https://www.scipedia.com/wd/index.php?action=history&feed=atom&title=Wang_et_al_2026bWang et al 2026b - Revision history2026-05-25T08:17:13ZRevision history for this page on the wikiMediaWiki 1.27.0-wmf.10https://www.scipedia.com/wd/index.php?title=Wang_et_al_2026b&diff=331023&oldid=prevScipediacontent: Scipediacontent moved page Review 415670617716 to Wang et al 2026b2026-05-04T08:09:38Z<p>Scipediacontent moved page <a href="/public/Review_415670617716" class="mw-redirect" title="Review 415670617716">Review 415670617716</a> to <a href="/public/Wang_et_al_2026b" title="Wang et al 2026b">Wang et al 2026b</a></p>
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</td></tr></table>Scipediacontenthttps://www.scipedia.com/wd/index.php?title=Wang_et_al_2026b&diff=331004&oldid=prevScipediacontent: Created page with " == Abstract == <p>This paper proposes an adaptive event-triggered trajectory and attitude tracking control framework for quadrotor unmanned aerial vehicle (QUAV) with extern..."2026-05-04T08:00:15Z<p>Created page with " == Abstract == <p>This paper proposes an adaptive event-triggered trajectory and attitude tracking control framework for quadrotor unmanned aerial vehicle (QUAV) with extern..."</p>
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== Abstract ==<br />
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<p>This paper proposes an adaptive event-triggered trajectory and attitude tracking control framework for quadrotor unmanned aerial vehicle (QUAV) with external disturbances. To handle unknown uncertainties in QUAV control system, we propose a dual neural network (NN) architecture: combining reinforcement learning with disturbance estimation for real-time disturbance compensation. Specifically, the Actor-NN generates compensation signals to offset uncertainties, while the Critic-NN dynamically evaluates control performance to adjust the learning process. A nonlinear neural network disturbance observer (NNDO) is incorporated to estimate the lumped total disturbances in real time. By combining backstepping control approach with event-triggered mechanism, the proposed control strategy achieves rigorous closed-loop stability with guaranteed exclusion of Zeno behavior. Experimental validation on QUAV demonstrates the effectiveness of the proposed scheme in balancing computational efficiency and control performance.OPEN ACCESS Received: 30/08/2025 Accepted: 12/12/2025</p><br />
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== Document ==<br />
<pdf>Media:Draft_content_142076911-3128-document.pdf</pdf></div>Scipediacontent