Output feedback neural adaptive control design for nonlinear time-delay systems

Abstract

This paper addresses the adaptive output feedback control design problem for a class of nonlinear systems with unknown state time delays by combining the dynamic gain and neural network. A novel reduced-order dynamic gain observer is introduced to estimate the unmeasured system states. Radial basis function neural networks (RBF NNs) are used to approximate unknown functions. An adaptive NN output feedback controller is designed based on the backstepping technique. By arranging the proper Lyapunov–Krasovskii functional, we prove that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded. Finally, a physical example and a numerical example are given to prove the effectiveness of the proposed control scheme.

Keywords:

• Adaptive output feedback control
• dynamic gain
• neural network (NN)
• nonlinear time-delay systems

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant number 61873330], the Taishan Scholarship Project of Shandong Province [grant number Ts2022], and Shandong Provincial Natural Science Foundation [grant number ZR2021ZD13].