Adaptive dynamic surface control for a class of time-delay nonlinear systems with hysteresis inputs and dynamic uncertainties

In this paper, a novel adaptive neural network dynamic surface control for a class of time delay nonlinear systems with dynamic uncertainties and unknown hysteresis which is described by saturated-type Prandtl-Ishlinskii model is proposed. The main advantages of our scheme are that Combining the Finite Covering Lemma (Heine-Borel Theorem) with neural networks, a novel method is proposed to approximate the time delay terms, which leads to the abandonment of the traditional Lyapunov-Krasovskii functionals; by introducing an initializing technique, the L_∞performance of the tracking error can be achieved and good transient performance can be guaranteed Simulation results are presented to demonstrate the efficiency of the proposed scheme.