SINGULARITY-FREE ADAPTIVE BACKSTEPPING DESIGN FOR STRICT-FEEDBACK SYSTEMS USING NEURAL NETWORKS

摘要：

A switching-type neural network (NN) based adaptive backstepping control design is presented for the tracking tasks of strict-feedback systems.It consists of four parts in each virtual control design step: a one-layer NN for approximating the unknown nonlinearity to render the adaptive control applicable; a certainty-equivalence adaptive controller for compensating the resembled nonlinearities; a high-gain controller which takes over temporarily once the former is approaching singularity; last, a nonlinear damping component for counteracting the degradation due to the approximation errors.Among others, it has the distinct features of requiring minimal prior knowledge of the unknown nonlinearities, less control effort, and relatively simple control structure.

关键词： Neural network Adaptive backstepping control design

作者: JENG-TZE HUANG

作者单位: Department of Electronic Engineering, Vanung University of Technology, Chungli 340, Taiwan

会议类型: 国际会议

会议名称: 2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

会议地点: 香港

会议语种:英文

页码: 2755-2760

在线出版日期: 2007-08-19（万方平台首次上网日期，不代表论文的发表时间）