title: Neural networks for tracking of unknown SISO discrete-time nonlinear dynamic systems.
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.
authors.affiliation: Department of Electrical & Computer Engineering, Sultan Qaboos University, Muscat, Oman. Electronic address: saleheen.aftab@gmail.com.
authors.forename: Muhammad Saleheen
authors.initials: MS
authors.lastname: Aftab
authors.affiliation: Department of Electrical & Computer Engineering, Sultan Qaboos University, Muscat, Oman. Electronic address: mshafiq@squ.edu.om.
authors.forename: Muhammad
authors.initials: M
authors.lastname: Shafiq
mesh_terms: D000465:Algorithms; D001132:Arm / Q000502:physiology; D002138:Calibration; D003198:Computer Simulation; D003199:Computer Systems; D004867:Equipment Design; D006801:Humans; D000069550:Machine Learning; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D012371:Robotics; D014584:User-Computer Interface
keywords: Decentralized control; Direct adaptive inverse control; Indirect adaptive Inverse control; Lyapunov function Neural tracking; Stable adaptive tracking