Neural model-based adaptive control for systems with unknown Preisach-type hysteresis
Chuntao LI
Yonghong TAN
摘要：An adaptive control scheme is presented for systems with unknown hysteresis. In order to handle the case where the hysteresis output is unmeasurale, a novel model is firstly developed to describe the characteristic of hysteresis. This model is motivated by Preisach model but implemented by using neural networks (NN). The main advantage is that it is easily used for controller design. Then, the adaptive controller based on the proposed model is presented for a class of SISO nonlinear systems preceded by unknown hysteresis, which is estimated by the proposed model. The laws for model updating and the control laws for the neural adaptive controller are derived from Lyapunov stability theorem, therefore the semi- global stability of the closed-loop system is guaranteed. At last, the simulation results are illustrated.
机标关键词：adaptive control schemesimulation resultsLyapunov stabilitynonlinear systemscontroller designglobal stabilityneural networksPreisach model
分类号：O1(数学)
资助基金：国家自然科学基金(50265001)广西科学基金(0339068)
论文发表日期：2004-01-01
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：9( 51-59 )
英文信息