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• W4366149087 abstract "This paper proposes an event-triggered deep learning control strategy to achieve real-time trajectory tracking control for quadrotors. In the training data collection phase, the event-triggered model predictive control (MPC) method is applied to the quadrotor in the simulation environment to generate training data. Then, a deep neural network (DNN) controller is trained to approximate the optimal control policy of the event-triggered MPC. To further save computing resources of on-board processor, the event-triggered mechanism is incorporated with the DNN controller, and the dual-mode approach is employed in it. Finally, simulation and experimental results show that the proposed controller can ensure almost similar trajectory tracking performance to the event-triggered MPC controller while requiring a lower control computation cost." @default.
• W4366149087 created "2023-04-19" @default.
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• W4366149087 date "2024-03-01" @default.
• W4366149087 modified "2023-10-02" @default.
• W4366149087 title "Event-Triggered Deep Learning Control of Quadrotors for Trajectory Tracking" @default.
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• W4366149087 doi "https://doi.org/10.1109/tie.2023.3266560" @default.
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