Neural Network Supervision Control Strategy for Inverted Pendulum Tracking Control
This paper presents several control methods and realizes the stable tracking for the inverted pendulum system. Based on the advantages of RBF and traditional PID, a novel PID controller based on the RBF neural network supervision control method (PID-RBF) is proposed. This method realizes the adaptiv...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2021/5536573 |
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| _version_ | 1832551010891988992 |
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| author | Hongliang Gao Xiaoling Li Chao Gao Jie Wu |
| author_facet | Hongliang Gao Xiaoling Li Chao Gao Jie Wu |
| author_sort | Hongliang Gao |
| collection | DOAJ |
| description | This paper presents several control methods and realizes the stable tracking for the inverted pendulum system. Based on the advantages of RBF and traditional PID, a novel PID controller based on the RBF neural network supervision control method (PID-RBF) is proposed. This method realizes the adaptive adjustment of the stable tracking signal of the system. Furthermore, an improved PID controller based on RBF neural network supervision control strategy (IPID-RBF) is presented. This control strategy adopts the supervision control method of feed-forward and feedback. The response speed of the system is further improved, and the overshoot of the tracking signal is further reduced. The tracking control simulation of the inverted pendulum system under three different signals is given to illustrate the effectiveness of the proposed method. |
| format | Article |
| id | doaj-art-daa522386da74c10a884cc5e8c26d4e9 |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-daa522386da74c10a884cc5e8c26d4e92025-02-03T06:05:16ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2021-01-01202110.1155/2021/55365735536573Neural Network Supervision Control Strategy for Inverted Pendulum Tracking ControlHongliang Gao0Xiaoling Li1Chao Gao2Jie Wu3School of Electrical Engineering and Automation, Hubei Normal University, Huangshi 435002, ChinaSchool of Electrical Engineering and Automation, Hubei Normal University, Huangshi 435002, ChinaThe China Ship Development and Design Center, Wuhan 430064, ChinaSchool of Electrical Engineering and Automation, Hubei Normal University, Huangshi 435002, ChinaThis paper presents several control methods and realizes the stable tracking for the inverted pendulum system. Based on the advantages of RBF and traditional PID, a novel PID controller based on the RBF neural network supervision control method (PID-RBF) is proposed. This method realizes the adaptive adjustment of the stable tracking signal of the system. Furthermore, an improved PID controller based on RBF neural network supervision control strategy (IPID-RBF) is presented. This control strategy adopts the supervision control method of feed-forward and feedback. The response speed of the system is further improved, and the overshoot of the tracking signal is further reduced. The tracking control simulation of the inverted pendulum system under three different signals is given to illustrate the effectiveness of the proposed method.http://dx.doi.org/10.1155/2021/5536573 |
| spellingShingle | Hongliang Gao Xiaoling Li Chao Gao Jie Wu Neural Network Supervision Control Strategy for Inverted Pendulum Tracking Control Discrete Dynamics in Nature and Society |
| title | Neural Network Supervision Control Strategy for Inverted Pendulum Tracking Control |
| title_full | Neural Network Supervision Control Strategy for Inverted Pendulum Tracking Control |
| title_fullStr | Neural Network Supervision Control Strategy for Inverted Pendulum Tracking Control |
| title_full_unstemmed | Neural Network Supervision Control Strategy for Inverted Pendulum Tracking Control |
| title_short | Neural Network Supervision Control Strategy for Inverted Pendulum Tracking Control |
| title_sort | neural network supervision control strategy for inverted pendulum tracking control |
| url | http://dx.doi.org/10.1155/2021/5536573 |
| work_keys_str_mv | AT honglianggao neuralnetworksupervisioncontrolstrategyforinvertedpendulumtrackingcontrol AT xiaolingli neuralnetworksupervisioncontrolstrategyforinvertedpendulumtrackingcontrol AT chaogao neuralnetworksupervisioncontrolstrategyforinvertedpendulumtrackingcontrol AT jiewu neuralnetworksupervisioncontrolstrategyforinvertedpendulumtrackingcontrol |