Event-Triggered-Based Neuroadaptive Bipartite Containment Tracking for Networked Unmanned Aerial Vehicles
This paper addresses the event-triggered neuroadaptive bipartite containment tracking problem for networked unmanned aerial vehicles (UAVs) subject to resource constraints and actuator failures. A fully distributed event-triggered mechanism is innovatively developed to eliminate dependency on global...
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MDPI AG
2025-04-01
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| author | Bowen Chen Boxian Lin Meng Li Zhiqiang Li Xinyu Zhang Mengji Shi Kaiyu Qin |
| author_facet | Bowen Chen Boxian Lin Meng Li Zhiqiang Li Xinyu Zhang Mengji Shi Kaiyu Qin |
| author_sort | Bowen Chen |
| collection | DOAJ |
| description | This paper addresses the event-triggered neuroadaptive bipartite containment tracking problem for networked unmanned aerial vehicles (UAVs) subject to resource constraints and actuator failures. A fully distributed event-triggered mechanism is innovatively developed to eliminate dependency on global information while rigorously excluding the Zeno phenomenon through nonperiodic threshold verification. The proposed mechanism enables neighboring UAVs to exchange information and update control signals exclusively at triggering instants, significantly reducing communication burdens and energy consumption. To handle unknown nonlinear dynamics under resource-limited scenarios, a novel event-triggered neural network (NN) approximation scheme is established where weight updating occurs only during event triggers, effectively decreasing computational resource occupation. Simultaneously, an adaptive robust compensation mechanism is constructed to counteract composite disturbances induced by actuator failures and approximation residuals. Based on the Lyapunov stability analysis, we theoretically prove that all closed-loop signals remain uniformly ultimately bounded while achieving prescribed bipartite containment objectives, where follower UAVs ultimately converge to the dynamic convex hull formed by multiple leaders with cooperative-competitive interactions. Finally, numerical simulations are conducted to validate the effectiveness of the theoretical results. Comparative simulation results show that the proposed event-triggered control scheme reduces the utilization of resources by <inline-formula><math display="inline"><semantics><mrow><mn>95</mn><mo>%</mo></mrow></semantics></math></inline-formula> and <inline-formula><math display="inline"><semantics><mrow><mn>67</mn><mo>%</mo></mrow></semantics></math></inline-formula> compared with the traditional time-triggered and static-triggered mechanisms, respectively. |
| format | Article |
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| institution | OA Journals |
| issn | 2504-446X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-9e9988b17cf747d29fd208b7d3c5ecf32025-08-20T02:28:33ZengMDPI AGDrones2504-446X2025-04-019431710.3390/drones9040317Event-Triggered-Based Neuroadaptive Bipartite Containment Tracking for Networked Unmanned Aerial VehiclesBowen Chen0Boxian Lin1Meng Li2Zhiqiang Li3Xinyu Zhang4Mengji Shi5Kaiyu Qin6School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, ChinaThis paper addresses the event-triggered neuroadaptive bipartite containment tracking problem for networked unmanned aerial vehicles (UAVs) subject to resource constraints and actuator failures. A fully distributed event-triggered mechanism is innovatively developed to eliminate dependency on global information while rigorously excluding the Zeno phenomenon through nonperiodic threshold verification. The proposed mechanism enables neighboring UAVs to exchange information and update control signals exclusively at triggering instants, significantly reducing communication burdens and energy consumption. To handle unknown nonlinear dynamics under resource-limited scenarios, a novel event-triggered neural network (NN) approximation scheme is established where weight updating occurs only during event triggers, effectively decreasing computational resource occupation. Simultaneously, an adaptive robust compensation mechanism is constructed to counteract composite disturbances induced by actuator failures and approximation residuals. Based on the Lyapunov stability analysis, we theoretically prove that all closed-loop signals remain uniformly ultimately bounded while achieving prescribed bipartite containment objectives, where follower UAVs ultimately converge to the dynamic convex hull formed by multiple leaders with cooperative-competitive interactions. Finally, numerical simulations are conducted to validate the effectiveness of the theoretical results. Comparative simulation results show that the proposed event-triggered control scheme reduces the utilization of resources by <inline-formula><math display="inline"><semantics><mrow><mn>95</mn><mo>%</mo></mrow></semantics></math></inline-formula> and <inline-formula><math display="inline"><semantics><mrow><mn>67</mn><mo>%</mo></mrow></semantics></math></inline-formula> compared with the traditional time-triggered and static-triggered mechanisms, respectively.https://www.mdpi.com/2504-446X/9/4/317bipartite containment trackingnetworked unmanned aerial vehiclesneural networkadaptive controlevent-triggered control |
| spellingShingle | Bowen Chen Boxian Lin Meng Li Zhiqiang Li Xinyu Zhang Mengji Shi Kaiyu Qin Event-Triggered-Based Neuroadaptive Bipartite Containment Tracking for Networked Unmanned Aerial Vehicles Drones bipartite containment tracking networked unmanned aerial vehicles neural network adaptive control event-triggered control |
| title | Event-Triggered-Based Neuroadaptive Bipartite Containment Tracking for Networked Unmanned Aerial Vehicles |
| title_full | Event-Triggered-Based Neuroadaptive Bipartite Containment Tracking for Networked Unmanned Aerial Vehicles |
| title_fullStr | Event-Triggered-Based Neuroadaptive Bipartite Containment Tracking for Networked Unmanned Aerial Vehicles |
| title_full_unstemmed | Event-Triggered-Based Neuroadaptive Bipartite Containment Tracking for Networked Unmanned Aerial Vehicles |
| title_short | Event-Triggered-Based Neuroadaptive Bipartite Containment Tracking for Networked Unmanned Aerial Vehicles |
| title_sort | event triggered based neuroadaptive bipartite containment tracking for networked unmanned aerial vehicles |
| topic | bipartite containment tracking networked unmanned aerial vehicles neural network adaptive control event-triggered control |
| url | https://www.mdpi.com/2504-446X/9/4/317 |
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