An efficient on-line thermal input estimation method using kalman filter and recursive least square algorithm

Tuan, Pan-Chio; Lee, Sou-Chen; Hou, Wen-Tien

doi:10.1080/174159797088027665

Cited by 37 publications

(17 citation statements)

References 14 publications

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“…Finally, the initial values that are assumed for both the state estimate error covariance matrix in the Kalman filter estimator, P, and the heat flux estimate error covariance matrix in the recursive least squares estimator, Pb, have been investigated. In all his work, Tuan et al [1][2][3] assigned very large values for them: 10E10 for P and 10E8 for Pb and he recommends that the filter will ignore the first few estimates. In this work it is found that there is no effect of these assumptions on the estimator performance at latter times.…”

Section: Resultsmentioning

confidence: 99%

“…An example similar to that presented by Tuan et al [3] has been used in the present study. The problem uses a linear one dimensional geometry and the sensor is located at the insulated boundary, figure 1.…”

Section: Simulation Casesmentioning

confidence: 96%

“…This algorithm has been used for solving many problems of one and two dimensional linear inverse heat conduction problems [1][2][3]. It employs two estimators.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Numerical study of the modeling error in the online input estimation algorithm used for inverse heat conduction problems (IHCPs)

Ali

Hamed

Lightstone

2008

J. Phys.: Conf. Ser.

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Abstract.A numerical investigation has been conducted to study the effect of modeling error in the state equation on the performance of the online input estimation algorithm in its application to the inverse heat conduction problems. This modeling error is used as a tuning parameter known as the stabilizing parameter in the online input estimation algorithm of the inverse heat conduction problems. Three different cases which cover most forms of the boundary heat flux functions have been considered. These cases are: square wave, triangular wave and mixed wave heat fluxes. The investigation has been carried for a one dimensional inverse heat conduction problem. Temperature measurements required for the inverse algorithm was generated by using a numerical solution of the direct heat conduction problem employing the three boundary heat flux functions. The most important finding of this investigation is that a robust range of the stabilizing parameter has been found which achieves the desired trade-off between the filter tracking ability and its sensitivity to measurement errors. For all three considered cases, it has been found that there is a common optimal value of the stabilizing parameter at which the estimate bias is minimal. This finding is very important for practical applications since this parameter is unknown practically and this study provides a needed guidance for assuming this parameter. The effect of changing other important parameters in the online input estimation algorithm on its performance has also been studied in this investigation.

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Section: Resultsmentioning

confidence: 99%

Section: Simulation Casesmentioning

confidence: 96%

See 1 more Smart Citation

Numerical study of the modeling error in the online input estimation algorithm used for inverse heat conduction problems (IHCPs)

Ali

Hamed

Lightstone

2008

J. Phys.: Conf. Ser.

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“…Thus, these methods are not real-time on-line algorithms. The RIE method proposed by Tuan et al (1997) is a sequential method, in which the algorithm is based on the KF and RLSE. The KF generates the recursive innovation sequence.…”

Section: Introductionmentioning

confidence: 99%

Load torque estimation problem for electric machinery servo systems by an inverse method

Chen¹,

Lee

2008

Journal of the Chinese Institute of Engineers

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This study analyzes a load torque estimation problem for an electric machinery servo system using the recursive input estimation (RIE) algorithm. This study also presents four novel estimation models, which have different numbers of sensors, considers the process and the measurement noise to estimate load torque without a torque sensor. The proposed algorithm is a novel estimation technique for solving force feedback and state estimation problems for an electric machinery servo control system. In this work, the DC servomotor system is utilized as an electric machinery servo system, and four important RIE characteristics were verified by numerical simulation results: (i) Adaptive forgetting factor in the RIE algorithm can estimate load torque more effectively than constant forgetting factor; (ii) The significant variation of control input and/or load torque impact affects the estimation precision; (iii) The highperformance load torque estimation model can be established based only on the given control input and techogenerator sensor; (iv) Using different degrees of model errors, the estimation of the performance tendency toward good or bad for control input and load torque are the same. This characteristic shows that good estimated performance for an unknown load torque can be confirmed based on the strength of good control input estimated performance. In other words, model error can be identified via the deterministic control input and its estimation results. The proposed models may have practical applications for solving disturbance compensation problems in electric machinery servo systems.

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“…Tuan 5,6 adopted the input estimation method to inversely solve the 1-D and 2-D heat conduction problems. Ma [7][8][9] and Deng 10 as well used this method to estimate the force input to the structure system.…”

Section: Introductionmentioning

confidence: 99%

Blast Load Input Estimation of the Medium Girder Bridge using Inverse Method

Lee

Chen

2008

DSJ

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Innovative adaptive weighted input estimation inverse methodology for estimating the unknown time-varying blast loads on the truss structure system is presented. This method is based on the Kalman filter and the recursive least square estimator (RLSE). The filter models the system dynamics in a linear set of state equations. The state equations of the truss structure are constructed using the finite element method. The input blast loads of the truss structure system are inverse estimated from the system responses measured at two distinct nodes. This work presents an efficient weighting factor γ applied in the RLSE, which is capable of providing a reasonable estimation results. The results obtained from the simulations show that the method is effective in estimating input blast loads, so has great stability and precision.

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An efficient on-line thermal input estimation method using kalman filter and recursive least square algorithm

Cited by 37 publications

References 14 publications

Numerical study of the modeling error in the online input estimation algorithm used for inverse heat conduction problems (IHCPs)

Numerical study of the modeling error in the online input estimation algorithm used for inverse heat conduction problems (IHCPs)

Load torque estimation problem for electric machinery servo systems by an inverse method

Blast Load Input Estimation of the Medium Girder Bridge using Inverse Method

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