Neuro-adaptive backstepping control of SISO non-affine systems with unknown gain sign.
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.
Electrical Engineering Department, Iran University of Science and Technology, Tehran 16846-13114, Iran. Electronic address: zahra_ramezani@elec.iust.ac.ir.
Zahra
Z
Ramezani
Department of Power and Control Engineering, School of Electrical and Computer Engineering, Shiraz University, 71348-51154 Shiraz, Iran. Electronic address: arefi@shirazu.ac.ir.
Mohammad Mehdi
MM
Arefi
Department of Electrical Engineering, Lamar University, Beaumont, TX 77710, USA. Electronic address: h.zargar@lamar.edu.
Hassan
H
Zargarzadeh
Electrical Engineering Department, Iran University of Science and Technology, Tehran 16846-13114, Iran. Electronic address: jahedmr@iust.ac.ir.
Mohammad Reza
MR
Jahed-Motlagh
Backstepping technique; Neuro-adaptive control; Nonlinear non-affine systems; Nussbaum-type function; Observer-based control