Unmanned Aerial Vehicles: Control Methods and Future Challenges

Zuo, Zongyu; Liu, Cunjia; Han, Qing‐Long; Song, Jiawei

doi:10.1109/jas.2022.105410

Cited by 106 publications

(40 citation statements)

References 126 publications

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“…Yang et al [11] introduced an optimal control strategy of winner-take-all model for target tracking and cooperative competition of multi-UAVs. Furthermore, Zuo et al [12] summarized the flight control methods and future challenges of UAVs.…”

Section: Related Workmentioning

confidence: 99%

Joint Optimization of Deployment and Trajectory in UAV and IRS-Assisted IoT Data Collection System

Dong

Liu

Jiang

et al. 2022

IEEE Internet Things J.

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Unmanned aerial vehicles (UAV) can be applied in many Internet of Things (IoT) systems, e.g., smart farms, as a data collection platform. However, the UAV-IoT wireless channels may be occasionally blocked by trees or high-rise buildings. Intelligent reflecting surface (IRS) can be applied to improve the wireless channel quality by smartly reflecting the signal via a large number of low-cost passive reflective elements. This paper aims to minimize the energy consumption of the system by jointly optimizing the deployment and trajectory of the UAV. The problem can be formulated as a mixed-integerand-nonlinear-programming (MINLP), which is difficult to be addressed by the traditional solution, which may be easily fall into the local optimal. To address this issue, we propose a Joint Optimization framework of depLoyment and Trajectory (JOLT), where an adaptive whale optimization algorithm (AWOA) is applied to optimize the deployment of the UAV, and an elastic ring self-organizing map (ERSOM) is introduced to optimize the trajectory of the UAV. Specifically, in AWOA, a variable-length population strategy is applied to find the optimal number of stop points, and a nonlinear parameter a and a partial mutation rule are introduced to balance the exploration and exploitation. In ERSOM, a competitive neural network is also introduced to learn the trajectory of the UAV by competitive learning, and a ring structure is presented to avoid the trajectory intersection. Extensive experiments are carried out to show the effectiveness of the proposed JOLT framework.

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Section: Related Workmentioning

confidence: 99%

Joint Optimization of Deployment and Trajectory in UAV and IRS-Assisted IoT Data Collection System

Dong

Liu

Jiang

et al. 2022

IEEE Internet Things J.

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show abstract

“…The above definitions indicate that the studied tracking problem is a geometric path-following problem. 27 Combining Equations ( 1)-( 3), we can express the second order derivative of lateral error e y and heading error e 𝜑 as…”

Section: Ta B L Ementioning

confidence: 99%

“…Similarly, the heading error

{e}_{\varphi }

is calculated by the desired heading angle of the reference waypoint minus the current heading angle. The above definitions indicate that the studied tracking problem is a geometric path‐following problem 27 . Combining Equations (1)–(3), we can express the second order derivative of lateral error

{e}_y

and heading error

{e}_{\varphi }

\begin{matrix} alignleft \\ align-1 ë_{y} & align-2 = \frac{C_{f}}{m} δ_{f} - \frac{C_{f} + C_{r}}{m v_{x}} (ė_{y} - v_{x} e_{φ}) - [\frac{l_{f} C_{f} - l_{r} C_{r}}{m v_{x}}] (ė_{φ} + {\dot{φ}}_{des}) + v_{x} ė_{φ}, \\ align-1 ë_{φ} & align-2 = \frac{l_{f} C_{f}}{I_{z}} δ_{f} - \frac{l_{f} C_{f} - l_{r} C_{r}}{I_{z} v_{x}} (ė_{y} - v_{} \end{matrix}

…”

Section: Modeling Of Path Tracking Systemmentioning

confidence: 99%

Adaptive robust path tracking control for autonomous vehicles with measurement noise

Huang

Yang

et al. 2022

Intl J Robust & Nonlinear

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In practical autonomous vehicle systems, model uncertainty and measurement noise are two challenging factors that deteriorate path tracking accuracy and system stability. This article proposes an adaptive robust controller to deal with these two factors and enhance path tracking accuracy. First, the path tracking model considering time‐varying uncertainty is built and represented as nominal and uncertain portions. Second, the control law is designed separately for both portions. A linear quadratic regulator controller is utilized to stabilize the nominal system. An additional robust control law is proposed to suppress the matched uncertainty and measurement noise, with an adaptive scheme aimed at estimating the bound outside uncertainty. The path tracking system with the developed control law is proved to possess uniform boundedness, uniform ultimate boundedness properties, and robustness against mismatched uncertainty. Eventually, the effectiveness of the developed controller is validated using both MATLAB/Simulink‐TruckSim co‐simulation and an autonomous vehicle platform. The results demonstrate that the designed adaptive robust control can achieve accurate path tracking in the presence of time‐varying uncertainty and measurement noise.

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“…Unmanned aerial vehicles (UAVs) have been increasingly used in industrial production and daily life [1][2][3][4][5]. For example, UAV transportation is widely applied to the logistics industry due to rapid increase of online shopping [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Geometric control for trajectory‐tracking of a quadrotor UAV with suspended load

Jun-an

Yuan

Zhu

2022

IET Control Theory & Appl

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A geometric control is proposed for the trajectory-tracking of a quadrotor unmanned aerial vehicle with suspended load. The plant to be controlled is modelled by using Euler-Lagrangian equations, and it is linearised around zero swing angles of the load. The swing angles of the load are supposed to be unmeasurable, and they are estimated by the state observer. The controller is design in the inner-outer loop framework, where the outer loop trajectory-tracking is basically implemented by internal model principle, and the inner loop attitude control is designed on SO(3) to avoid singularities. It is proved that, with the proposed geometric control, the tracking error of the inner loop converges in finite time, and the outer loop trajectory tracking error is asymptotically stable. Theoretical results are substantiated by a numerical example, where the closed-loop system is capable of tracking a circular curve.

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Unmanned Aerial Vehicles: Control Methods and Future Challenges

Abstract: Unmanned aerial vehicles: control methods and future challenges Unmanned aerial vehicles: control methods and future challenges PLEASE CITE THE PUBLISHED VERSION

Cited by 106 publications

References 126 publications

Joint Optimization of Deployment and Trajectory in UAV and IRS-Assisted IoT Data Collection System

Joint Optimization of Deployment and Trajectory in UAV and IRS-Assisted IoT Data Collection System

Adaptive robust path tracking control for autonomous vehicles with measurement noise

Geometric control for trajectory‐tracking of a quadrotor UAV with suspended load

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