Nonlinear System Identification of Swarm of UAVs Using Deep Learning Methods
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866909319935557632 |
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| author | Yazdannik, Saman Tayefi, Morteza Farrokh, Mojtaba |
| author_facet | Yazdannik, Saman Tayefi, Morteza Farrokh, Mojtaba |
| contents | This study designs and evaluates multiple nonlinear system identification techniques for modeling the UAV swarm system in planar space. learning methods such as RNNs, CNNs, and Neural ODE are explored and compared. The objective is to forecast future swarm trajectories by accurately approximating the nonlinear dynamics of the swarm model. The modeling process is performed using both transient and steady-state data from swarm simulations. Results show that the combination of Neural ODE with a well-trained model using transient data is robust for varying initial conditions and outperforms other learning methods in accurately predicting swarm stability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_12906 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Nonlinear System Identification of Swarm of UAVs Using Deep Learning Methods Yazdannik, Saman Tayefi, Morteza Farrokh, Mojtaba Machine Learning Systems and Control This study designs and evaluates multiple nonlinear system identification techniques for modeling the UAV swarm system in planar space. learning methods such as RNNs, CNNs, and Neural ODE are explored and compared. The objective is to forecast future swarm trajectories by accurately approximating the nonlinear dynamics of the swarm model. The modeling process is performed using both transient and steady-state data from swarm simulations. Results show that the combination of Neural ODE with a well-trained model using transient data is robust for varying initial conditions and outperforms other learning methods in accurately predicting swarm stability. |
| title | Nonlinear System Identification of Swarm of UAVs Using Deep Learning Methods |
| topic | Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2311.12906 |