Integrated sliding mode control of robot manipulator based on fuzzy adaptive RBF
To solve the uncertainty of the parameters of the manipulator dynamics model, the control accuracy and convergence rate of the system affected by the joint friction and external interference, a compound control strategy based on the manipulator dynamics model is proposed. Firstly, a modified power-o...
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EDP Sciences
2024-12-01
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| Series: | Xibei Gongye Daxue Xuebao |
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| Online Access: | https://www.jnwpu.org/articles/jnwpu/full_html/2024/06/jnwpu2024426p1099/jnwpu2024426p1099.html |
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| _version_ | 1825206547957940224 |
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| author | FENG Jiaqing ZHANG Lei TIAN Dongyu |
| author_facet | FENG Jiaqing ZHANG Lei TIAN Dongyu |
| author_sort | FENG Jiaqing |
| collection | DOAJ |
| description | To solve the uncertainty of the parameters of the manipulator dynamics model, the control accuracy and convergence rate of the system affected by the joint friction and external interference, a compound control strategy based on the manipulator dynamics model is proposed. Firstly, a modified power-of-two convergence law is used and combined with an integral sliding mode to design a sliding mode control term to shorten the convergence of the tracking error. Secondly, the approximations of the uncertain variables of the dynamical model are accomplished by using the three sets of RBF neural networks and introducing an adaptive mechanism for online self-tuning of weights, the approximation errors of the RBF neural networks are compensated by using the sliding-mode control term designed in the previous section. Finally, the fuzzy controllers are utilized to calculate the coupled joint friction and outside disturbances. The simulation works show that comparing with the chunked RBF neural network to approximate the sliding mode control logy, the proposed hybrid control theory reduces the mechanical arm joint angular rate response time by 39.4%, the largest solid-state error was cut by 76.8%, and the medium-sized solid-state error was cut by 62.7%, improved control preciseness and the responsiveness of the spatial trajectory tracking of the manipulator arm's joints. |
| format | Article |
| id | doaj-art-2138e716dc9747939ebd764f96a7877c |
| institution | Kabale University |
| issn | 1000-2758 2609-7125 |
| language | zho |
| publishDate | 2024-12-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | Xibei Gongye Daxue Xuebao |
| spelling | doaj-art-2138e716dc9747939ebd764f96a7877c2025-02-07T08:23:13ZzhoEDP SciencesXibei Gongye Daxue Xuebao1000-27582609-71252024-12-014261099111010.1051/jnwpu/20244261099jnwpu2024426p1099Integrated sliding mode control of robot manipulator based on fuzzy adaptive RBFFENG Jiaqing0ZHANG Lei1TIAN Dongyu2School of Electronic Information, Xi'an Polytechnic UniversitySchool of Electronic Information, Xi'an Polytechnic UniversitySchool of Electronic Information, Xi'an Polytechnic UniversityTo solve the uncertainty of the parameters of the manipulator dynamics model, the control accuracy and convergence rate of the system affected by the joint friction and external interference, a compound control strategy based on the manipulator dynamics model is proposed. Firstly, a modified power-of-two convergence law is used and combined with an integral sliding mode to design a sliding mode control term to shorten the convergence of the tracking error. Secondly, the approximations of the uncertain variables of the dynamical model are accomplished by using the three sets of RBF neural networks and introducing an adaptive mechanism for online self-tuning of weights, the approximation errors of the RBF neural networks are compensated by using the sliding-mode control term designed in the previous section. Finally, the fuzzy controllers are utilized to calculate the coupled joint friction and outside disturbances. The simulation works show that comparing with the chunked RBF neural network to approximate the sliding mode control logy, the proposed hybrid control theory reduces the mechanical arm joint angular rate response time by 39.4%, the largest solid-state error was cut by 76.8%, and the medium-sized solid-state error was cut by 62.7%, improved control preciseness and the responsiveness of the spatial trajectory tracking of the manipulator arm's joints.https://www.jnwpu.org/articles/jnwpu/full_html/2024/06/jnwpu2024426p1099/jnwpu2024426p1099.htmlmechanical armorbit trackingself-adaptive rbf neural networkfuzzy offsetintegral sliding mode |
| spellingShingle | FENG Jiaqing ZHANG Lei TIAN Dongyu Integrated sliding mode control of robot manipulator based on fuzzy adaptive RBF Xibei Gongye Daxue Xuebao mechanical arm orbit tracking self-adaptive rbf neural network fuzzy offset integral sliding mode |
| title | Integrated sliding mode control of robot manipulator based on fuzzy adaptive RBF |
| title_full | Integrated sliding mode control of robot manipulator based on fuzzy adaptive RBF |
| title_fullStr | Integrated sliding mode control of robot manipulator based on fuzzy adaptive RBF |
| title_full_unstemmed | Integrated sliding mode control of robot manipulator based on fuzzy adaptive RBF |
| title_short | Integrated sliding mode control of robot manipulator based on fuzzy adaptive RBF |
| title_sort | integrated sliding mode control of robot manipulator based on fuzzy adaptive rbf |
| topic | mechanical arm orbit tracking self-adaptive rbf neural network fuzzy offset integral sliding mode |
| url | https://www.jnwpu.org/articles/jnwpu/full_html/2024/06/jnwpu2024426p1099/jnwpu2024426p1099.html |
| work_keys_str_mv | AT fengjiaqing integratedslidingmodecontrolofrobotmanipulatorbasedonfuzzyadaptiverbf AT zhanglei integratedslidingmodecontrolofrobotmanipulatorbasedonfuzzyadaptiverbf AT tiandongyu integratedslidingmodecontrolofrobotmanipulatorbasedonfuzzyadaptiverbf |