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

Abstract

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 conduction problems. Also presented herein are quantitative performance test comparisons of the proposed algorithm with five inverse methods. Finally, the proposed algorithm simply upgrades the conventional input estimation approach, making it appropriate for implementation purposes.