Extended state observer-based quantitative model-free adaptive iterative learning control
GUO Xiao-lin
LIU Yang
LIN Na
CHI Rong-hu
Abstract：A quantized model-free adaptive iterative learning control based on extended state observer is proposed for nonlinear non-affine discrete-time systems.An iterative dynamic linearization method is introduced to deal with nonlinear and non-affine structural uncertainties,and an iterative linear data model(iLDM)based on partial form is proposed.The error quantization description is given,and the learning control law based on quantized data and the parameter iteration adaptive law are designed.The latter can not only estimate the uncertain parameters of iLDM,but also adjust the learning gain of the control law,which enhances the robustness of the control scheme.At the same time,an extended state observer in the iterative domain is designed to estimate and compensate multiple non-repeated uncertainties such as parameter estimation,unmodeled dynamics and external disturbances.Both mathematical analysis and simulation study demonstrate the effectiveness of the proposed method.
Keywords：iterative learning controldata quantificationextended state observermultiple non-repeated uncertaintiesnonlinear nonaffine systems
Publication Date：2025-02-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：10( 253-262 )