Robust Adaptive Nonlinear Control Law for a General Class of Nonlinear Systems with Operator-Based Hysteresis Models

For the nonlinear systems preceded by smart actuators which exhibit hysteresis nonlinearities, it is a challenge to mitigate effects of the hysteresis. By utilizing an operator-based Prandtl-Ishlinskii model and a neural network approximator, a robust adaptive control scheme is developed for a general class of continuous-time nonlinear dynamic systems with unknown hysteresis nonlinearities. The boundedness of the closed-loop system is achieved and the tracking error converges to a set of adjustable neighborhood of zero independent of initial conditions. The effectiveness of the proposed control approach is demonstrated through a simulation example.