Adaptive Trajectory Tracking Algorithm of a Quadrotor with Sliding Mode Control and Multilayer Neural Network
To improve the trajectory tracking accuracy, the anti-jamming performance, and the environment adaptability of a quadrotor, the paper proposes a new adaptive trajectory tracking algorithm with multilayer neural network and sliding mode control method. The major difference between other related appro...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2022/1457532 |
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| _version_ | 1832562377276522496 |
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| author | Kang Niu Di Yang Xi Chen Rong Wang Jianqiao Yu |
| author_facet | Kang Niu Di Yang Xi Chen Rong Wang Jianqiao Yu |
| author_sort | Kang Niu |
| collection | DOAJ |
| description | To improve the trajectory tracking accuracy, the anti-jamming performance, and the environment adaptability of a quadrotor, the paper proposes a new adaptive trajectory tracking algorithm with multilayer neural network and sliding mode control method. The major difference between other related approaches is that the paper uses the multilayer neural network in the system and the neural network is online computing in the whole process. Firstly, the paper establishes the quadrotor dynamic model and introduces the conception of Sigma-Pi neural network. Then, the paper adds the neural network to the attitude and trajectory tracking control loop. Moreover, the paper designs the adaptive neural network control law. At last, to illustrate the stability of the adaptive control law, the paper gives the Lyapunov stability analysis. Finally, to demonstrate the effectiveness of the method, the paper gives different types of simulation. Comparing with different cases, when increasing the layer of the neural network, the trajectory tracking performance becomes better. In addition, introducing multilayer neural network into the system could improve the anti-interference ability of the system and has a high-precision in tracking the desire trajectory. |
| format | Article |
| id | doaj-art-04f6ae0781424f1f8b82b0ae5dc980a2 |
| institution | Kabale University |
| issn | 1099-0526 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-04f6ae0781424f1f8b82b0ae5dc980a22025-02-03T01:22:40ZengWileyComplexity1099-05262022-01-01202210.1155/2022/1457532Adaptive Trajectory Tracking Algorithm of a Quadrotor with Sliding Mode Control and Multilayer Neural NetworkKang Niu0Di Yang1Xi Chen2Rong Wang3Jianqiao Yu4Department of AstronauticsDepartment of AstronauticsDepartment of AstronauticsShanghai Electromechanical Engineering Research InstituteDepartment of AstronauticsTo improve the trajectory tracking accuracy, the anti-jamming performance, and the environment adaptability of a quadrotor, the paper proposes a new adaptive trajectory tracking algorithm with multilayer neural network and sliding mode control method. The major difference between other related approaches is that the paper uses the multilayer neural network in the system and the neural network is online computing in the whole process. Firstly, the paper establishes the quadrotor dynamic model and introduces the conception of Sigma-Pi neural network. Then, the paper adds the neural network to the attitude and trajectory tracking control loop. Moreover, the paper designs the adaptive neural network control law. At last, to illustrate the stability of the adaptive control law, the paper gives the Lyapunov stability analysis. Finally, to demonstrate the effectiveness of the method, the paper gives different types of simulation. Comparing with different cases, when increasing the layer of the neural network, the trajectory tracking performance becomes better. In addition, introducing multilayer neural network into the system could improve the anti-interference ability of the system and has a high-precision in tracking the desire trajectory.http://dx.doi.org/10.1155/2022/1457532 |
| spellingShingle | Kang Niu Di Yang Xi Chen Rong Wang Jianqiao Yu Adaptive Trajectory Tracking Algorithm of a Quadrotor with Sliding Mode Control and Multilayer Neural Network Complexity |
| title | Adaptive Trajectory Tracking Algorithm of a Quadrotor with Sliding Mode Control and Multilayer Neural Network |
| title_full | Adaptive Trajectory Tracking Algorithm of a Quadrotor with Sliding Mode Control and Multilayer Neural Network |
| title_fullStr | Adaptive Trajectory Tracking Algorithm of a Quadrotor with Sliding Mode Control and Multilayer Neural Network |
| title_full_unstemmed | Adaptive Trajectory Tracking Algorithm of a Quadrotor with Sliding Mode Control and Multilayer Neural Network |
| title_short | Adaptive Trajectory Tracking Algorithm of a Quadrotor with Sliding Mode Control and Multilayer Neural Network |
| title_sort | adaptive trajectory tracking algorithm of a quadrotor with sliding mode control and multilayer neural network |
| url | http://dx.doi.org/10.1155/2022/1457532 |
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