Abstract
In view of the traditional correlation integral optimization method, when the system disturbances are correlative with the decision variables, objective function does 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 decision variables can be obtained to ensure the convergence to the optimal value in the process of iterative optimization. The simulation and industrial application results have verified the feasibility and effectiveness of the proposed method.
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