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)
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2014.2357451

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 backstepping NN control schemes, this paper analyzes the convergence of the tracking error using Barbalat's Lemma via some nonnegative functions rather than the positive-definite Lyapunov functions. Thus, the accuracy of the ultimate tracking error can be determined and adjusted accurately a priori, and the closed-loop system is guaranteed to be globally uniformly ultimately bounded. The main technical novelty is to construct three new n th-order continuously differentiable functions, which are used to design the control law, the virtual control variables, and the adaptive laws. Finally, two simulation examples are given to illustrate the effectiveness and advantages of the proposed control method.