Sparse Online Gaussian Process Adaptive Control of Unmanned Aerial Vehicle with Slung Payload DOI Creative Commons
Muhammed Rasit Kartal, Dmitry Ignatyev, Argyrios Zolotas

et al.

Drones, Journal Year: 2024, Volume and Issue: 8(11), P. 687 - 687

Published: Nov. 19, 2024

In the past decade, Unmanned Aerial Vehicles (UAVs) have garnered significant attention across diverse applications, including surveillance, cargo shipping, and agricultural spraying. Despite their widespread deployment, concerns about maintaining stability safety, particularly when carrying payloads, persist. The development of such UAV platforms necessitates implementation robust control mechanisms to ensure stable precise maneuvering capabilities. Numerous operations require integration which introduces substantial challenges. Notably, involving unstable payloads as liquid or slung pose a considerable challenge in this regard, falling into category mismatched uncertain systems. This study focuses on establishing for payload-carrying Our approach involves combination various algorithms: incremental backstepping algorithm (IBKS), integrator (IBS), Proportional–Integral–Derivative (PID), Sparse Online Gaussian Process (SOGP), machine learning technique that identifies mitigates disturbances. With comparison linear nonlinear methodologies through different scenarios, an investigation effective solution has been performed. Implementation component, employing SOGP, effectively detects counteracts Insights are discussed within remit rejecting sloshing disturbance.

Language: Английский

Recent Advancements in Morphing Applications: Architecture, Artificial Intelligence Integration, Challenges, and Future Trends- A Comprehensive Survey DOI Creative Commons
Md. Najmul Mowla, Davood Asadi,

Tahir Durhasan

et al.

Aerospace Science and Technology, Journal Year: 2025, Volume and Issue: 161, P. 110102 - 110102

Published: Feb. 26, 2025

Language: Английский

Citations

0
A Decision Risk Assessment and Alleviation Framework under Data Quality Challenges in Manufacturing DOI Creative Commons
Tangxiao Yuan, Kondo H. Adjallah, Alexandre Sava

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(20), P. 6586 - 6586

Published: Oct. 12, 2024

The ability and rapid access to execution data information in manufacturing workshops have been greatly improved with the wide spread of Internet Things artificial intelligence technologies, enabling real-time unmanned integrated control facilities production. However, widespread issue quality field raises concerns among users about robustness automatic decision-making models before their application. This paper addresses three main challenges relative issues during automated decision-making: parameter identification under measurement uncertainty, sensor accuracy selection, fault-tolerant control. To address these problems, this proposes a risk assessment framework case continuous production workshops. aims determine method for systematically assessing specific scenarios. It specifies preparation requirements, as well assumptions such datasets on typical working conditions, model. Within framework, are transformed into deviation problems. By employing Monte Carlo simulation measure impact decision risk, direct link between risks is established. defines steps challenges. A study steel industry confirms effectiveness framework. proposed offers new approach safety reducing industrial settings.

Language: Английский

Citations

1
Sparse Online Gaussian Process Adaptive Control of Unmanned Aerial Vehicle with Slung Payload DOI Creative Commons
Muhammed Rasit Kartal, Dmitry Ignatyev, Argyrios Zolotas

et al.

Drones, Journal Year: 2024, Volume and Issue: 8(11), P. 687 - 687

Published: Nov. 19, 2024

In the past decade, Unmanned Aerial Vehicles (UAVs) have garnered significant attention across diverse applications, including surveillance, cargo shipping, and agricultural spraying. Despite their widespread deployment, concerns about maintaining stability safety, particularly when carrying payloads, persist. The development of such UAV platforms necessitates implementation robust control mechanisms to ensure stable precise maneuvering capabilities. Numerous operations require integration which introduces substantial challenges. Notably, involving unstable payloads as liquid or slung pose a considerable challenge in this regard, falling into category mismatched uncertain systems. This study focuses on establishing for payload-carrying Our approach involves combination various algorithms: incremental backstepping algorithm (IBKS), integrator (IBS), Proportional–Integral–Derivative (PID), Sparse Online Gaussian Process (SOGP), machine learning technique that identifies mitigates disturbances. With comparison linear nonlinear methodologies through different scenarios, an investigation effective solution has been performed. Implementation component, employing SOGP, effectively detects counteracts Insights are discussed within remit rejecting sloshing disturbance.

Language: Английский

Citations

0