Correlation integral optimization method and application in adaptive disturbance estimation
CHEN Jie
ZHAO Zhong
Abstract：In view of the traditional correlation integral algorithm, when the system disturbances are correlative with the decision variables, objective function can not converge to the optimal value in the process of iterative optimization. In this work, an improved method of correlation integral optimization is proposed. Based on the steady data driven model, an adaptive disturbance estimator is constructed to estimate the mean values of the disturbances and compensate the gradient values obtained by the traditional least square method. Based on the correlation integral optimization method, the modified optimization variables can be obtained to ensure the convergence of the optimization process. The simulation and industrial application results have verified the feasibility and effectiveness of the proposed method.
Keywords：steady-state optimizationcorrelation-integrationgradient estimationdisturbance estimationadaptive algorithm
Publication Date：2017-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：9( 956-964 )