Design of time delayed control systems in UAV using model based predictive algorithm

Ju, Zhang; Zhu, Xiaoping; Zhou, Zhou

doi:10.1109/car.2010.5456849

Cited by 10 publications

(6 citation statements)

References 5 publications

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“…Therefore, it is a feasible and beneficial option to utilize UAVs to ensure the connectivity of wireless communication networks by meeting the surging data demands. For efficient and rapid dispatch of UAVs, the prediction of potential hotspot areas plays a crucial role in helping network operators acquire the information of occurrence and degrees of congestion in advance to reduce the entire network communication delay [6]- [8]. Machine learning (ML) techniques are useful tools that have the ability to efficiently predict the distribution of future traffic data [9]- [15].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Predictive Deployment of UAVs With Multiple Access

Guo

Zang

et al. 2023

IEEE Open J. Commun. Soc.

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This paper presents a machine learning-based framework for the predictive deployment of unmanned aerial vehicles (UAVs) as flying base stations (BSs) to offload heavy traffic from ground BSs. To account for time-varying traffic distribution, a long short-term memory (LSTM)-based prediction algorithm is introduced to predict future cellular traffic. A joint K-means and expectation maximization (EM) algorithm based on Gaussian mixture models (GMM) is proposed to determine the service area of each UAV based on the predicted user service distribution. Based on the predicted traffic, the optimal positions of UAVs are derived, and four multiple access techniques, namely, rate splitting multiple access (RSMA), frequency domain multiple access (FDMA), time domain multiple access (TDMA), and nonorthogonal multiple access (NOMA), are compared to minimize the total transmit power. Simulation results show that the proposed method can reduce up to 24% of the total power consumption compared to the conventional method without traffic prediction. Furthermore, RSMA is found to require the lowest transmit power among the four multiple access techniques. Therefore, this paper focuses on the comparison of multiple access techniques for UAV deployment, which is essential for the efficient and effective use of UAVs as flying BSs.

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Section: Introductionmentioning

confidence: 99%

Machine Learning for Predictive Deployment of UAVs With Multiple Access

Guo

Zang

et al. 2023

IEEE Open J. Commun. Soc.

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“…In order to overcome the limitations of linear control methods, more and more researchers have begun to devote themselves to the research of nonlinear control methods. Among them, the methods applied to aerial robot systems mainly include sliding mode control [19,20] (SMC), backstepping [21], model predictive control (MPC) [22,23], active disturbance rejection control [24,25] (ADRC), and intelligent control [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

A Novel Aerial Manipulator with Front Cutting Effector: Modeling, Control, and Evaluation

Yang

Guoxing

et al. 2021

Complexity

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This paper proposes a novel aerial manipulator with front cutting effector (AMFCE) to address the aerial physical interaction (APhI) problem. First, the system uncertainty and external disturbance during the system movement and contact operation are estimated by modeling the entire robot and contact position. Next, based on the established model, the nonlinear disturbance observer (NDO) is used to estimate and compensate the unknown external disturbance of the system and the uncertainty of the model parameters in real time. Then, the nonsingular terminal synovial membrane control method is used to suppress the part that is difficult to estimate. Finally, a controller which is suitable for the movement and operation of the entire system is designed. The controller’s performance is verified through experiments, and the results show that the design, modeling, and control of the entire system can achieve the APhI.

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“…Therefore, it is a feasible and beneficial option to utilize UAVs to ensure the connectivity of wireless communication network via meeting the surging data demands. For efficient and rapid dispatch of UAVs, the prediction of potential hotspot areas plays a crucial role to help network operators acquire the information of occurrence and degrees of congestion in advance to reduce the entire network communication delay [5]- [7]. Machine learning techniques is a useful tool which has the ability to efficiently predict the distribution of future traffic data [8]- [14].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Predictive Deployment of UAVs with Multiple Access

Lu¹,

Yang²,

Chen³

et al. 2020

Preprint

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In this paper, a machine learning based deployment framework of unmanned aerial vehicles (UAVs) is studied. In the considered model, UAVs are deployed as flying base stations (BS) to offload heavy traffic from ground BSs. Due to time-varying traffic distribution, a long short-term memory (LSTM) based prediction algorithm is introduced to predict the future cellular traffic. To predict the user service distribution, a KEG algorithm, which is a joint K-means and expectation maximization (EM) algorithm based on Gaussian mixture model (GMM), is proposed for determining the service area of each UAV. Based on the predicted traffic, the optimal UAV positions are derived and three multi-access techniques are compared so as to minimize the total transmit power. Simulation results show that the proposed method can reduce up to 24% of the total power consumption compared to the conventional method without traffic prediction. Besides, rate splitting multiple access (RSMA) has the lower required transmit power compared to frequency domain multiple access (FDMA) and time domain multiple access (TDMA).

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Design of time delayed control systems in UAV using model based predictive algorithm

Cited by 10 publications

References 5 publications

Machine Learning for Predictive Deployment of UAVs With Multiple Access

Machine Learning for Predictive Deployment of UAVs With Multiple Access

A Novel Aerial Manipulator with Front Cutting Effector: Modeling, Control, and Evaluation

Machine Learning for Predictive Deployment of UAVs with Multiple Access

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