title: Observer-Based Adaptive Neural Network Control for Nonlinear Systems in Nonstrict-Feedback Form.
abstract: 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.
authors.forename: Bing
authors.initials: B
authors.lastname: Chen
authors.forename: Huaguang
authors.initials: H
authors.lastname: Zhang
authors.forename: Chong
authors.initials: C
authors.lastname: Lin