Adaptive tracking control for a class of pure-feedback non-linear systems including actuator hysteresis and dynamic uncertainties

In this study, an adaptive neural network dynamic surface control for a class of pure-feedback non-linear systems with dynamic uncertainties and unknown hysteresis is proposed. The hysteresis is described by a saturated-type Prandtl-Ishlinskii (PI) model, which is more applicable than the traditional PI model. The main advantages of the authors scheme are that by introducing an initialising technique, the L_∞ performance of the tracking error can be achieved, the assumption on dynamic uncertainties is relaxed, the explosion of the complexity problem when the hysteresis is fused with back-stepping design can be eliminated, which together with the estimation of vector norm of unknown parameters makes the control law be simplified and the computational burden be greatly reduced. Simulation results are presented to demonstrate the efficiency of the proposed scheme.