| Summary: | 碩士 === 中正理工學院 === 兵器系統工程研究所 === 88 === An innovative input estimation inverse methodology for estimating the time varying unknowns such as thermal sources or heat flux acting on the boundary of a nonlinear thermal system is presented. The algorithm includes the Extended Kalman Filter (EKF) which recursively estimates the interior temperature of the body in a system involving noisy measurement and modeling error. For the EKF estimation procedure, an important regression equation between the observable bias residual innovation and the thermal unknown is provided. Based on this regression model, a recursive least square estimator weighted by the adaptive forgetting factor is proposed to estimate those unknowns which are defined as the input. In real thermal process, owing to the thermal conductivity is function of the temperature, the problem usually are nonlinear, thus the estimation procedure become more complicated and difficulty. In this thesis, the signal-to-noise ratio is used to analyze the interactive relationship among the forgetting factor and the measurement and modeling error variance. By this analysis, this work also presents an efficient adaptive forgetting factor under robust signal-to-noise ratio zone, capable of providing a reasonable estimation results. Finally, The superior capabilities of the proposed algorithm are demonstrated through several simulated examples with different types of the time-varying heat sources as the unknown inputs.
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