Unmanned Aerial Vehicle in the Machine Learning Environment

Khan, Asharul Islam; Al-Mulla, Yaseen A.

doi:10.1016/j.procs.2019.09.442

Cited by 32 publications

(13 citation statements)

References 16 publications

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“… Reference No. Antenna Used Algorithm Used Compared to Result [ 72 ] planar k-neural networks support vector machines Synthesize the research on unmanned aerial vehicles (UAVs) based on a machine learning environment. [ 73 ] conventional reinforcement learning Why, how and which types of algorithms are used in U-RANS [ 74 ] reflectarrays k-nearest algorithms Localization as a classification problem by using machine learning [ 75 ] mimo antenna artificial intelligence Get a detailed overview of the AI's potential applications in UAV-based networks.…”

Section: Discussionmentioning

confidence: 99%

A review on machine learning and deep learning for various antenna design applications

Khan

Hossain

Majumder

et al. 2022

Heliyon

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Section: Discussionmentioning

confidence: 99%

A review on machine learning and deep learning for various antenna design applications

Khan

Hossain

Majumder

et al. 2022

Heliyon

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“…ML techniques have been deeply investigated and used in a wide applications in the autonomous agents and unmanned systems because of its adaptive learning ability [25], [26]. RL is one of the branches of ML, which can optimize the action policy based on the interactions between agent and environment.…”

Section: Introductionmentioning

confidence: 99%

UAV Path Planning Based on Multi-Layer Reinforcement Learning Technique

Cui

Wang

2021

IEEE Access

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Unmanned aerial vehicles (UAVs) have been widely used in many applications due to its small size, swift mobility and low cost. Therefore, the study of guidance, navigation and control (GNC) system of UAV has becoming a popular research direction. Path planning plays an important role in the GNC system. In this paper, a multi-layer path planning algorithm based on reinforcement learning (RL) technique is proposed. Compared to the classic Q-learning, the proposed multi-layer algorithm has a distinct advantage that it collects both global and local information which greatly improves overall performance. The proposed RL algorithm has two layers, the higher layer deals with the local information, which could be considered as a short-term strategy. The lower layer deals with the global information, which could be considered as a long-term strategy. Both the higher layer and lower layer are working in harmony to plan a collision-free path. B-spline curve approach is applied for on-line path smoothing. Simulation results in different scenarios prove the effectiveness of multi-layer Q-learning algorithm.

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“…A number of recent articles on the use of computational intelligence in the control of flight systems are now being reviewed. In (Khan and Al-Mulla, 2019) about the importance of machine learning in UAV systems has been discussed. In Patel et al (2020) the authors use a combination of neural network and PD controller to control the UAV, in which the neural network monitors the UAV's status online and eliminates possible errors.…”

Section: Introductionmentioning

confidence: 99%

Hybrid intelligent adaptive controller for tiltrotor UAV

Tavoosi

2020

IJIUS

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PurposeIn this paper, an innovative hybrid intelligent position control method for vertical take-off and landing (VTOL) tiltrotor unmanned aerial vehicle (UAV) is proposed. So the more accurate the reference position signals tracking, the proposed control system will be better.Design/methodology/approachIn the proposed method, for the vertical flight mode, first the model reference adaptive controller (MRAC) operates and for the horizontal flight, the model predictive control (MPC) will operate. Since the linear model is used for both of these controllers and naturally has an error compared to the real nonlinear model, a neural network is used to compensate for them. So the main novelties of this paper are a new hybrid control design (MRAC & MPC) and a neural network-based compensator for tiltrotor UAV.FindingsThe proper performance of the proposed control method in the simulation results is clear. Also the results showed that the role of compensator is very important and necessary, especially in extreme speed wind conditions and uncertain parameters.Originality/valueNovel hybrid control method. 10;-New method to use neural network as compensator in an UAV.

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Unmanned Aerial Vehicle in the Machine Learning Environment

Cited by 32 publications

References 16 publications

A review on machine learning and deep learning for various antenna design applications

A review on machine learning and deep learning for various antenna design applications

UAV Path Planning Based on Multi-Layer Reinforcement Learning Technique

Hybrid intelligent adaptive controller for tiltrotor UAV

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