Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5004394 | ISA Transactions | 2015 | 12 Pages |
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.
Keywords
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Physical Sciences and Engineering
Engineering
Control and Systems Engineering
Authors
Muhammad Saleheen Aftab, Muhammad Shafiq,