title: Approximation-based adaptive neural control design for a class of nonlinear systems.
abstract: This paper focuses on approximation-based adaptive neural control of a class of nonlinear non-strict-feedback systems. Based on the structural characteristic and the monotonously increasing property of the system bounding functions, a variable separation method is first developed. By this method, an approximation-based adaptive backstepping approach is proposed for a class of nonlinear non-strict-feedback systems. It is shown that the proposed controller guarantees semi-global boundedness of all the signals in the closed-loop systems. Three examples are used to illustrate the effectiveness of the proposed approach.
authors.forename: Bing
authors.initials: B
authors.lastname: Chen
authors.forename: Kefu
authors.initials: K
authors.lastname: Liu
authors.forename: Xiaoping
authors.initials: X
authors.lastname: Liu
authors.forename: Peng
authors.initials: P
authors.lastname: Shi
authors.forename: Chong
authors.initials: C
authors.lastname: Lin
authors.forename: Huaguang
authors.initials: H
authors.lastname: Zhang
mesh_terms: D005246:Feedback; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics