Deterministic Learning‐Based Adaptive Neural Tracking Control for Nonlinear Systems Subject to External Disturbances

ABSTRACT

This article intends to investigate the deterministic learning‐based adaptive neural tracking problem for a class of nonlinear systems with external disturbances. Different from the existing studies on deterministic learning problems, we accommodate a class of external disturbances that is a combination of a finite number of sinusoidal signals with unknown initial angles and amplitudes, and step signals with unknown constant step magnitudes. Based on the internal model principle, a new version of a deterministic learning strategy is proposed to realize trajectory tracking, unknown dynamics learning, and disturbance rejection of nonlinear systems. Finally, simulation results demonstrate the effectiveness of the proposed control strategy.