Active fault-tolerant control scheme of aerial manipulators with actuator faults

Ding, Yadong; Wang, Yaoyao; Jiang, Surong; Bai, Chen

doi:10.1007/s11771-021-4644-7

Cited by 11 publications

(3 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…Ding Yadong et al [47] proposed an active fault-tolerant control method for a flying manipulator based on a non-singular terminal sliding mode and an extended state observer. Considering actuator failure and modeling error, combined with the adaptive law of actuator failure, its performance is better than traditional fault-tolerant control methods.…”

Section: Modeling and Control Of Aerial Manipulatormentioning

confidence: 99%

Review of Aerial Manipulator and its Control

Wei-hong

Cao

Chun-lai

2021

IJRCS

View full text Add to dashboard Cite

The aerial manipulator is a new type of aerial robot with active operation capability, which is composed of a rotary-wing drone and an actuator. Although aerial manipulation has greatly increased the scope of robot operations, the research on aerial manipulators also faces many difficulties, such as the selection of aerial platforms and actuators, system modeling and control, etc. This article attempts to collect the research team’s Achievements in the field of aerial robotic arms. The main results of the aerial manipulator system and corresponding dynamic modeling and control are reviewed, and its problems are summarized and prospected.

show abstract

“…They can more accurately represent and infer the state of the system and its uncertainties. They can also design control methods to address these uncertainties, such as adaptive control [6,7], model predictive control [8,9], nonlinear control [10], filtering and estimation [11,12], and robust control [13,14], among others. With the increasing demand for uncertainty modeling and probabilistic inference, BNs have been widely used in various fields, including risk assessment [15], fault diagnosis [16], decision systems [17], gene sequence analysis [18], biomedical image processing [19], and other areas.…”

Section: Introductionmentioning

confidence: 99%

Bayesian network structure learning with a new ensemble weights and edge constraints setting mechanism

Liu,

Zhou,

Huang

2024

Complex Intell. Syst.

View full text Add to dashboard Cite

Bayesian networks (BNs) are highly effective in handling uncertain problems, which can assist in decision-making by reasoning with limited and incomplete information. Learning a faithful directed acyclic graph (DAG) from a large number of complex samples of a joint distribution is currently a challenging combinatorial problem. Due to the growing volume and complexity of data, some Bayesian structure learning algorithms are ineffective and lack the necessary precision to meet the required needs. In this paper, we propose a new PCCL-CC algorithm. To ensure the accuracy of the network structure, we introduce the new ensemble weights and edge constraints setting mechanism. In this mechanism, we employ a method that estimates the interaction between network nodes from multiple perspectives and divides the learning process into multiple stages. We utilize an asymmetric weighted ensemble method and adaptively adjust the network structure. Additionally, we propose a causal discovery method that effectively utilizes the causal relationships among data samples to correct the network structure and mitigate the influence of Markov equivalence classes (MEC). Experimental results on real datasets demonstrate that our approach outperforms state-of-the-art methods.

show abstract

Active fault-tolerant control scheme of aerial manipulators with actuator faults

Cited by 11 publications

References 29 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

Review of Aerial Manipulator and its Control

Bayesian network structure learning with a new ensemble weights and edge constraints setting mechanism

Contact Info

Product

Resources

About