Solution for Forward Kinematics of Parallel Mechanism based on PSO-BPNN and Newton-Raphson Algorithm
Taking parallel mechanism as a research object， aiming at the problem that neural network algorithm is easy to fall into local optimization and the Newton-Raphson algorithm is sensitive to the initial value of iteration when solving forward kinematics， a general forward kinematics algorithm combinin...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Transmission
2021-07-01
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| Series: | Jixie chuandong |
| Subjects: | |
| Online Access: | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2021.07.014 |
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| _version_ | 1841547298120663040 |
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| author | Qiguo Hu Yanli Luo Lijie Cao Jun Zhang |
| author_facet | Qiguo Hu Yanli Luo Lijie Cao Jun Zhang |
| author_sort | Qiguo Hu |
| collection | DOAJ |
| description | Taking parallel mechanism as a research object， aiming at the problem that neural network algorithm is easy to fall into local optimization and the Newton-Raphson algorithm is sensitive to the initial value of iteration when solving forward kinematics， a general forward kinematics algorithm combining PSO-BPNN and Newton-Raphson algorithm is proposed. The inverse kinematics equation of parallel mechanism is established to obtain the value of the driving rod， which is used as the training sample， and the BPNN model is optimized by PSO to obtain the solution for forward kinematic， which is taken as the initial iterative value of the newton-raphson algorithm to solve the forward kinematics of parallel mechanism. To verify the effectiveness and universality of the algorithm， simulation examples of 3-PCR and 3-PPR parallel mechanisms are given. The simulation results show that Newton-Raphson algorithm does not converge due to the large difference between the initial iteration value and the target value. Compared with the PSO-BPNN algorithm， the absolute error obtained by combining PSO-BPNN and Newton-Raphson algorithm is reduced by at least 99.68% and 99.96%， and the number of iterations is less. PSO-BPNN and Newton-Raphson algorithm not only overcomes the shortcomings of poor local convergence of the neural network algorithm， but also avoids the influence of initial value selection on the accuracy of the Newton-Raphson algorithm， which has good versatility. |
| format | Article |
| id | doaj-art-d5174c145fe049599e6c4ef85615cf25 |
| institution | Kabale University |
| issn | 1004-2539 |
| language | zho |
| publishDate | 2021-07-01 |
| publisher | Editorial Office of Journal of Mechanical Transmission |
| record_format | Article |
| series | Jixie chuandong |
| spelling | doaj-art-d5174c145fe049599e6c4ef85615cf252025-01-10T14:48:37ZzhoEditorial Office of Journal of Mechanical TransmissionJixie chuandong1004-25392021-07-01459610218848187Solution for Forward Kinematics of Parallel Mechanism based on PSO-BPNN and Newton-Raphson AlgorithmQiguo HuYanli LuoLijie CaoJun ZhangTaking parallel mechanism as a research object， aiming at the problem that neural network algorithm is easy to fall into local optimization and the Newton-Raphson algorithm is sensitive to the initial value of iteration when solving forward kinematics， a general forward kinematics algorithm combining PSO-BPNN and Newton-Raphson algorithm is proposed. The inverse kinematics equation of parallel mechanism is established to obtain the value of the driving rod， which is used as the training sample， and the BPNN model is optimized by PSO to obtain the solution for forward kinematic， which is taken as the initial iterative value of the newton-raphson algorithm to solve the forward kinematics of parallel mechanism. To verify the effectiveness and universality of the algorithm， simulation examples of 3-PCR and 3-PPR parallel mechanisms are given. The simulation results show that Newton-Raphson algorithm does not converge due to the large difference between the initial iteration value and the target value. Compared with the PSO-BPNN algorithm， the absolute error obtained by combining PSO-BPNN and Newton-Raphson algorithm is reduced by at least 99.68% and 99.96%， and the number of iterations is less. PSO-BPNN and Newton-Raphson algorithm not only overcomes the shortcomings of poor local convergence of the neural network algorithm， but also avoids the influence of initial value selection on the accuracy of the Newton-Raphson algorithm， which has good versatility.http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2021.07.014Parallel mechanismForward kinematicsPSO back propagation neural networkNewton-Raphson iteration algorithm |
| spellingShingle | Qiguo Hu Yanli Luo Lijie Cao Jun Zhang Solution for Forward Kinematics of Parallel Mechanism based on PSO-BPNN and Newton-Raphson Algorithm Jixie chuandong Parallel mechanism Forward kinematics PSO back propagation neural network Newton-Raphson iteration algorithm |
| title | Solution for Forward Kinematics of Parallel Mechanism based on PSO-BPNN and Newton-Raphson Algorithm |
| title_full | Solution for Forward Kinematics of Parallel Mechanism based on PSO-BPNN and Newton-Raphson Algorithm |
| title_fullStr | Solution for Forward Kinematics of Parallel Mechanism based on PSO-BPNN and Newton-Raphson Algorithm |
| title_full_unstemmed | Solution for Forward Kinematics of Parallel Mechanism based on PSO-BPNN and Newton-Raphson Algorithm |
| title_short | Solution for Forward Kinematics of Parallel Mechanism based on PSO-BPNN and Newton-Raphson Algorithm |
| title_sort | solution for forward kinematics of parallel mechanism based on pso bpnn and newton raphson algorithm |
| topic | Parallel mechanism Forward kinematics PSO back propagation neural network Newton-Raphson iteration algorithm |
| url | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2021.07.014 |
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