Inverse Kinematic Control of a Delta Robot Using Neural Networks in Real-Time
This paper presents an inverse kinematic controller using neural networks for trajectory controlling of a delta robot in real-time. The developed control scheme is purely data-driven and does not require prior knowledge of the delta robot kinematics. Moreover, it can adapt to the changes in the kine...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-10-01
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| Series: | Robotics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2218-6581/10/4/115 |
| _version_ | 1827669791963348992 |
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| author | Akram Gholami Taymaz Homayouni Reza Ehsani Jian-Qiao Sun |
| author_facet | Akram Gholami Taymaz Homayouni Reza Ehsani Jian-Qiao Sun |
| author_sort | Akram Gholami |
| collection | DOAJ |
| description | This paper presents an inverse kinematic controller using neural networks for trajectory controlling of a delta robot in real-time. The developed control scheme is purely data-driven and does not require prior knowledge of the delta robot kinematics. Moreover, it can adapt to the changes in the kinematics of the robot. For developing the controller, the kinematic model of the delta robot is estimated by using neural networks. Then, the trained neural networks are configured as a controller in the system. The parameters of the neural networks are updated while the robot follows a path to adaptively compensate for modeling uncertainties and external disturbances of the control system. One of the main contributions of this paper is to show that updating the parameters of neural networks offers a smaller tracking error in inverse kinematic control of a delta robot with consideration of joint backlash. Different simulations and experiments are conducted to verify the proposed controller. The results show that in the presence of external disturbance, the error in trajectory tracking is bounded, and the negative effect of joint backlash in trajectory tracking is reduced. The developed method provides a new approach to the inverse kinematic control of a delta robot. |
| first_indexed | 2024-03-10T03:11:03Z |
| format | Article |
| id | doaj.art-dc4bccaefc3b4db1872745c1b2182015 |
| institution | Directory Open Access Journal |
| issn | 2218-6581 |
| language | English |
| last_indexed | 2024-03-10T03:11:03Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Robotics |
| spelling | doaj.art-dc4bccaefc3b4db1872745c1b21820152023-11-23T10:26:44ZengMDPI AGRobotics2218-65812021-10-0110411510.3390/robotics10040115Inverse Kinematic Control of a Delta Robot Using Neural Networks in Real-TimeAkram Gholami0Taymaz Homayouni1Reza Ehsani2Jian-Qiao Sun3Department of Mechanical Engineering, University of California, Merced, CA 95343, USADepartment of Mechanical Engineering, University of California, Merced, CA 95343, USADepartment of Mechanical Engineering, University of California, Merced, CA 95343, USADepartment of Mechanical Engineering, University of California, Merced, CA 95343, USAThis paper presents an inverse kinematic controller using neural networks for trajectory controlling of a delta robot in real-time. The developed control scheme is purely data-driven and does not require prior knowledge of the delta robot kinematics. Moreover, it can adapt to the changes in the kinematics of the robot. For developing the controller, the kinematic model of the delta robot is estimated by using neural networks. Then, the trained neural networks are configured as a controller in the system. The parameters of the neural networks are updated while the robot follows a path to adaptively compensate for modeling uncertainties and external disturbances of the control system. One of the main contributions of this paper is to show that updating the parameters of neural networks offers a smaller tracking error in inverse kinematic control of a delta robot with consideration of joint backlash. Different simulations and experiments are conducted to verify the proposed controller. The results show that in the presence of external disturbance, the error in trajectory tracking is bounded, and the negative effect of joint backlash in trajectory tracking is reduced. The developed method provides a new approach to the inverse kinematic control of a delta robot.https://www.mdpi.com/2218-6581/10/4/115inverse kinematic controltrajectory trackingneural networksdelta robot |
| spellingShingle | Akram Gholami Taymaz Homayouni Reza Ehsani Jian-Qiao Sun Inverse Kinematic Control of a Delta Robot Using Neural Networks in Real-Time Robotics inverse kinematic control trajectory tracking neural networks delta robot |
| title | Inverse Kinematic Control of a Delta Robot Using Neural Networks in Real-Time |
| title_full | Inverse Kinematic Control of a Delta Robot Using Neural Networks in Real-Time |
| title_fullStr | Inverse Kinematic Control of a Delta Robot Using Neural Networks in Real-Time |
| title_full_unstemmed | Inverse Kinematic Control of a Delta Robot Using Neural Networks in Real-Time |
| title_short | Inverse Kinematic Control of a Delta Robot Using Neural Networks in Real-Time |
| title_sort | inverse kinematic control of a delta robot using neural networks in real time |
| topic | inverse kinematic control trajectory tracking neural networks delta robot |
| url | https://www.mdpi.com/2218-6581/10/4/115 |
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