has an H-infinity performance index under the condition that the stochastic nonlinear system is uniformly ultimately bounded in probability and the H-infinity tracking controller can be obtained easily. At the same time, based on the control scheme, the assumption that approximation errors must be square-integral in some literature has been eliminated. Radial basis function (RBF) neural networks are used to approximate the packaged unknown nonlinear functions. The designed controller can guarantee that the tracking error and other all the signals in the closed-loop system are bounded in probability, and the controlled system has an H-infinity disturbance attenuation performance for external perturbations. Finally, the simulation results are given to demonstrate the feasibility and validity of the proposed method. © 2019, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.