Observer-Based Adaptive Neural Network Control for Nonlinear Systems in Nonstrict-Feedback Form

• Published in: IEEE transaction on neural networks and learning systems Vol. 27; no. 1; pp. 89 - 98
• Main Authors: Chen, Bing, Zhang, Huaguang, Lin, Chong
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive control systems; Adaptive neural control; Adaptive systems; Approximation; Artificial neural networks; Backstepping; Control design; Control systems; Controllers; Neural networks; Nonlinear systems; Nonlinearity; nonstrict-feedback structure; Radial basis function; State observers; Vectors; nonstrict-feedback structure; nonlinear systems; backstepping; Adaptive neural control
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2015.2412121

This paper focuses on the problem of adaptive neural network (NN) control for a class of nonlinear nonstrict-feedback systems via output feedback. A novel adaptive NN backstepping output-feedback control approach is first proposed for nonlinear nonstrict-feedback systems. The monotonicity of system bounding functions and the structure character of radial basis function (RBF) NNs are used to overcome the difficulties that arise from nonstrict-feedback structure. A state observer is constructed to estimate the immeasurable state variables. By combining adaptive backstepping technique with approximation capability of radial basis function NNs, an output-feedback adaptive NN controller is designed through backstepping approach. It is shown that the proposed controller guarantees semiglobal boundedness of all the signals in the closed-loop systems. Two examples are used to illustrate the effectiveness of the proposed approach.