Robust flight control system design of a fixed wing UAV using optimal dynamic programming

被引:0
|
作者
Adnan Fayyaz Ud Din
Imran Mir
Faiza Gul
Suleman Mir
Syed Sahal Nazli Alhady
Mohammad Rustom Al Nasar
Hamzah Ali Alkhazaleh
Laith Abualigah
机构
[1] Air University,Institute of Avionics and Aeronautics
[2] College of Aeronautical Engineering,School of Avionics and Electrical Engineering
[3] National University of Sciences and Technology,School of Avionics and Electrical Engineering, College of Aeronautical Engineering
[4] National University of Sciences and Technology,Department of Electrical Engineering
[5] Air University,Department of Electrical Engineering
[6] Aerospace and Aviation,School of Electrical and Electronic Engineering
[7] Fast-National University of Computer and Emerging Sciences,College of Computer Information Technology (CCIT), Department of Information Technology Management
[8] Universiti Sains Malaysia,College of Engineering and IT, IT Department
[9] American University in the Emirates (AUE),Hourani Center for Applied Scientific Research
[10] University of Dubai Academic City,Faculty of Information Technology
[11] Al-Ahliyya Amman University,undefined
[12] Middle East University,undefined
来源
Soft Computing | 2023年 / 27卷
关键词
Flight dynamics; Control law; Machine learning; Reinforcement learning; Bellman’s principle of optimality; Reinforcement learning-based dynamic programming; Modified model free dynamic programming; Optimal reinforcement learning; Nonlinear simulations;
D O I
暂无
中图分类号
学科分类号
摘要
Innovative design intricacies of new generation of UAVs, necessitate formulation of control laws utilizing intelligent techniques which are independent of underlying dynamic model besides being robust to changing environment. In current research, a novel control architecture is presented for maximizing glide range of the UAV which bears an unconventional design. To handle the control complexities emerging due to the unique design of the UAV, a distinct RL technique named ’optimal dynamic programming’ is proposed which besides being computationally acceptable also effectively controls the entire flight regime of the UAV. The proposed methodology has been specifically modified to configure the problem in continuous state and control space domains. The efficacy of the results and performance characteristics, demonstrated the ability of the presented algorithm to dynamically adapt to the changing environment, thereby making it suitable for UAV applications. Nonlinear simulations performed under different environmental conditions demonstrated the effectiveness of the proposed methodology over the conventional classical approaches.
引用
收藏
页码:3053 / 3064
页数:11
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