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Reinforced Learning-Based Robust Control Design for Unmanned Aerial Vehicle

  • Research Article-Computer Engineering and Computer Science
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Abstract

Innovation in UAV design technologies over the last decade and a half has resulted in capabilities that flourished the development of unique and complex multi-mission capable UAVs. These emerging new distinctive designs of UAVs necessitate development of intelligent and robust Control Laws which are independent of inherent plant variations besides being adaptive to environmental changes for achieving desired design objectives. Current research focuses on development of a control framework which aims to maximize the glide range for an experimental UAV employing reinforcement learning (RL)-based intelligent control architecture. A distinct model-free RL technique, abbreviated as ‘MRL’, is suggested which is capable of handling UAV control complications while keeping the computation cost low. At core, the basic RL DP algorithm has been sensibly modified to cater for the continuous state and control space domains associated with the current problem. Review of the performance characteristics through analysis of the results indicates the prowess of the presented algorithm to dynamically adapt to the changing environment, thereby making it suitable for complex designed UAV applications. Nonlinear simulations carried out under varying environmental conditions illustrated the effectiveness of the proposed methodology and its success over the conventional classical approaches.

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Data Availability Statement

Data are available from the authors upon reasonable request.

Abbreviations

b ::

Wing span (m)

\(\tilde{c}\) ::

Mean aerodynamic chord (m)

CAD ::

Computer-aided design

CFD ::

Computational fluid dynamics

\(C_{M_x}\)::

Coefficient of rolling moment

\(C_{M_y}\) ::

Coefficient of pitching moment

\(C_{M_z}\) ::

Coefficient of yawing moment

\(C_{F_x}\) ::

Force coefficient in the X-direction

\(C_{F_y}\) ::

Force coefficient in the Y-direction

\(C_{F_z}\) ::

Force coefficient in the Z-direction

DoF ::

Degree of freedom

DDD ::

Dull dirty and dangerous

g ::

Acceleration due to gravity \((m/sec^2)\)

h ::

Altitude (m)

LF ::

Left-side control fin

MRL ::

Model-free reinforcement learning

ML ::

Machine learning

m ::

Mass of the vehicle (kg)

\(P_E\) ::

East position vector (km)

\(P_N\) ::

North position vector (km)

P ::

Roll rate (deg/sec)

Q ::

Pitch rate (deg/sec)

R ::

Yaw rate (deg/sec)

RL ::

Reinforcement learning

RF ::

Right-side control fin

S ::

Wing area \((m^2)\)

UAV ::

Unmanned aerial vehicle

\(V_T\) ::

Far stream velocity (m/sec)

n ::

Numerical weights

xpos ::

Current X-position(m)

zpos ::

Current Z-position(m)

r ::

Momentary reward

R ::

Total reward

pny ::

Penalty

\(\alpha \) ::

Angle of attack (deg)

\(\beta \) ::

Sideslip angle (deg)

\(\gamma \) ::

Flight path angle (deg)

\(\psi \) ::

Yaw angle (deg)

\(\phi \) ::

Roll angle (deg)

\(\theta \) ::

Theta angle (deg)

\(\delta _L\) ::

LF deflection (deg)

\(\delta _R\) ::

RF deflection (deg)

\(\rho \) ::

Air density \((kg/m^3)\)

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Authors and Affiliations

  1. Institute of Avionics and Aeronautics, Air University, Islamabad, Pakistan

    Adnan Fayyaz Ud Din

  2. Department of Avionics Engineering, Air University, Aerospace and Aviation, Campus Kamra, Islamabad, Pakistan

    Imran Mir & Faiza Gul

  3. School of Engineering and Technology, Department of Information Technology, ALDAR University College, Garhoud, Dubai, UAE

    Mohammad Rustom Al Nasar

  4. Faculty of Computer Sciences and Informatics, Amman Arab University, Amman, 11953, Jordan

    Laith Abualigah

  5. School of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang, 11800, Malaysia

    Laith Abualigah

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  1. Adnan Fayyaz Ud Din
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Din, A.F.U., Mir, I., Gul, F. et al. Reinforced Learning-Based Robust Control Design for Unmanned Aerial Vehicle. Arab J Sci Eng 48, 1221–1236 (2023). https://doi.org/10.1007/s13369-022-06746-0

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