<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The keypoint of autonomous driving technology is the accurate instructions maked by desicision-makers based on the perception information. Human plays an important role in the decision-makers. The cognitive load is usually used to quantify the impact of human-computer interaction during flighting. In this paper, we proposed a innovate automatic landing control method based on the cognitive load theory and Deep Deterministic Policy Gradient. Different to the traditional algorithm which heavily relays on an accurate model, the reinforcement learning algorithm is used to design the control strategy in the proposed method. And an improved DDPG algorithm is proposed based on the impact of cognitive load, to improve the training efficiency of the DDPG algorithm and reduce the correlation between data. And construct a human-machine reinforcement learning model. The final position, mean square error of pitch angle, and standard deviation of the aircraft gradually decrease with the number of iterations and tend to 0, indicating that the aircraft is gradually stabilizing its landing. The experimental results demonstrate that the proposed model can greatly improve the longitudinal stability of the aircraft.</p>
</abstract>

The keypoint of autonomous driving technology is the accurate instructions maked by desicision-makers based on the perception information. Human plays an important role in the decision-makers. The cognitive load is usually used to quantify the impact of human-computer interaction during flighting. In this paper, we proposed a innovate automatic landing control method based on the cognitive load theory and Deep Deterministic Policy Gradient. Different to the traditional algorithm which heavily relays on an accurate model, the reinforcement learning algorithm is used to design the control strategy in the proposed method. And an improved DDPG algorithm is proposed based on the impact of cognitive load, to improve the training efficiency of the DDPG algorithm and reduce the correlation between data. And construct a human-machine reinforcement learning model. The final position, mean square error of pitch angle, and standard deviation of the aircraft gradually decrease with the number of iterations and tend to 0, indicating that the aircraft is gradually stabilizing its landing. The experimental results demonstrate that the proposed model can greatly improve the longitudinal stability of the aircraft.

Automatic Landing Control of Aircraft Based on Cognitive Load Theory and DDPG

International Journal of Advanced Network, Monitoring and Controls

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Automatic Landing Control of Aircraft Based on Cognitive Load Theory and DDPG"}

The keypoint of autonomous driving technology is the accurate instructions maked by desicision-makers based on the perception information. Human plays an...

Automatic Landing Control of Aircraft Based on Cognitive Load Theory and DDPG

Online veröffentlicht: 28. März 2024

Seitenbereich: 68 - 77

DOI: https://doi.org/10.2478/ijanmc-2024-0007

Schlüsselwörter
Component, Cognitive Load, Human Factors, Longitudinal Stability Control, Reinforcement Learning, Deep Deterministic Policy Gradient

© 2024 Chao Wang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Automatic Landing Control of Aircraft Based on Cognitive Load Theory and DDPG

Online veröffentlicht: 28. März 2024

Seitenbereich: 68 - 77

DOI: https://doi.org/10.2478/ijanmc-2024-0007

SchlüsselwörterComponent, Cognitive Load, Human Factors, Longitudinal Stability Control, Reinforcement Learning, Deep Deterministic Policy Gradient

© 2024 Chao Wang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
Component, Cognitive Load, Human Factors, Longitudinal Stability Control, Reinforcement Learning, Deep Deterministic Policy Gradient