Globally Stable Adaptive Backstepping Neural Network Control for Uncertain Strict-Feedback Systems With Tracking Accuracy Known a Priori

• Published in: IEEE transaction on neural networks and learning systems Vol. 26; no. 9; pp. 1842 - 1854
• Main Authors: Weisheng Chen, Ge, Shuzhi Sam, Jian Wu, Maoguo Gong
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Adaptive backstepping design; Adaptive systems; Algorithms; Approximation methods; Artificial neural networks; Backstepping; Barbalat's Lemma; Computer Simulation; Control systems; Feedback; Humans; Lyapunov methods; Neural networks; Neural Networks, Computer; Nonlinear Dynamics; radial basis function (RBF) neural network (NN); tracking accuracy known a priori; Tracking stock; uncertain strict-feedback system; Uncertainty; tracking accuracy known a priori; Adaptive backstepping design; uncertain strict-feedback system; Barbalat’s Lemma; radial basis function (RBF) neural network (NN)

This paper addresses the problem of globally stable direct adaptive backstepping neural network (NN) tracking control design for a class of uncertain strict-feedback systems under the assumption that the accuracy of the ultimate tracking error is given a priori. In contrast to the classical adaptive...