Neuroobserver‐Based Adaptive Output Control for Decentralized Systems Guaranteeing Transient Performance

ABSTRACT This work is focused on the neural adaptive output feedback control of decentralized uncertain nonlinear systems subject to bounded disturbance, unmodeled dynamics, and uncertainties. Specifically, pervasive and general decentralized systems are studied, and auxiliary variables are constructed to account for the unmodeled dynamics and the uncertainties associated with high‐order interconnection terms. Neuro‐observer is introduced to obtain the unmeasured states online, and output feedback adaptive control is implemented. Then, adaptive dynamic surface controller is designed by Lyapunov method and the problem of “explosion of complexity” in traditional backstepping method is eliminated. By initialization technique, not only the ultimately uniformly boundedness of all closed‐loop signals is obtained but also the transient performance of tracking errors can be guaranteed in the sense of norm. Numerical and practical simulation examples are performed to validate the effectiveness of the proposed control scheme.