Dynamic modelling and PFL-based trajectory tracking control for underactuated cable-driven truss-like manipulator

Ding, Shuchen; Qiao, Shangling; Liu, Rongqiang; Josephat, Bundi

doi:10.1007/s11771-021-4833-4

Cited by 3 publications

(2 citation statements)

References 57 publications

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“…However, modeling and analyzing energy systems is crucial for conducting research on OMVs. A well-developed energy model can serve as an evaluation criterion for determining energy-saving control (Ding, Peng, et al, 2021;Ding, Wang, et al, 2021;Sarkar et al, 2016;Yang et al, 2019Yang et al, , 2020, a basis for energy-saving path planning (Kularatne et al, 2018;Liu et al, 2022;Wei et al, 2019), or even as a model for assessing energy efficiency in cluster tasks (Furno et al, 2017;Gu et al, 2021Gu et al, , 2022Thompson et al, 2020;Yan et al, 2015). Therefore, a clearer understanding of the motion response of OMVs in complex and variable ocean environments and the construction of a good energy model are of far-reaching significance to marine robotics research.…”

Section: Related Work Of Robot Energy Modelingmentioning

confidence: 99%

Modeling analysis and experiment for energy system of ocean energy driven unmanned marine vehicle: The Yulang II case study

Jia,

Liao,

et al. 2024

Journal of Field Robotics

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It is critical to master a marine robot's energy system characteristics adequately to ensure that the robot can perform long‐endurance tasks in complex and ever‐changing marine environments. This paper presents, for the first time, an energy system modeling and analysis method for a marine robot driven by wind, solar, and wave composite energy, using Yulang II as an example. First, an onshore experiment method was designed, and then the conversion experiment of wind, sunlight, and electrical energy was carried out based on that experiment method. At the same time, based on computational fluid dynamics technology, a resistance pole curve considering wave energy capture is constructed. In addition, the attitude, relative motion, and battery constraints of the robot in the ocean are analyzed to construct the energy system model of Yulang II. Finally, the method and model proposed in this paper were compared and verified by simulation and field tests. The results indicated that the corrected forecast deviation was 5.71% in the typical marine environment of the experiment, which supported the study of ocean energy‐driven unmanned marine vehicles strongly.

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Section: Related Work Of Robot Energy Modelingmentioning

confidence: 99%

Modeling analysis and experiment for energy system of ocean energy driven unmanned marine vehicle: The Yulang II case study

Jia,

Liao,

et al. 2024

Journal of Field Robotics

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“…On the basis of the partially linearized form equation (23), many control methods could be utilized to control the linearized counterpart. A linear quadratic regulator (LQR)‐based control method can be applied, 57 shown as

(][, \begin{array}{l} v_{1} \\ v_{2} \end{array}) = - K_{l} (][, \begin{array}{l} α_{1} - α_{d 1} \\ {\dot{α}}_{1} - {\dot{α}}_{d 1} \\ α_{2} - α_{d 2} \\ {\dot{α}}_{2} - {\dot{α}}_{d 2} \end{array}) + (][, \begin{array}{l} {\overset{bold¨}{α}}_{d 1} \\ {\overset{bold¨}{α}}_{d 2} \end{array}),

where

K_{l} = (][, \begin{array}{l} 31.6228 & 7.9527 & 0 & 0 \\ 0 & 0 & 31.6228 & 23.7328 \end{array})

…”

Section: Controller Designmentioning

confidence: 99%

Trajectory tracking control of underactuated tendon‐driven truss‐like manipulator based on type‐1 and interval type‐2 fuzzy logic approach

Ding

Wen

et al. 2021

Int J of Intelligent Sys

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This paper deals with the trajectory tracking control problem of a planar underactuated tendon‐driven truss‐like manipulator (UTTM) by using fuzzy logic control methods, including type‐1 fuzzy and interval type‐2 fuzzy approaches. The UTTM is a novel designed underactuated robot that excels in manipulating in harsh environments due to its tendon‐driven parallelogram structure. This unique structure, however, poses challenges for UTTM's trajectory tracking controller design, including underactuation, excessive nonlinearity, parameter uncertainty, and so on. To solve these problems, a fuzzy logic‐based approach is proposed. First, the dynamic model of UTTM is transformed into a partially linearized form. Then a type‐1 fuzzy controller is designed according to a linear quadratic regulator controller for the linearized system. Then by blurring the membership functions of type‐1 fuzzy controllers, an interval type‐2 fuzzy logic controller is designed, aiming at dealing with uncertainties. The proposed type‐1 and interval type‐2 fuzzy logic controllers are validated through simulations, and made comparisons with each other, especially when the system is involved with uncertainties. Simulation results show that the interval type‐2 fuzzy‐based trajectory tracking controller provides better performance than the type‐1 counterpart in terms of tracking accuracy and capacity against uncertainties.

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Swing Reduction Control of Ship Crane Based on Rope Length Change

et al. 2023

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Dynamic modelling and PFL-based trajectory tracking control for underactuated cable-driven truss-like manipulator

Cited by 3 publications

References 57 publications

Modeling analysis and experiment for energy system of ocean energy driven unmanned marine vehicle: The Yulang II case study

Modeling analysis and experiment for energy system of ocean energy driven unmanned marine vehicle: The Yulang II case study

Trajectory tracking control of underactuated tendon‐driven truss‐like manipulator based on type‐1 and interval type‐2 fuzzy logic approach

Swing Reduction Control of Ship Crane Based on Rope Length Change

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