Nonlinear Motion Control for a Quadrotor Transporting a Cable-Suspended Payload

Lv, Zongyang; Wu, Yuhu; Wang, Rui

doi:10.1109/tvt.2020.2997733

Cited by 37 publications

(7 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are three reference frames to describe the QCSP (Lv et al, 2020) system (see left of Figure 1): the inertial frame I {X i , Y i , Z i }, the quadrotor body frame B{X b , Y b , Z b }, and the payload body frame B p {X p , Y p , Z p }. What needs to be mentioned is that the inertial frame follows the North-East-Down (NED) notation.…”

Section: Dynamic Model and Object Detection Algorithm Of The Qcspmentioning

confidence: 99%

“…Following previous work (Lv et al, 2020), the dynamic model of the QCSP system is described by the following equations:…”

Section: Dynamic Model and Object Detection Algorithm Of The Qcspmentioning

confidence: 99%

“…What needs to be mentioned is that A [A ϕ A θ 0] ⊤ is given in Eq. 12a of (Lv et al, 2020), and the inertial matrix J q is given in Eq. 4 of (Lv et al, 2020).…”

Section: Dynamic Model and Object Detection Algorithm Of The Qcspmentioning

confidence: 99%

“…Because of the underactuated character of the QCSP, the proposed controller mainly consists of two parts: the cascade controller for attitude self-stabilization and the active disturbance rejection controller for position control (see the right subfigure of Figure 1). Referring to (Lv et al, 2020), the design process of the cascade controller mainly consists of three parts: inner-loop attitude, middle-loop swing angle, and outer-loop velocity subcontrollers.…”

Section: Controller Designmentioning

confidence: 99%

“…With the development of unmanned aerial vehicle (UAV) technology (Wu et al, 2018), drone transport has become an important branch of UAV applications. The quadrotor with the cablesuspended payload (QCSP) (Lv et al, 2020;Lv et al, 2021) equipped with a camera and an embedded platform, is of great relevance to the realization of rescue and transport tasks. The QCSP actively adjusts the UAV's own attitude to quickly reduce the oscillation of the suspended load and then runs the vision algorithm through the embedded platform to process the images from the camera to obtain an accurate target position for the drone.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Energy-Efficiency-Oriented Vision Feedback Control of QCSP Systems: Linear ADRC Approach

Feng

2022

Front. Energy Res.

View full text Add to dashboard Cite

How to save the energy of unmanned aerial vehicles (UAVs) and then enable long-distance transport is a very real and difficult task. However, for UAVs, the classic object detection algorithm, such as the deep convolutional neural network–based object detection algorithm and the classic flight control algorithm, such as the PID-based position control algorithm, require significant energy, which limits the application scenarios of the UAV system. In view of this problem, this paper proposes a lightweight object detection network and a linear active disturbance rejection controller (LADRC) for the quadrotor with the cable-suspended payload (QCSP) system to improve energy efficiency. The system uses a YOLOV3 network and embeds it into the Jesson NX mobile platform to accurately detect the target position. Furthermore, a nonlinear velocity controller with a cable-suspended structure to control the velocity of the payload, a LADRC algorithm is adopted to achieve fast and accurate control of the payload position. Simulations and real flight experiments show that the proposed object detection algorithm and the LADRC control strategy can save the energy of drone effectively.

show abstract

Section: Dynamic Model and Object Detection Algorithm Of The Qcspmentioning

confidence: 99%

“…Following previous work (Lv et al, 2020), the dynamic model of the QCSP system is described by the following equations:…”

Section: Dynamic Model and Object Detection Algorithm Of The Qcspmentioning

confidence: 99%

“…What needs to be mentioned is that A [A ϕ A θ 0] ⊤ is given in Eq. 12a of (Lv et al, 2020), and the inertial matrix J q is given in Eq. 4 of (Lv et al, 2020).…”

Section: Dynamic Model and Object Detection Algorithm Of The Qcspmentioning

confidence: 99%

Section: Controller Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Energy-Efficiency-Oriented Vision Feedback Control of QCSP Systems: Linear ADRC Approach

Feng

2022

Front. Energy Res.

View full text Add to dashboard Cite

show abstract

Adaptive sliding mode control for quadrotor transport systems with uncertain parameters and disturbances

Huang,

Yang,

Chen

2024

Adaptive Control & Signal

View full text Add to dashboard Cite

SummaryQuadrotor transportation systems have been widely utilized in both commercial and civilian fields. However, challenges arise due to the variable payload mass and unpredictable wind disturbances during cargo transport, potentially leading to excessive payload swinging and system instability. To tackle this issue, this article proposes an adaptive sliding mode control approach that concurrently achieves trajectory tracking for the quadrotor and mitigates payload swinging. In the outer loop subsystem, the quadrotor dynamics model is partitioned into two components that is, actuated and underactuated components. Sliding surfaces are designed based on this divided system model, and two adaptive laws are designed to compensate for payload mass uncertainty and unknown wind disturbances. The system's asymptotic stability is assured through the application of the Lyapunov theorem. In the inner loop subsystem, a disturbance rejection controller is formulated. Comparative simulation results conclusively demonstrate the outstanding performance of the proposed method in terms of trajectory tracking and robustness.

show abstract

An enhanced coupling nonlinear control for quadrotor with suspended double‐pendulum payload

Yan,

Lin,

Cai

et al. 2024

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

In the field of logistics transportation, unmanned aerial vehicles are commonly utilized for cargo transport in a suspended manner, and the suspended payloads will exhibit the double‐pendulum dynamic characteristics (dPDC). However, due to the underactuation and load‐swing problems caused by the dPDC, it is challenging to design controllers for a quadrotor with a suspended double‐pendulum type payload. Existing related studies simplify the dPDC to a single‐pendulum dynamic characteristic, which results in degraded transient performance. To overcome the issue, this article presents a novel controller approach that considers the real dynamic characteristics, that is, dPDC. The proposed approach tackles the quadrotor position control and anti‐swing problem in two steps. First, a composite signal incorporating both the quadrotor position and the payload swing angles is introduced to restrain the payload swing. Second, a nonlinear controller based on the energy analysis method is proposed to overcome the coupling between rotation and translation, which addresses the underactuation problem. Theoretical analysis confirms the asymptotic stability of the system under this control strategy. Numerical and experimental results are provided to demonstrate the effectiveness of the proposed controller.

show abstract

Nonlinear Motion Control for a Quadrotor Transporting a Cable-Suspended Payload

Cited by 37 publications

References 23 publications

Energy-Efficiency-Oriented Vision Feedback Control of QCSP Systems: Linear ADRC Approach

Energy-Efficiency-Oriented Vision Feedback Control of QCSP Systems: Linear ADRC Approach

Adaptive sliding mode control for quadrotor transport systems with uncertain parameters and disturbances

An enhanced coupling nonlinear control for quadrotor with suspended double‐pendulum payload

Contact Info

Product

Resources

About