Disturbance observer based adaptive neural prescribed performance control for a class of uncertain nonlinear systems with unknown backlash-like hysteresis - Wang, Xinjun; Yin, Xinghui; Shen, Fei (Neurocomputing (Amsterdam), 2018)
In this paper, an adaptive neural tracking control is studied for a class of strict-feedback nonlinear systems with guaranteed predefined performance subject to unknown backlash-like hysteresis input, uncertain parameters and external unknown disturbance. An adaptive neural control method combined with backstepping technique, and the radial basis function neural networks (RBFNNs) is proposed for the systems under consideration. In recursive backstepping designs, the tracking control performance can be guaranteed by exploiting a new performance function. A disturbance observer is employed to approximate the unknown disturbance. It is shown that by using Lyapunov methods, the designed controller can guarantee the prespecified transient and ensure semi-globally uniformly ultimately bounded (SGUUB) of all signals within the closed-loop systems. Simulation results are presented to illustrate the validity of the approach.