Correcting encoder interpolation error on the Green Bank Telescope using an iterative model based identification algorithm

2015 ◽  
Vol 1 (4) ◽  
pp. 044005
Author(s):  
Timothy Franke ◽  
Tim Weadon ◽  
John Ford ◽  
Mario Garcia-Sanz
Keyword(s):  
Interpolation Error ◽  
Identification Algorithm ◽  
Model Based ◽  
Iterative Model ◽  
Green Bank Telescope

An iterative model-based cogging compensator for the Green Bank Telescope servo system

2014 ◽  
Author(s):  
Timothy Franke ◽  
Timothy Weadon ◽  
John Ford ◽  
Mario Garcia-Sanz
Keyword(s):  
Servo System ◽  
Model Based ◽  
Iterative Model ◽  
Green Bank Telescope

Iterative model‐based experimental design for spherical agglomeration processes

AIChE Journal ◽  
2021 ◽  
Author(s):  
Kanjakha Pal ◽  
Botond Szilagyi ◽  
Christopher L. Burcham ◽  
Daniel J. Jarmer ◽  
Zoltan K. Nagy
Keyword(s):  
Experimental Design ◽  
Model Based ◽  
Iterative Model

scholarly journals ONLINE PARAMETER IDENTIFICATION OF RICE TRANSPLANTER MODEL BASED ON IPSO-EKF ALGORITHM

2020 ◽  
Vol 61 (2) ◽  
pp. 25-34 ◽  
Author(s):  
Yibo Li ◽  
Hang Li ◽  
Xiaonan Guo
Keyword(s):  
Parameter Identification ◽  
Measured Data ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Output Data ◽  
Swarm Optimization ◽  
Model Based ◽  
Simulation Results ◽  
Filtering Effect ◽  
Ipso Algorithm

In order to improve the accuracy of rice transplanter model parameters, an online parameter identification algorithm for the rice transplanter model based on improved particle swarm optimization (IPSO) algorithm and extended Kalman filter (EKF) algorithm was proposed. The dynamic model of the rice transplanter was established to determine the model parameters of the rice transplanter. Aiming at the problem that the noise matrices in EKF algorithm were difficult to select and affected the best filtering effect, the proposed algorithm used the IPSO algorithm to optimize the noise matrices of the EKF algorithm in offline state. According to the actual vehicle tests, the IPSO-EKF was used to identify the cornering stiffness of the front and rear tires online, and the identified cornering stiffness value was substituted into the model to calculate the output data and was compared with the measured data. The simulation results showed that the accuracy of parameter identification for the rice transplanter model based on the IPSO-EKF algorithm was improved, and established an accurate rice transplanter model.

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