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- W4385059315 abstract "Quadrotors have recently become one of the most popular and widely used types of unmanned aerial vehicles (UAVs) because of their distinctive applications. This category of vertical take-off and landing (VTOL) UAVs presents many properties, such as high maneuverability, compact size, simple propulsion system, and structural design. However, the under-actuated and nonlinear dynamics of these vehicles require the adoption of quite sophisticated control strategies for stability and trajectory tracking. This paper addresses the quadrotors attitude control problem using a Reinforcement Learning (RL) approach. To that end, a new Simulated Annealing-Deep Deterministic Policy Gradient (SA-DDPG) algorithm is being developed to achieve high control performance in a variety of scenarios. Furthermore, the proposed reward function is designed to speed-up the learning process. The obtained results demonstrate the efficiency of the proposed approach in both learning stability and attitude control competency." @default.
- W4385059315 created "2023-07-23" @default.
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- W4385059315 date "2023-02-20" @default.
- W4385059315 modified "2023-09-25" @default.
- W4385059315 title "Simulated Annealing-Deep Deterministic Policy Gradient Algorithm For Quadrotor Attitude Control" @default.
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- W4385059315 doi "https://doi.org/10.1109/aset56582.2023.10180752" @default.
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