Solution of inverse radiation-conduction problems using a Kalman filter coupled with the recursive least–square estimator

2017 ◽  
Vol 111 ◽  
pp. 582-592 ◽  
Author(s):  
Shuang Wen ◽  
Hong Qi ◽  
Ya-Tao Ren ◽  
Jian-Ping Sun ◽  
Li-Ming Ruan
Keyword(s):  
Kalman Filter ◽  
Least Square ◽  
Recursive Least Square ◽  
Least Square Estimator

Adaptive Modeling of Laser Powder Deposition Process for Control and Monitoring Application

2005 ◽  
Author(s):  
Mohammad Durali ◽  
Alireza Fathi ◽  
Amir Khajepour ◽  
Ehsan Toyserkani
Keyword(s):  
Kalman Filter ◽  
System Identification ◽  
Least Square Method ◽  
Deposition Process ◽  
Least Square ◽  
Filter Method ◽  
Recursive Least Square ◽  
Noise Elimination ◽  
Laser Powder Deposition ◽  
Powder Deposition

Laser Powder Deposition technique is an advanced production method with many applications. Despite this fact, reliable and accurate control schemes have not yet fully developed for this method. This article presents method for in time identification of the process for modeling and adaptation of proper control strategy. ARMAX structure is chosen for system model. Recursive least square method and Kalman Filter methods are adopted for system identification, and their performance are compared. Experimental data was used for system identification, and proper filtering schemes are devised here for noise elimination and increased estimation results. It was concluded that although both methods yield efficient performance and accurate results, Kalman Filter method gives better results in parameter estimations. The comparison of the results shows that this method can be used very efficiently in control and monitoring of Laser Powder Deposition process.

Inverse Active Load Inputs Estimation of the 3D Spatial Truss Structure System

2011 ◽  
Vol 27 (4) ◽  
pp. 469-477
Author(s):  
M.-H. Lee
Keyword(s):  
Finite Element ◽  
Kalman Filter ◽  
Least Square Method ◽  
Least Square ◽  
Truss Structure ◽  
Recursive Least Square ◽  
Finite Element Scheme ◽  
Element Scheme ◽  
Noise Covariance ◽  
Recursive Least Square Method

ABSTRACTThis study presents an innovative fuzzy inverse method with the finite-element scheme for estimating the unknown time-varying load inputs on a three-dimensional (3D) spatial truss structural system. The finite-element scheme is employed to discretize the problem in space, allowing multidimensional problems of various geometries to be treated. This method is based on the fuzzy Kalman Filter (FKF) technology and the fuzzy weighting recursive least square method (FWRLSM). The fuzzy Kalman filter measures the system responses at two distinct nodes in the 3D spatial truss structure. The fuzzy weighting recursive least square method is derived using the residual innovation sequence to compute the input loads. The proposed method's superiority is demonstrated using several typical simulation cases that vary with different estimator and the distinct levels of the initial process noise covariance and the measurement noise covariance. The results show that this method has great stability and accuracy.

INTELLIGENT FUZZY WEIGHTED INPUT ESTIMATION METHOD FOR THE DYNAMIC FORCE INPUTS OF A CANTILEVER BEAM MOUNTED WITH CONCENTRATED MASSES

2012 ◽  
Vol 04 (01) ◽  
pp. 1250005 ◽  
Author(s):  
MING-HUI LEE
Keyword(s):  
Kalman Filter ◽  
Cantilever Beam ◽  
Dynamic Performance ◽  
Estimation Method ◽  
Weighting Factor ◽  
Least Square ◽  
Dynamic Force ◽  
Recursive Least Square ◽  
Inference System ◽  
Input Estimation

This study proposes an intelligent fuzzy weighted input estimation method for the force inputs of a cantilever beam structural system. The finite element scheme is employed to discretize the problem in space, allowing multi-dimensional problems of various geometries to be treated. The Kalman filter (KF) and the recursive least square estimator (RLSE) are two main portions of this method. In this method, the efficient estimator is weighted by the fuzzy weighting factor proposed based on the fuzzy logic inference system. By directly synthesizing the Kalman filter with the estimator, this work presents an efficient robust forgetting zone, which is capable of providing a reasonable tradeoff between the tracking capability and the flexibility against noises. The input forces of structural sytem can be estimated by this method to promote the analysis reliability of the dynamic performance. The simulation results are compared by alternating between the constant and adaptive weighting factors. The results demonstrate that the application of the presented method is successful in coping with the dynamic system of cantilever beam.

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