Deep Reinforcement Learning for Integrated Non-Linear Control of Autonomous UAVs

In this research, an intelligent control architecture for an experimental Unmanned Aerial Vehicle (UAV) bearing unconventional inverted V-tail design, is presented. To handle UAV’s inherent control complexities, while keeping them computationally acceptable, a variant of distinct Deep Reinforcement...

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Bibliographic Details
Published in:Processes 2022-07, Vol.10 (7), p.1307
Main Authors: ud Din, Adnan Fayyaz, Mir, Imran, Gul, Faiza, Mir, Suleman, Saeed, Nasir, Althobaiti, Turke, Abbas, Syed Manzar, Abualigah, Laith
Format: Article
Language:English
Subjects:
Algorithms
Changing environments
Control theory
Deep learning
Dynamic programming
Feedback loops
Learning
Machine learning
Nonlinear control
Optimal control
Optimization techniques
Reinforcement
Surveillance
Unmanned aerial vehicles
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Summary:In this research, an intelligent control architecture for an experimental Unmanned Aerial Vehicle (UAV) bearing unconventional inverted V-tail design, is presented. To handle UAV’s inherent control complexities, while keeping them computationally acceptable, a variant of distinct Deep Reinforcement Learning (DRL) algorithm, namely Deep Deterministic Policy Gradient (DDPG) is proposed. Conventional DDPG algorithm after being modified in its learning architecture becomes capable of intelligently handling the continuous state and control space domains besides controlling the platform in its entire flight regime. Nonlinear simulations were then performed to analyze UAV performance under different environmental and launch conditions. The effectiveness of the proposed strategy is further demonstrated by comparing the results with the linear controller for the same UAV whose feedback loop gains are optimized by employing technique of optimal control theory. Results indicate the significance of the proposed control architecture and its inherent capability to adapt dynamically to the changing environment, thereby making it of significant utility to airborne UAV applications.
ISSN:2227-9717
2227-9717
DOI:10.3390/pr10071307