Adaptive prescribed performance parameter estimation with input/output data
ZENG Yu-xuan
WANG Xian
HE Hao-ran
HUANG Ying-bo
NA Jing
Abstract：Although numerous adaptive parameter estimation methods have been developed,most of them rely on the system state information and cannot quantitatively analyze the parameter error convergence performance(e.g.,convergence rate,maximum overshoot,etc).In this sense,this paper will propose a novel adaptive prescribed performance parameter estimation method with nonlinear system input/output.To avoid using system state information,the K-filter operation is first introduced to obtain a mapping from the system input/output to the system unknown parameters.Then,a set of auxiliary variables are designed to extract the parameter estimation error information with simple algebraic calculation for parameter estimation.To improve the parameter estimation error convergence,the prescribed performance function(PPF)and associated error transformation mechanism are suggested to predefine the transient performance and steady-state performance of the parameter estimation error.Then,a novel adaptive parameter estimation algorithm is presented and the parameter estimation error convergence is rigorously proved by using the Lyapunov theory.Finally,numerical simulation and experimental results are also provided to show the superiority of the proposed method over some available results.
Keywords：parameter estimationprescribed performancenonlinear systemsystem input-output
Publication Date：2025-08-30
Online Publishing Date：2025-10-10(First online date of this platform, not the publication date of the document)
Pages：9( 1641-1649 )