Neural-network-based adaptive tracking control for nonlinear pure-feedback systems subject to periodic disturbance

Neural-network-based adaptive tracking control for nonlinear pure-feedback systems subject to periodic disturbance

Zuo, Renwei (Air Force Engineering University China)
Lv, Maolong (TU Delft Team Bart De Schutter)
Li, Yinghui (Air Force Engineering University China)
Nie, Hongyan (China United Northwest Institute forEngineering Design and Research Corporation, Xi'an)

2021

This paper presents an adaptive neural control to solve the tracking problem of a class of pure-feedback systems with non-differentiable non-affine functions in the presence of unknown periodically time-varying disturbances. To handle with the design difficulty from non-affine structure of pure-feedback system, a continuous and positive control gain function is constructed to model the periodically disturbed non-affine function as a form that facilitates the control design. As a result, the non-affine function is not necessary to be differentiable with respect to control variables or input. In addition, the bounds of non-affine function are unknown functions, and some appropriate compact sets are introduced to investigate the bounds of non-affine function so as to cope with the difficulty from these unknown bounds. It is proven that the closed-loop control system is semi-globally uniformly ultimately bounded by choosing the appropriate design parameters. Finally, comparative simulations are provided to illustrate the effectiveness of the proposed control scheme.

non-affine function
periodic disturbance
Pure-feedback system
robust compensator

http://resolver.tudelft.nl/uuid:a1b7cb1f-e963-4e36-b2d6-42eeea8b5cd3

https://doi.org/10.1080/00207179.2021.1918350

0020-7179

International Journal of Control, 95 (9), 2554-2564

Institutional Repository

journal article

© 2021 Renwei Zuo, Maolong Lv, Yinghui Li, Hongyan Nie