Neural networks for tracking of unknown SISO discrete-time nonlinear dynamic systems. (November 2015)

• Record Type: Journal Article
• Authors: Aftab, Muhammad Saleheen; Shafiq, Muhammad
• Is Part Of: ISA transactions. Volume 59(2015:Nov.)
• Page Start: 363
• Page End: 374
• Publication Date: 2015-11
• Subjects: Lyapunov function Neural tracking -- Decentralized control -- Direct adaptive inverse control -- Indirect adaptive Inverse control -- Stable adaptive tracking; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2015.09.003
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: This article presents a Lyapunov function based neural network tracking (LNT) strategy for single-input, single-output (SISO) discrete-time nonlinear dynamic systems. The proposed LNT architecture is composed of two feedforward neural networks operating as controller and estimator. A Lyapunov function based back propagation learning algorithm is used for online adjustment of the controller and estimator parameters. The controller and estimator error convergence and closed-loop system stability analysis is performed by Lyapunov stability theory. Moreover, two simulation examples and one real-time experiment are investigated as case studies. The achieved results successfully validate the controller performance.