scholarly journals On error analysis of a closed-loop subspace model identification method

2021 ◽  
Vol 54 (9) ◽  
pp. 701-706
Author(s):  
Hiroshi Oku ◽  
Kenji Ikeda
Keyword(s):  
Error Analysis ◽  
Closed Loop ◽  
Model Identification ◽  
Identification Method

scholarly journals Visual Closed-Loop Dynamic Model Identification of Parallel Robots Based on Optical CMM Sensor

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 836 ◽  
Author(s):  
Pengcheng Li ◽  
Ahmad Ghasemi ◽  
Wenfang Xie ◽  
Wei Tian
Keyword(s):  
Dynamic Model ◽  
Visual Servoing ◽  
Closed Loop ◽  
Model Identification ◽  
Parallel Robot ◽  
Parallel Robots ◽  
Model Parameters ◽  
Error Identification ◽  
Output Error ◽  
Identification Method

Parallel robots present outstanding advantages compared with their serial counterparts; they have both a higher force-to-weight ratio and better stiffness. However, the existence of closed-chain mechanism yields difficulties in designing control system for practical applications, due to its highly coupled dynamics. This paper focuses on the dynamic model identification of the 6-DOF parallel robots for advanced model-based visual servoing control design purposes. A visual closed-loop output-error identification method based on an optical coordinate-measuring-machine (CMM) sensor for parallel robots is proposed. The main advantage, compared with the conventional identification method, is that the joint torque measurement and the exact knowledge of the built-in robot controllers are not needed. The time-consuming forward kinematics calculation, which is employed in the conventional identification method of the parallel robot, can be avoided due to the adoption of optical CMM sensor for real time pose estimation. A case study on a 6-DOF RSS parallel robot is carried out in this paper. The dynamic model of the parallel robot is derived based on the virtual work principle, and the built dynamic model is verified through Matlab/SimMechanics. By using an outer loop visual servoing controller to stabilize both the parallel robot and the simulated model, a visual closed-loop output-error identification method is proposed and the model parameters are identified by using a nonlinear optimization technique. The effectiveness of the proposed identification algorithm is validated by experimental tests.

Closed Loop Identification by Optimization Method

2015 ◽  
Vol 1084 ◽  
pp. 636-641
Author(s):  
Valeriy F. Dyadik ◽  
Nikolay S. Krinitsyn ◽  
Vyacheslav A. Rudnev
Keyword(s):  
Closed Loop ◽  
Uranium Hexafluoride ◽  
Optimization Method ◽  
Model Parameters ◽  
Control Loop ◽  
Loop Control ◽  
Identification Method ◽  
Closed Loop Control System ◽  
Closed Loop Identification ◽  
Loop Control System

The article is devoted to the adaptation of the controller parameters during its operation as a part of a control loop. The possibility to identify the parameters of the controlled plant model in the closed control loop has been proved by a computer simulation. The described active identification method is based on the response processing of the closed loop control system to standard actions. The developed algorithm has been applied to determine the model parameters of the flaming fluorination reactor used for the production of uranium hexafluoride. Designed identification method improves the quality of the product and the efficiency of the entire production.

scholarly journals T-S Fuzzy Systems Optimization Identification Based on FCM and PSO

2020 ◽  
Author(s):  
Yaxue Ren ◽  
Fucai Liu ◽  
Jingfeng Lv ◽  
Aiwen Meng ◽  
Yintang Wen
Keyword(s):  
Membership Function ◽  
Gaussian Function ◽  
Model Identification ◽  
Fuzzy Model ◽  
Pso Algorithm ◽  
Identification Accuracy ◽  
Fine Tuning ◽  
Recursive Least Squares ◽  
Identification Method ◽  
Two Parameters

Abstract The division of fuzzy space is very important in the identification of premise parameters and the Gaussian membership function is applied to the premise fuzzy set. However, the two parameters of Gaussian membership function, center and width, are not easy to be determined. In this paper, a novel T-S fuzzy model optimal identification method of optimizing two parameters of Gaussian function based on Fuzzy c-means (FCM) and particle swarm optimization (PSO) algorithm is presented. Firstly, we use FCM algorithm to determine the Gaussian center for rough adjustment. Then, under the condition that the center of Gaussian function is fixed, the PSO algorithm is used to optimize another adjustable parameter, the width of the Gaussian membership function, to achieve fine tuning, so as to complete the identification of prerequisite parameters of fuzzy model. In addition, the recursive least squares (RLS) algorithm is used to identify the conclusion parameters. Finally, the effectiveness of this method for T-S fuzzy model identification is verified by simulation examples, and the higher identification accuracy can be obtained by using the novel identification method described compared with other identification methods.

Identification of Complex System Based on Neural Network

2012 ◽  
Vol 433-440 ◽  
pp. 4342-4347
Author(s):  
Zhen Hai Dou ◽  
Ya Jing Wang
Keyword(s):  
Neural Network ◽  
Complex System ◽  
Mean Squared Error ◽  
Model Identification ◽  
Complex Model ◽  
System Model ◽  
Mathematics Model ◽  
Identification Method ◽  
Squared Error ◽  
Actual Output

In order to conquer the difficulty of building up the mathematics model of some complex system, model identification method based on neural network is put forward. By this method, according to actual sample datum, the complex model of crude oil heating furnace is identified at appropriate quantity of net layers and notes. The identification results show that output of model can basically consistent with the actual output and their mean squared error (MSE) almost is 0. Therefore, model identification method based on neural network is an effective method in complex system identification.

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