Robust adaptive neural control for pure-feedback stochastic nonlinear systems with Prandtl-Ishlinskii hysteresis

This work considers robust adaptive neural-based control of pure-feedback stochastic nonlinear systems with the generalized Prandtl-Ishlinskii hysteresis. The mean-value theorem is employed to handle the non-affine difficulties from the generalized Prandtl-Ishlinskii hysteresis and the pure-feedback systems. By using the radial basis function (RBF) neural networks' universal approximation capability and back-stepping technique, an adaptive neural control scheme with minimum adaptive parameter is developed. The presented controller can guarantee the semi-global boundedness in fourth-moment of all signals of the resulting closed-loop system. Furthermore, the system output is ensured to converge to a small domain of the given trajectories. Simulation results are presented to demonstrate the effectiveness of the scheme. (C) 2018 Elsevier B.V. All rights reserved.