ADAPTIVE ROBUST WEIGHTING INPUT ESTIMATION METHOD FOR THE 1-D INVERSE HEAT CONDUCTION PROBLEM

This work presents an adaptive weighting input estimation algorithm that efficiently and robustly on-line estimates time-varied thermal unknowns. While providing for the adaptivity, the Kalman filter allows us to derive a regression equation between the bias innovation and the thermal unknown. Based on this regression model, a recursive least-squares estimator weighting by an adaptive forgetting factor is proposed to extract the unknowns that are defined as the inputs. The maximum-likelihood-type estimator( M estimator) combining the Huber psi function is used to construct the adaptive weighting forgetting factor as a function of biased innovation at each time step, thereby allowing us to estimate the unknown in a system involving measurement noise, modeling error, and unpredictable time-varying changes of the unknowns. In addition, Ike superior capabilities of the proposed algorithm are demonstrated in several time-varying estimate cases and two benchmark performance tests in one-dimensional inverse heat...

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