A PWA model identification method based on optimal operating region partition with the output-error minimization for nonlinear systems

2020 ◽  
Vol 88 ◽  
pp. 1-9 ◽  
Author(s):  
Chunyue Song ◽  
Jiaorao Wang ◽  
Xinda Ma ◽  
Jun Zhao
Keyword(s):  
Nonlinear Systems ◽  
Model Identification ◽  
Error Minimization ◽  
Optimal Operating ◽  
Output Error ◽  
Identification Method ◽  
Region Partition ◽  
Operating Region

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.

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.

A New Integrated Algorithm of Structure Determination and Parameter Estimation for Nonlinear Systems Identification

1999 ◽  
Author(s):  
Wang Xiao Wang ◽  
Jianyin Xie
Keyword(s):  
Parameter Estimation ◽  
Nonlinear Systems ◽  
Structure Determination ◽  
Model Identification ◽  
Identification Algorithm ◽  
Householder Transformation ◽  
Systems Identification ◽  
Value Decomposition ◽  
Svd Method ◽  
Nonlinear Systems Identification

Abstract A new integrated algorithm of structure determination and parameter estimation is proposed for nonlinear systems identification in this paper, which is based on the Householder Transformation (HT), Givens and Modified Gram-Schmidt (MGS) algorithms. While being used for the polynomial and rational NARMAX model identification, it can select the model terms while deleting the unimportant ones from the assumed full model, avoiding the storage difficulty as the CGS identification algorithm does which is proposed by Billings et. al., and is numerically more stable. Combining the H algorithm with the modified bidiagonalization least squares (MBLS) algorithm and the singular value decomposition (SVD) method respectively, two algorithms referred to as the MBLSHT and SVDHT ones are proposed for the polynomial and rational NARMAX model identification. They are all numerically more stable than the HT or Givens or MGS algorithm given in this paper, and the MBLSHT algorithm has the best performance. A higher precision for the parameter estimation can thus be obtained by them, as supported b simulation results.

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