Neuro-adaptive backstepping control of SISO non-affine systems with unknown gain sign.

Autor: Ramezani Z; Electrical Engineering Department, Iran University of Science and Technology, Tehran 16846-13114, Iran. Electronic address: zahra_ramezani@elec.iust.ac.ir., Arefi MM; Department of Power and Control Engineering, School of Electrical and Computer Engineering, Shiraz University, 71348-51154 Shiraz, Iran. Electronic address: arefi@shirazu.ac.ir., Zargarzadeh H; Department of Electrical Engineering, Lamar University, Beaumont, TX 77710, USA. Electronic address: h.zargar@lamar.edu., Jahed-Motlagh MR; Electrical Engineering Department, Iran University of Science and Technology, Tehran 16846-13114, Iran. Electronic address: jahedmr@iust.ac.ir.
Jazyk: angličtina
Zdroj: ISA transactions [ISA Trans] 2016 Nov; Vol. 65, pp. 199-209. Date of Electronic Publication: 2016 Sep 20.
DOI: 10.1016/j.isatra.2016.08.024
Abstrakt: This paper presents two neuro-adaptive controllers for a class of uncertain single-input, single-output (SISO) nonlinear non-affine systems with unknown gain sign. The first approach is state feedback controller, so that a neuro-adaptive state-feedback controller is constructed based on the backstepping technique. The second approach is an observer-based controller and K-filters are designed to estimate the system states. The proposed method relaxes a priori knowledge of control gain sign and therefore by utilizing the Nussbaum-type functions this problem is addressed. In these methods, neural networks are employed to approximate the unknown nonlinear functions. The proposed adaptive control schemes guarantee that all the closed-loop signals are semi-globally uniformly ultimately bounded (SGUUB). Finally, the theoretical results are numerically verified through simulation examples. Simulation results show the effectiveness of the proposed methods.
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Databáze: MEDLINE
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