An Application of Model Reference Adaptive Control for Multi-Agent Synchronization in Drone Networks
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866913409954480128 |
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| author | Arevalo-Castiblanco, Miguel F. Wi, Yejin and, Marzia Cescon Uribe, Cesar A. |
| author_facet | Arevalo-Castiblanco, Miguel F. Wi, Yejin and, Marzia Cescon Uribe, Cesar A. |
| contents | This paper presents the application of a Distributed Model Reference Adaptive Control (DMRAC) strategy for robust multi-agent synchronization of a network of drones. The proposed approach enables the development of controllers capable of accommodating differences in real-life model parameters between agents, thereby enhancing overall network performance. We compare the performance of the adaptive control laws with classical PID controllers for the reference tracking task. Each follower drone has a model reference adaptive controller that continuously updates its parameters based on real-time feedback and reference model information. This adaptability ensures an adequate performance that, compared to conventional non-adaptive techniques, can reduce the amount of energy required and consequently increase the operating duration of the drones. The experimental results, particularly in vertical velocity control, underscore the effectiveness of the proposed approach in achieving synchronized behavior. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_00570 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An Application of Model Reference Adaptive Control for Multi-Agent Synchronization in Drone Networks Arevalo-Castiblanco, Miguel F. Wi, Yejin and, Marzia Cescon Uribe, Cesar A. Systems and Control This paper presents the application of a Distributed Model Reference Adaptive Control (DMRAC) strategy for robust multi-agent synchronization of a network of drones. The proposed approach enables the development of controllers capable of accommodating differences in real-life model parameters between agents, thereby enhancing overall network performance. We compare the performance of the adaptive control laws with classical PID controllers for the reference tracking task. Each follower drone has a model reference adaptive controller that continuously updates its parameters based on real-time feedback and reference model information. This adaptability ensures an adequate performance that, compared to conventional non-adaptive techniques, can reduce the amount of energy required and consequently increase the operating duration of the drones. The experimental results, particularly in vertical velocity control, underscore the effectiveness of the proposed approach in achieving synchronized behavior. |
| title | An Application of Model Reference Adaptive Control for Multi-Agent Synchronization in Drone Networks |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2407.00570 |