Neuro-adaptive backstepping control of SISO non-affine systems with unknown gain sign. (November 2016)

• Record Type: Journal Article
• Authors: Ramezani, Zahra; Arefi, Mohammad Mehdi; Zargarzadeh, Hassan; Jahed-Motlagh, Mohammad Reza
• Is Part Of: ISA transactions. Volume 65(2016:Nov.)
• Page Start: 199
• Page End: 209
• Publication Date: 2016-11
• Subjects: Neuro-adaptive control -- Backstepping technique -- Nussbaum-type function -- Nonlinear non-affine systems -- Observer-based control; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2016.08.024
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: 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. Highlights: In the first approach, all the states are assumed to be available; a neuro-adaptive state-feedback backstepping control is designed. In the second approach it is assumed that the system states are not available for measurement. Therefore, an observer on K-filters is designed to estimate the immeasurable states. In two methods, the upper bound of uncertainties is not required to be known in advance. By using NN through an adaptation mechanism, this upper bound is approximated. One of the advantages of the proposed algorithms is that, the common strict positive real (SPR) condition is eliminated during the design procedure. The Nussbaum-gain technique is effectively employed to design two adaptive controllers for SISO nonlinear systems.