Optimized neural network based sliding mode control for quadrotors with disturbances
In this paper, optimized radial basis function neural networks (RBFNNs) are employed to construct a sliding mode control (SMC) strategy for quadrotors with unknown disturbances. At first, the dynamics model of the controlled quadrotor is built, where some unknown external disturbances are considered...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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AIMS Press
2021-04-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | http://www.aimspress.com/article/doi/10.3934/mbe.2021092?viewType=HTML |
| _version_ | 1818329407041830912 |
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| author | Ping Li Zhe Lin Hong Shen Zhaoqi Zhang Xiaohua Mei |
| author_facet | Ping Li Zhe Lin Hong Shen Zhaoqi Zhang Xiaohua Mei |
| author_sort | Ping Li |
| collection | DOAJ |
| description | In this paper, optimized radial basis function neural networks (RBFNNs) are employed to construct a sliding mode control (SMC) strategy for quadrotors with unknown disturbances. At first, the dynamics model of the controlled quadrotor is built, where some unknown external disturbances are considered explicitly. Then SMC is carried out for the position and the attitude control of the quadrotor. However, there are unknown disturbances in the obtained controllers, so RBFNNs are employed to approximate the unknown parts of the controllers. Furtherly, Particle Swarm optimization algorithm (PSO) based on minimizing the absolute approximation errors is used to improve the performance of the controllers. Besides, the convergence of the state tracking errors of the quadrotor is proved. In order to exposit the superiority of the proposed control strategy, some comparisons are made between the RBFNN based SMC with and without PSO. The results show that the strategy with PSO achieves quicker and smoother trajectory tracking, which verifies the effectiveness of the proposed control strategy. |
| first_indexed | 2024-12-13T12:47:34Z |
| format | Article |
| id | doaj.art-17ccac3ca407476b83415ae98dad51b2 |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-12-13T12:47:34Z |
| publishDate | 2021-04-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-17ccac3ca407476b83415ae98dad51b22022-12-21T23:45:26ZengAIMS PressMathematical Biosciences and Engineering1551-00182021-04-011821774179310.3934/mbe.2021092Optimized neural network based sliding mode control for quadrotors with disturbancesPing Li0Zhe Lin 1Hong Shen2Zhaoqi Zhang3Xiaohua Mei4College of Information Science and Engineering, Huaqiao University, Xiamen 361021, ChinaCollege of Information Science and Engineering, Huaqiao University, Xiamen 361021, ChinaCollege of Information Science and Engineering, Huaqiao University, Xiamen 361021, ChinaCollege of Information Science and Engineering, Huaqiao University, Xiamen 361021, ChinaCollege of Information Science and Engineering, Huaqiao University, Xiamen 361021, ChinaIn this paper, optimized radial basis function neural networks (RBFNNs) are employed to construct a sliding mode control (SMC) strategy for quadrotors with unknown disturbances. At first, the dynamics model of the controlled quadrotor is built, where some unknown external disturbances are considered explicitly. Then SMC is carried out for the position and the attitude control of the quadrotor. However, there are unknown disturbances in the obtained controllers, so RBFNNs are employed to approximate the unknown parts of the controllers. Furtherly, Particle Swarm optimization algorithm (PSO) based on minimizing the absolute approximation errors is used to improve the performance of the controllers. Besides, the convergence of the state tracking errors of the quadrotor is proved. In order to exposit the superiority of the proposed control strategy, some comparisons are made between the RBFNN based SMC with and without PSO. The results show that the strategy with PSO achieves quicker and smoother trajectory tracking, which verifies the effectiveness of the proposed control strategy.http://www.aimspress.com/article/doi/10.3934/mbe.2021092?viewType=HTMLquadrotorsliding mode control (smc)radial basis function neural network (rbfnn)particle swarm optimization (pso)disturbance |
| spellingShingle | Ping Li Zhe Lin Hong Shen Zhaoqi Zhang Xiaohua Mei Optimized neural network based sliding mode control for quadrotors with disturbances Mathematical Biosciences and Engineering quadrotor sliding mode control (smc) radial basis function neural network (rbfnn) particle swarm optimization (pso) disturbance |
| title | Optimized neural network based sliding mode control for quadrotors with disturbances |
| title_full | Optimized neural network based sliding mode control for quadrotors with disturbances |
| title_fullStr | Optimized neural network based sliding mode control for quadrotors with disturbances |
| title_full_unstemmed | Optimized neural network based sliding mode control for quadrotors with disturbances |
| title_short | Optimized neural network based sliding mode control for quadrotors with disturbances |
| title_sort | optimized neural network based sliding mode control for quadrotors with disturbances |
| topic | quadrotor sliding mode control (smc) radial basis function neural network (rbfnn) particle swarm optimization (pso) disturbance |
| url | http://www.aimspress.com/article/doi/10.3934/mbe.2021092?viewType=HTML |
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