Hybrid Optimal Kinematic Parameter Identification for an Industrial Robot Based on BPNN-PSO
A novel hybrid algorithm that employs BP neural network (BPNN) and particle swarm optimization (PSO) algorithm is proposed for the kinematic parameter identification of industrial robots with an enhanced convergence response. The error model of the industrial robot is established based on a modified...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/4258676 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832553393624711168 |
|---|---|
| author | Guanbin Gao Fei Liu Hongjun San Xing Wu Wen Wang |
| author_facet | Guanbin Gao Fei Liu Hongjun San Xing Wu Wen Wang |
| author_sort | Guanbin Gao |
| collection | DOAJ |
| description | A novel hybrid algorithm that employs BP neural network (BPNN) and particle swarm optimization (PSO) algorithm is proposed for the kinematic parameter identification of industrial robots with an enhanced convergence response. The error model of the industrial robot is established based on a modified Denavit-Hartenberg method and Jacobian matrix. Then, the kinematic parameter identification of the industrial robot is transformed to a nonlinear optimization in which the unknown kinematic parameters are taken as optimal variables. A hybrid algorithm based on a BPNN and the PSO is applied to search for the optimal variables which are used to compensate for the error of the kinematic parameters and improve the positioning accuracy of the industrial robot. Simulations and experiments based on a realistic industrial robot are all provided to validate the efficacy of the proposed hybrid identification algorithm. The results show that the proposed parameter-identification method based on the BPNN and PSO has fewer iterations and faster convergence speed than the standard PSO algorithm. |
| format | Article |
| id | doaj-art-523bb0fdd0654e2fb0e7faff792a6dc6 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-523bb0fdd0654e2fb0e7faff792a6dc62025-02-03T05:54:05ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/42586764258676Hybrid Optimal Kinematic Parameter Identification for an Industrial Robot Based on BPNN-PSOGuanbin Gao0Fei Liu1Hongjun San2Xing Wu3Wen Wang4Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, ChinaFaculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, ChinaFaculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, ChinaFaculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, ChinaSchool of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou 310018, ChinaA novel hybrid algorithm that employs BP neural network (BPNN) and particle swarm optimization (PSO) algorithm is proposed for the kinematic parameter identification of industrial robots with an enhanced convergence response. The error model of the industrial robot is established based on a modified Denavit-Hartenberg method and Jacobian matrix. Then, the kinematic parameter identification of the industrial robot is transformed to a nonlinear optimization in which the unknown kinematic parameters are taken as optimal variables. A hybrid algorithm based on a BPNN and the PSO is applied to search for the optimal variables which are used to compensate for the error of the kinematic parameters and improve the positioning accuracy of the industrial robot. Simulations and experiments based on a realistic industrial robot are all provided to validate the efficacy of the proposed hybrid identification algorithm. The results show that the proposed parameter-identification method based on the BPNN and PSO has fewer iterations and faster convergence speed than the standard PSO algorithm.http://dx.doi.org/10.1155/2018/4258676 |
| spellingShingle | Guanbin Gao Fei Liu Hongjun San Xing Wu Wen Wang Hybrid Optimal Kinematic Parameter Identification for an Industrial Robot Based on BPNN-PSO Complexity |
| title | Hybrid Optimal Kinematic Parameter Identification for an Industrial Robot Based on BPNN-PSO |
| title_full | Hybrid Optimal Kinematic Parameter Identification for an Industrial Robot Based on BPNN-PSO |
| title_fullStr | Hybrid Optimal Kinematic Parameter Identification for an Industrial Robot Based on BPNN-PSO |
| title_full_unstemmed | Hybrid Optimal Kinematic Parameter Identification for an Industrial Robot Based on BPNN-PSO |
| title_short | Hybrid Optimal Kinematic Parameter Identification for an Industrial Robot Based on BPNN-PSO |
| title_sort | hybrid optimal kinematic parameter identification for an industrial robot based on bpnn pso |
| url | http://dx.doi.org/10.1155/2018/4258676 |
| work_keys_str_mv | AT guanbingao hybridoptimalkinematicparameteridentificationforanindustrialrobotbasedonbpnnpso AT feiliu hybridoptimalkinematicparameteridentificationforanindustrialrobotbasedonbpnnpso AT hongjunsan hybridoptimalkinematicparameteridentificationforanindustrialrobotbasedonbpnnpso AT xingwu hybridoptimalkinematicparameteridentificationforanindustrialrobotbasedonbpnnpso AT wenwang hybridoptimalkinematicparameteridentificationforanindustrialrobotbasedonbpnnpso |