Constant Force PID Control for Robotic Manipulator Based on Fuzzy Neural Network Algorithm
The increased demand for robotic manipulator has driven the development of industrial manufacturing. In particular, the trajectory tracking and contact constant force control of the robotic manipulator for the working environment under contact condition has become popular because of its high precisi...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/3491845 |
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| _version_ | 1832566604314968064 |
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| author | Zhu Dachang Du Baolin Zhu Puchen Chen Shouyan |
| author_facet | Zhu Dachang Du Baolin Zhu Puchen Chen Shouyan |
| author_sort | Zhu Dachang |
| collection | DOAJ |
| description | The increased demand for robotic manipulator has driven the development of industrial manufacturing. In particular, the trajectory tracking and contact constant force control of the robotic manipulator for the working environment under contact condition has become popular because of its high precision and quality operation. However, the two factors are opposite, that is to say, to maintain constant force control, it is necessary to make limited adjustment to the trajectory. It is difficult for the traditional PID controller because of the complexity parameters and nonlinear characteristics. In order to overcome this issue, a PID controller based on fuzzy neural network algorithm is developed in this paper for tracking the trajectory and contact constant force simultaneously. Firstly, the kinetic and potential energy is calculated, and the Lagrange function is constructed for a two-link robotic manipulator. Furthermore, a precise dynamic model is built for analyzing. Secondly, fuzzy neural network algorithm is proposed, and two kinds of turning parameters are derived for trajectory tracking and contact constant force control. Finally, numerical simulation results are reported to demonstrate the effectiveness of the proposed method. |
| format | Article |
| id | doaj-art-3da04afc448144a389373b8270331f11 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-3da04afc448144a389373b8270331f112025-02-03T01:03:40ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/34918453491845Constant Force PID Control for Robotic Manipulator Based on Fuzzy Neural Network AlgorithmZhu Dachang0Du Baolin1Zhu Puchen2Chen Shouyan3School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, ChinaSchool of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, ChinaSchool of Automation, Guangdong University of Technology, Guangzhou 510006, ChinaSchool of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, ChinaThe increased demand for robotic manipulator has driven the development of industrial manufacturing. In particular, the trajectory tracking and contact constant force control of the robotic manipulator for the working environment under contact condition has become popular because of its high precision and quality operation. However, the two factors are opposite, that is to say, to maintain constant force control, it is necessary to make limited adjustment to the trajectory. It is difficult for the traditional PID controller because of the complexity parameters and nonlinear characteristics. In order to overcome this issue, a PID controller based on fuzzy neural network algorithm is developed in this paper for tracking the trajectory and contact constant force simultaneously. Firstly, the kinetic and potential energy is calculated, and the Lagrange function is constructed for a two-link robotic manipulator. Furthermore, a precise dynamic model is built for analyzing. Secondly, fuzzy neural network algorithm is proposed, and two kinds of turning parameters are derived for trajectory tracking and contact constant force control. Finally, numerical simulation results are reported to demonstrate the effectiveness of the proposed method.http://dx.doi.org/10.1155/2020/3491845 |
| spellingShingle | Zhu Dachang Du Baolin Zhu Puchen Chen Shouyan Constant Force PID Control for Robotic Manipulator Based on Fuzzy Neural Network Algorithm Complexity |
| title | Constant Force PID Control for Robotic Manipulator Based on Fuzzy Neural Network Algorithm |
| title_full | Constant Force PID Control for Robotic Manipulator Based on Fuzzy Neural Network Algorithm |
| title_fullStr | Constant Force PID Control for Robotic Manipulator Based on Fuzzy Neural Network Algorithm |
| title_full_unstemmed | Constant Force PID Control for Robotic Manipulator Based on Fuzzy Neural Network Algorithm |
| title_short | Constant Force PID Control for Robotic Manipulator Based on Fuzzy Neural Network Algorithm |
| title_sort | constant force pid control for robotic manipulator based on fuzzy neural network algorithm |
| url | http://dx.doi.org/10.1155/2020/3491845 |
| work_keys_str_mv | AT zhudachang constantforcepidcontrolforroboticmanipulatorbasedonfuzzyneuralnetworkalgorithm AT dubaolin constantforcepidcontrolforroboticmanipulatorbasedonfuzzyneuralnetworkalgorithm AT zhupuchen constantforcepidcontrolforroboticmanipulatorbasedonfuzzyneuralnetworkalgorithm AT chenshouyan constantforcepidcontrolforroboticmanipulatorbasedonfuzzyneuralnetworkalgorithm |