A Direct Method for Robust Adaptive Nonlinear Control with Unknown Hysteresis

It is usually difficult and challenging to develop a general control framework for systems in the presence of unknown hysteresis nonlinearities. Based on the Prandtl-Ishlinskii model and the direct method for robust adaptive design given in [1], this paper deals with robust adaptive control of a class of uncertain nonlinear systems preceded by unknown hysteresis nonlinearity. By utilizing the Prandtl-Ishlinskii model and a neural networks approximator, the robust adaptive control developed ensures that all the close-loop system signals are bounded, and the tracking error converges to a set of adjustable neighborhood of zero independent of initial conditions.