{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:30:23Z","timestamp":1760236223867,"version":"build-2065373602"},"reference-count":33,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,11,2]],"date-time":"2021-11-02T00:00:00Z","timestamp":1635811200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.61803009"],"award-info":[{"award-number":["No.61803009"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities of China","doi-asserted-by":"publisher","award":["No.YWF-21-BJ-J-541"],"award-info":[{"award-number":["No.YWF-21-BJ-J-541"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012130","name":"Aeronautical Science Foundation of China","doi-asserted-by":"publisher","award":["No.20175851032"],"award-info":[{"award-number":["No.20175851032"]}],"id":[{"id":"10.13039\/501100012130","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province","doi-asserted-by":"publisher","award":["No BK20180358"],"award-info":[{"award-number":["No BK20180358"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this paper, we investigate the problem of unmanned aerial vehicles (UAVs) autonomous tracking moving target with only an airborne camera sensor. We proposed a novel integrated controller framework for this problem based on multi-neural-network modules (MNNMs). In this framework, two neural networks are designed for target perception and guidance control, respectively. The deep learning method and reinforcement learning method are applied to train the integrated controller. The training result demonstrates that the integrated controller can be trained more quickly and efficiently than the end-to-end controller trained by the deep reinforcement learning method. The flight tests with the integrated controller are implemented in simulated and realistic environments, the results show that the integrated controller trained in simulation can easily be transferred to the realistic environment and achieve the UAV tracking randomly moving target, which has a faster motion velocity. The integrated controller based on the MNNMs structure has a better performance on an autonomous tracking target than the control mode that combines with a perception network and a proportional integral derivative controller.<\/jats:p>","DOI":"10.3390\/s21217307","type":"journal-article","created":{"date-parts":[[2021,11,2]],"date-time":"2021-11-02T22:17:23Z","timestamp":1635891443000},"page":"7307","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["MNNMs Integrated Control for UAV Autonomous Tracking Randomly Moving Target Based on Learning Method"],"prefix":"10.3390","volume":"21","author":[{"given":"Mingjun","family":"Li","sequence":"first","affiliation":[{"name":"School of Automation Science and Electronic Engineering, Beihang University, Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhihao","family":"Cai","sequence":"additional","affiliation":[{"name":"School of Automation Science and Electronic Engineering, Beihang University, Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiang","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Automation Science and Electronic Engineering, Beihang University, Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yibo","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Automation Science and Electronic Engineering, Beihang University, Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingxun","family":"Wang","sequence":"additional","affiliation":[{"name":"Unmanned System Research Institute, Beihang University, Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kelin","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Automation, Southeast University, Nanjing 214135, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1109\/MRA.2012.2206473","article-title":"Toward a Fully Autonomous UAV: Research Platform for Indoor and Outdoor Urban Search and Rescue","volume":"19","author":"Tomic","year":"2012","journal-title":"IEEE Robot. 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