Adaptive neural PD controllers for mobile manipulator trajectory tracking
Artificial intelligence techniques have been used in the industry to control complex systems; among these proposals, adaptive Proportional, Integrative, Derivative (PID) controllers are intelligent versions of the most used controller in the industry. This work presents an adaptive neuron PD control...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2021-02-01
|
| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-393.pdf |
| _version_ | 1828886578988056576 |
|---|---|
| author | Jesus Hernandez-Barragan Jorge D. Rios Javier Gomez-Avila Nancy Arana-Daniel Carlos Lopez-Franco Alma Y. Alanis |
| author_facet | Jesus Hernandez-Barragan Jorge D. Rios Javier Gomez-Avila Nancy Arana-Daniel Carlos Lopez-Franco Alma Y. Alanis |
| author_sort | Jesus Hernandez-Barragan |
| collection | DOAJ |
| description | Artificial intelligence techniques have been used in the industry to control complex systems; among these proposals, adaptive Proportional, Integrative, Derivative (PID) controllers are intelligent versions of the most used controller in the industry. This work presents an adaptive neuron PD controller and a multilayer neural PD controller for position tracking of a mobile manipulator. Both controllers are trained by an extended Kalman filter (EKF) algorithm. Neural networks trained with the EKF algorithm show faster learning speeds and convergence times than the training based on backpropagation. The integrative term in PID controllers eliminates the steady-state error, but it provokes oscillations and overshoot. Moreover, the cumulative error in the integral action may produce windup effects such as high settling time, poor performance, and instability. The proposed neural PD controllers adjust their gains dynamically, which eliminates the steady-state error. Then, the integrative term is not required, and oscillations and overshot are highly reduced. Removing the integral part also eliminates the need for anti-windup methodologies to deal with the windup effects. Mobile manipulators are popular due to their mobile capability combined with a dexterous manipulation capability, which gives them the potential for many industrial applications. Applicability of the proposed adaptive neural controllers is presented by simulating experimental results on a KUKA Youbot mobile manipulator, presenting different tests and comparisons with the conventional PID controller and an existing adaptive neuron PID controller. |
| first_indexed | 2024-12-13T11:47:08Z |
| format | Article |
| id | doaj.art-3ea6a3fcaee840e19e0fdb55a94d31df |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-12-13T11:47:08Z |
| publishDate | 2021-02-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-3ea6a3fcaee840e19e0fdb55a94d31df2022-12-21T23:47:28ZengPeerJ Inc.PeerJ Computer Science2376-59922021-02-017e39310.7717/peerj-cs.393Adaptive neural PD controllers for mobile manipulator trajectory trackingJesus Hernandez-BarraganJorge D. RiosJavier Gomez-AvilaNancy Arana-DanielCarlos Lopez-FrancoAlma Y. AlanisArtificial intelligence techniques have been used in the industry to control complex systems; among these proposals, adaptive Proportional, Integrative, Derivative (PID) controllers are intelligent versions of the most used controller in the industry. This work presents an adaptive neuron PD controller and a multilayer neural PD controller for position tracking of a mobile manipulator. Both controllers are trained by an extended Kalman filter (EKF) algorithm. Neural networks trained with the EKF algorithm show faster learning speeds and convergence times than the training based on backpropagation. The integrative term in PID controllers eliminates the steady-state error, but it provokes oscillations and overshoot. Moreover, the cumulative error in the integral action may produce windup effects such as high settling time, poor performance, and instability. The proposed neural PD controllers adjust their gains dynamically, which eliminates the steady-state error. Then, the integrative term is not required, and oscillations and overshot are highly reduced. Removing the integral part also eliminates the need for anti-windup methodologies to deal with the windup effects. Mobile manipulators are popular due to their mobile capability combined with a dexterous manipulation capability, which gives them the potential for many industrial applications. Applicability of the proposed adaptive neural controllers is presented by simulating experimental results on a KUKA Youbot mobile manipulator, presenting different tests and comparisons with the conventional PID controller and an existing adaptive neuron PID controller.https://peerj.com/articles/cs-393.pdfPIDAdaptive PIDNeural controlMobile manipulator |
| spellingShingle | Jesus Hernandez-Barragan Jorge D. Rios Javier Gomez-Avila Nancy Arana-Daniel Carlos Lopez-Franco Alma Y. Alanis Adaptive neural PD controllers for mobile manipulator trajectory tracking PeerJ Computer Science PID Adaptive PID Neural control Mobile manipulator |
| title | Adaptive neural PD controllers for mobile manipulator trajectory tracking |
| title_full | Adaptive neural PD controllers for mobile manipulator trajectory tracking |
| title_fullStr | Adaptive neural PD controllers for mobile manipulator trajectory tracking |
| title_full_unstemmed | Adaptive neural PD controllers for mobile manipulator trajectory tracking |
| title_short | Adaptive neural PD controllers for mobile manipulator trajectory tracking |
| title_sort | adaptive neural pd controllers for mobile manipulator trajectory tracking |
| topic | PID Adaptive PID Neural control Mobile manipulator |
| url | https://peerj.com/articles/cs-393.pdf |
| work_keys_str_mv | AT jesushernandezbarragan adaptiveneuralpdcontrollersformobilemanipulatortrajectorytracking AT jorgedrios adaptiveneuralpdcontrollersformobilemanipulatortrajectorytracking AT javiergomezavila adaptiveneuralpdcontrollersformobilemanipulatortrajectorytracking AT nancyaranadaniel adaptiveneuralpdcontrollersformobilemanipulatortrajectorytracking AT carloslopezfranco adaptiveneuralpdcontrollersformobilemanipulatortrajectorytracking AT almayalanis adaptiveneuralpdcontrollersformobilemanipulatortrajectorytracking |