Adaptive neural prescribed performance control for a class of strict-feedback stochastic nonlinear systems with hysteresis input

This paper studies an adaptive neural tracking control problem for a class of strict-feedback stochastic nonlinear systems with guaranteed predefined performance subject to unknown backlash-like hysteresis input. First, utilizing the prescribed performance control, the predefined tracking control performance can be guaranteed via exploiting a new performance function without considering the accurate initial error. Second, by integrating neural network approximation capability into the backstepping technique, a robust adaptive neural control scheme is developed to deal with unknown nonlinear functions, stochastic disturbances and unknown hysteresis input. The designed controller overcomes the problem of the over parameterization. Under the proposed controller, all the signals in the closed-loop system are 4-Moment (or 2 Moment) semi-globally uniformly ultimately bounded (SGUUB), and the prespecified transient and steady tracking control performance are guaranteed. Simulation studies are performed to demonstrate and verify the effectiveness of the proposed method. (C) 2017 Elsevier B.V. All rights reserved.